Simplicity is the ultimate sophistication. Leonardo da Vinci.

Publication

Below are a list of recent and selected papers.

A mark * denotes the author to be a VITA student or Dr. Wang's mentee.

An up-to-date full paper list can be found here.

VITA Research Activities by Main Topics:
Past and Present (2018 - 2027)
Explore publications by topic and year
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VITA publications by topic, 2018–2027Each band shows annual publication counts on a common scale. A paper with multiple topics contributes to each relevant band. Hover or focus a year for its exact count; select it to see those publications.VITA Publications by Topic: Evolutionary View (All Years)2018201920202021202220232024202520262027Model Training & Optimization AlgorithmsModel Training & Optimization Algorithms, 2018: 0 publicationsModel Training & Optimization Algorithms, 2019: 1 publicationModel Training & Optimization Algorithms, 2020: 6 publicationsModel Training & Optimization Algorithms, 2021: 8 publicationsModel Training & Optimization Algorithms, 2022: 10 publicationsModel Training & Optimization Algorithms, 2023: 11 publicationsModel Training & Optimization Algorithms, 2024: 5 publicationsModel Training & Optimization Algorithms, 2025: 15 publicationsModel Training & Optimization Algorithms, 2026: 5 publicationsModel Training & Optimization Algorithms, 2027: 0 publicationsEfficient Inference & Architecture DesignEfficient Inference & Architecture Design, 2018: 1 publicationEfficient Inference & Architecture Design, 2019: 2 publicationsEfficient Inference & Architecture Design, 2020: 10 publicationsEfficient Inference & Architecture Design, 2021: 12 publicationsEfficient Inference & Architecture Design, 2022: 12 publicationsEfficient Inference & Architecture Design, 2023: 13 publicationsEfficient Inference & Architecture Design, 2024: 13 publicationsEfficient Inference & Architecture Design, 2025: 8 publicationsEfficient Inference & Architecture Design, 2026: 3 publicationsEfficient Inference & Architecture Design, 2027: 1 publicationReasoning and AgentsReasoning and Agents, 2018: 0 publicationsReasoning and Agents, 2019: 0 publicationsReasoning and Agents, 2020: 0 publicationsReasoning and Agents, 2021: 0 publicationsReasoning and Agents, 2022: 3 publicationsReasoning and Agents, 2023: 2 publicationsReasoning and Agents, 2024: 3 publicationsReasoning and Agents, 2025: 6 publicationsReasoning and Agents, 2026: 10 publicationsReasoning and Agents, 2027: 0 publicationsGeometric Deep Learning & GraphsGeometric Deep Learning & Graphs, 2018: 0 publicationsGeometric Deep Learning & Graphs, 2019: 0 publicationsGeometric Deep Learning & Graphs, 2020: 3 publicationsGeometric Deep Learning & Graphs, 2021: 4 publicationsGeometric Deep Learning & Graphs, 2022: 8 publicationsGeometric Deep Learning & Graphs, 2023: 7 publicationsGeometric Deep Learning & Graphs, 2024: 3 publicationsGeometric Deep Learning & Graphs, 2025: 2 publicationsGeometric Deep Learning & Graphs, 2026: 1 publicationGeometric Deep Learning & Graphs, 2027: 0 publicationsTrustworthy AITrustworthy AI, 2018: 1 publicationTrustworthy AI, 2019: 1 publicationTrustworthy AI, 2020: 6 publicationsTrustworthy AI, 2021: 7 publicationsTrustworthy AI, 2022: 12 publicationsTrustworthy AI, 2023: 6 publicationsTrustworthy AI, 2024: 6 publicationsTrustworthy AI, 2025: 6 publicationsTrustworthy AI, 2026: 3 publicationsTrustworthy AI, 2027: 0 publicationsWorld Model & Physical AIWorld Model & Physical AI, 2018: 0 publicationsWorld Model & Physical AI, 2019: 0 publicationsWorld Model & Physical AI, 2020: 1 publicationWorld Model & Physical AI, 2021: 0 publicationsWorld Model & Physical AI, 2022: 4 publicationsWorld Model & Physical AI, 2023: 8 publicationsWorld Model & Physical AI, 2024: 13 publicationsWorld Model & Physical AI, 2025: 9 publicationsWorld Model & Physical AI, 2026: 8 publicationsWorld Model & Physical AI, 2027: 0 publicationsAI in Medicine & HealthcareAI in Medicine & Healthcare, 2018: 1 publicationAI in Medicine & Healthcare, 2019: 2 publicationsAI in Medicine & Healthcare, 2020: 1 publicationAI in Medicine & Healthcare, 2021: 0 publicationsAI in Medicine & Healthcare, 2022: 1 publicationAI in Medicine & Healthcare, 2023: 5 publicationsAI in Medicine & Healthcare, 2024: 4 publicationsAI in Medicine & Healthcare, 2025: 3 publicationsAI in Medicine & Healthcare, 2026: 0 publicationsAI in Medicine & Healthcare, 2027: 0 publicationsDL Theory: Sparse NNs & TransformersDL Theory: Sparse NNs & Transformers, 2018: 0 publicationsDL Theory: Sparse NNs & Transformers, 2019: 0 publicationsDL Theory: Sparse NNs & Transformers, 2020: 1 publicationDL Theory: Sparse NNs & Transformers, 2021: 0 publicationsDL Theory: Sparse NNs & Transformers, 2022: 3 publicationsDL Theory: Sparse NNs & Transformers, 2023: 2 publicationsDL Theory: Sparse NNs & Transformers, 2024: 4 publicationsDL Theory: Sparse NNs & Transformers, 2025: 3 publicationsDL Theory: Sparse NNs & Transformers, 2026: 1 publicationDL Theory: Sparse NNs & Transformers, 2027: 0 publicationsLearning to Optimize & Inverse ProblemsLearning to Optimize & Inverse Problems, 2018: 2 publicationsLearning to Optimize & Inverse Problems, 2019: 3 publicationsLearning to Optimize & Inverse Problems, 2020: 2 publicationsLearning to Optimize & Inverse Problems, 2021: 3 publicationsLearning to Optimize & Inverse Problems, 2022: 7 publicationsLearning to Optimize & Inverse Problems, 2023: 6 publicationsLearning to Optimize & Inverse Problems, 2024: 0 publicationsLearning to Optimize & Inverse Problems, 2025: 0 publicationsLearning to Optimize & Inverse Problems, 2026: 0 publicationsLearning to Optimize & Inverse Problems, 2027: 0 publicationsImage Generation & RestorationImage Generation & Restoration, 2018: 0 publicationsImage Generation & Restoration, 2019: 7 publicationsImage Generation & Restoration, 2020: 6 publicationsImage Generation & Restoration, 2021: 8 publicationsImage Generation & Restoration, 2022: 4 publicationsImage Generation & Restoration, 2023: 7 publicationsImage Generation & Restoration, 2024: 4 publicationsImage Generation & Restoration, 2025: 1 publicationImage Generation & Restoration, 2026: 2 publicationsImage Generation & Restoration, 2027: 0 publications

Band thickness shows annual paper counts on a shared scale. Select a year within a topic to see its papers.

View annual counts
Annual publications by topic; multi-topic papers appear in each relevant row.
Topic2018201920202021202220232024202520262027
Model Training & Optimization Algorithms0168101151550
Efficient Inference & Architecture Design121012121313831
Reasoning and Agents00003236100
Geometric Deep Learning & Graphs0034873210
Trustworthy AI11671266630
World Model & Physical AI00104813980
AI in Medicine & Healthcare1210154300
DL Theory: Sparse NNs & Transformers0010324310
Learning to Optimize & Inverse Problems2323760000
Image Generation & Restoration0768474120
347 publications

R. Siva*, N. Bhatt*, Y. Yang, S. Lee*, N. Gadde, C. Ellis, A. Velasquez, Z. Wang, and U. Topcu

What Objects Enable, Not What They Are: Functional Latent Spaces for Affordance Reasoning

Conference on Robot Learning (CoRL)

F. Liu, N. Chimitt, L. Guo*, J. Jain, A. Kane, M. Kim, W. Robbins*, Y. Su, D. Ye, X. Zhang, J. Zhu, S. Satyakam, C. Perry, S. Chan, A. Ross, H. Shi, Z. Wang, A.Jain, and X. Liu

Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2026

Y. Ro*, V. Zhang, V. Arun, D. Kim, J. Lin, C. Rossbach, Z. Wang, and A. Akella

LFS: Learning-Based Flexible CPU Scheduler

USENIX Symposium on Networked Systems Design and Implementation (NSDI)

2027

S. Xing, J. Hong*, Y. Wang, R. Chen*, Z. Zhang*, A. Grama, Z. Tu, and Z. Wang

LLMs Can Get "Brain Rot"

Conference on Language Modeling (COLM)

M. Ho, Z. Zhu, R. Zhu, L. Li, Z. Fan*, Z. Wang, and J. Hong*

Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

Conference on Language Modeling (COLM)

J. Li*, B. Liu, C. Xu, Y. Wang, Z. Yao, Z. Wang, Q. Liu, and Y. He

The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows

Conference on Language Modeling (COLM)

2026

K. Kim, K. Wang*, Y. Xie, P. Xu, P. Sheng, C. Wei, Z. Wang, J. Shin, P. Viswanath, and S. Oh

Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models

Conference on Language Modeling (COLM)

N. Chen, L. Liu, Z. Li, Z. Zeng, Z. Zhu, W. Cong*, J. Hong*, Y. Yang, Z. Tu, Y. Wang, B. Ivanovic, M. Pavone, Z. Wang, Y. Zhou, and Z. Fan*

CrashTwin: A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

European Conference on Computer Vision (ECCV) [Oral]

S. Lee*, S. Shithil, D. Pushp, L. Liu, and Z. Wang

Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2026

P. Wang*, S. Yang, X. Wang, T. Xiao, X. Liu, C. Yu, Y. Lou, P. Li, Z. Wang, M. Lin, and R. Vidal

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning

International Conference on Machine Learning (ICML)

Y. Xie, K. Wang*, B. Cheng, J. Yao, Z. Sha*, A. Duffy, Y. Xi, H. Mei, C. Tan, C. Wei, P. Viswanath, and Z. Wang

MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games

International Conference on Machine Learning (ICML)

J. Zhu*, Y. Chen, P. Wang*, Y. He, P. Li, A. Akella, and Z. Wang

When Do Graph Foundation Models Transfer? A Data-Centric Theory

International Conference on Machine Learning (ICML)

T. Huang, Z. Wang, H. Hu, Z. Zhang*, G. Jin, X. Li, L. Shen, J. Shang, T. Chen*, K. Li, L. Liu, Q. Wen, and S. Liu*

GradientStabilizer: Fix the Norm, Not the Gradient

International Conference on Machine Learning (ICML)

Y. Wang*, P. Wang*, H. Jiang, Z. Yang*, Q. Huang, and Z. Wang

Revisiting Spectral Representations in Generative Diffusion Models

International Conference on Machine Learning (ICML)

2026

Z. Zhang*, S. Zhang, J. Lambert, W. Zhou, Z. Wang, M. Chen, A. Hard, R. Mathews, and L Wang

Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

International Conference on Machine Learning (ICML)

2026

Z. Wang, P. Wang*, and K. Wang

Weight Space Should Be a First-Class Generative AI Modality

International Conference on Machine Learning (ICML) Position Track

2026

L. Guo*, Y. Wang, H. Hu*, Y. Zheng*, Y. Jin, S. Huang, and Z. Wang

Mastering Regional 3DGS: Locating, Initializing, and Editing with Diverse 2D Priors

International Journal of Computer Vision (IJCV)

2026

Z. Li*, J. Hong*, J. Zhu*, S. Eum, S. Hu, S. You, and Z. Wang

POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking

Transactions on Machine Learning Research (TMLR)

Z. Ding, J. Hong*, Z. Shi, J. Wang, Z. Lin, L. Yin, M. Liu, Z. Wang, and Y. Chen

Scaling Textual Gradients via Sampling-Based Momentum

ACM Conference on AI and Agentic Systems (CAIS)

2026

H. Hu*, W. Zhao, L. Guo*, H. Jiang, J. Liu*, Z. Fan*, K. Wang, Z. Wang, and G. Pavlakos

HumanNOVA: Photorealistic, Universal and Rapid 3D Human Avatar Modeling from a Single Image

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight]

Z. Fan*, J. Zhang, R. Li, J. Zhang, R. Chen*, H. Hu*, K. Wang*, P. Wang*, H. Qu, S. Zhou, D. Wang, Z. Yan, H. Xu, J. Theiss, T. Chen, J. Li, Z. Tu, Z. Wang, and R Ranjan

VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Short Version: Best Paper Award, ACM MM 2025 MFMSI Workshop]

Y. Jiang, H. Jiang, A. Abdelkader, W. Chu, B. Feng, Z. Wang, and Q. Huang

Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2026

L. Balzano, T. Ding, B. Haeffele, S. Kwon, Q. Qu, P. Wang, Z. Wang, and C. Yaras

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models

IEEE Signal Processing Magazine (SPM)

2026

S. Alemohammad*, Z. Wang, and R. Baraniuk

Neon: Negative Extrapolation From Self-Training Improves Image Generation

International Conference on Learning Representations (ICLR) [Oral]

P. Wang*, R. Cai*, Z. Wang, H. Mei, Q. Liu, P. Li, and Z. Wang

Nabla-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space

International Conference on Learning Representations (ICLR)

C. Zheng, J. Sun, Y. Gao, E. Xie, Y. Wang*, P. Wang*, T. Xu, M. Chang, L. Ren, J. Li, J. Xiong, K. Rasul, M. Schwager, A. Schneider, Z. Wang, and Y. Nevmyvaka

Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel

International Conference on Learning Representations (ICLR)

2026

W. Zhao, Y. Han, Z. Tang, J. Tang, P. Zhou, K. Wang, B. Zhuang, Z. Wang, F. Wang, and Y. You

RAPID3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer

International Conference on Learning Representations (ICLR)

Y. Yang, J. Hong*, G. Perin*, Z. Fan*, L. Yin, Z. Wang, and U Topcu

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback

IEEE International Conference on Robotics and Automation (ICRA)

N. Bhatt*, P. Li, K. Gupta, R. Siva, D. Milan, A. Hogue, S. Chinchali, D. Fridovich-Keil, Z. Wang, and U. Topcu

UNCAP: Uncertainty-Guided Neurosymbolic Planning Using Natural Language Communication for Cooperative Autonomous Vehicles

International Conference on Autonomous Agents and Multiagent Systems (AAMAS) [Best Paper Finalist]

Y. Zheng*, Z. Liang, X. Cong, Y. Yang, L. Guo*, Y. Wang*, P. Wang*, and Z. Wang

Oscillation Inversion: Training-free Image and Video Enhancement through Oscillated Latents in Large Flow Models

AAAI Conference on Artificial Intelligence (AAAI) [Oral]

2026

W. Cong*, H. Zhu, K. Wang*, J. Lei, C. Stearns, Y. Cai, D. Wang, R. Ranjan, M. Feiszli, L. Guibas, Z. Wang, W. Wang, and Z. Fan*

VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment

International Conference on 3D Vision (3DV) [Short Version: Best Paper Award, CVPR 2025 AI4CC Workshop]

Z. Liang, D. Tang, Y. Zhou, X. Zhao, M. Shi, W. Zhao, Z. Li, P. Wang*, K. Schürholt, D. Borth, M. Bronstein, Y. You, Z. Wang, and K. Wang

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

Advances in Neural Information Processing Systems (NeurIPS)

K. Wang, D. Tang, W. Zhao, K. Schürholt, Z. Wang, and Y. You

Scaling Up Parameter Generation: A Recurrent Diffusion Approach

Advances in Neural Information Processing Systems (NeurIPS)

Z. Wang, W. Zhao, Y. Zhou, Z. Li, Z. Liang, M. Shi, X. Zhao, P. Zhou, K. Zhang, Z. Wang, K. Wang, and Y. You

REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training

Advances in Neural Information Processing Systems (NeurIPS)

H. Wang, P. Wang*, M. Li, S. Liu, S. Miao, Z. Wang, and P. Li

Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models

Advances in Neural Information Processing Systems (NeurIPS)

M. Zhang, Y. Zhang, J. Jia, Z. Wang, S. Liu, and T. Chen*

One Token Embedding Is Enough to Deadlock Your Reasoning Large Language Model

Advances in Neural Information Processing Systems (NeurIPS)

C. Zheng, J. Sun, Y. Gao, Y. Wang*, P. Wang*, J. Xiong, L. Ren, H. Cheng, J. Kulkarni, Y. Shen, Z. Wang, M. Schwager, A. Schneider, X. Liu, and J. Gao

SAS: Simulated Attention Score

Advances in Neural Information Processing Systems (NeurIPS)

2025

L. Li, Z. Fan*, W. Cong*, X. Liu, Y. Yin, M. Foutter, P. Pan, C. You, Y. Wang, Z. Wang, Y. Zhao, M. Pavone, and Y. Wei

Martian World Models: Controllable Video Synthesis with Physically Accurate 3D Reconstructions

Advances in Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS D & B)

S. Pandit, J. Xu, J. Hong*, Z. Wang, T. Chen*, K. Xu, Y. Ding

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Conference on Empirical Methods in Natural Language Processing (EMNLP)

J. Yao, K. Wang*, R. Hsieh*, H. Zhou*, T. Zou, Z. Cheng, Z. Wang, P. Viswanath

SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?

Conference on Language Modeling (COLM)

R. Chen*, Z. Zhang*, J. Hong*, S. Kundu, Z. Wang

SEAL: Steerable Reasoning Calibration of Large Language Models for Free

Conference on Language Modeling (COLM)

W. Cong*, H. Zhu, P. Wang*, B. Liu, D. Xu*, K. Wang*, D. Pan, Y. Wang, Z. Fan*, and Z. Wang

Can Scaling Test-Time Compute Improve World Foundation Model?

Conference on Language Modeling (COLM)

G. Perin*, R. Chen*, X. Chen*, N. T. Hirata, Z. Wang, and J. Hong*

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning

Conference on Language Modeling (COLM)

Y. Wang, R. Chen*, B. Li, D. Cho, Y. Deng, R. Zhang, T. Chen*, Z. Wang, A. Grama, J. Hong*

More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment

Conference on Language Modeling (COLM)

G. Holste*, E. Oikonomou, M. Tokodi, A. Kovacs, Z. Wang, and R. Khera

Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning

Journal of the American Medical Association (JAMA)

A. Jaiswal*, Y. Wang, L. Yin*, S. Liu*, R. Chen*, J. Zhao, A. Grama, Y. Tian, and Z. Wang

From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories and Applications

International Conference on Machine Learning (ICML)

G. Kim, J. Li*, S. Gandham, O. Baldonado, A. Gangidi, P. Balaji, Z. Wang, and A. Akella

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

International Conference on Machine Learning (ICML)

J. Zhu*, P. Wang*, R. Cai*, J. Lee, P. Li, and Z. Wang

Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding

International Conference on Machine Learning (ICML)

L. Hong* and Z. Wang

On the Provable Separation of Scales in Maximal Update Parameterization

International Conference on Machine Learning (ICML)

2025

J. Li*, Z. Wang, Q. Liu

PIPA: Preference Alignment as Prior-Informed Statistical Estimation

International Conference on Machine Learning (ICML)

2025

Y. Ro*, Z. Zhang*, S. Kundu, Z. Wang, and A. Akella

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving

International Conference on Machine Learning (ICML)

D. Xu*, Y. Jiang*, C. Huang, L. Song, T. Gernoth, L. Cao, Z. Wang, H. Tang

Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention

International Conference on Machine Learning (ICML)

P. Wang* and Z. Wang

Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning

International Conference on Neuro-symbolic Systems (NeuS) [DAPRA Disruptive Idea Paper Award]

2025

Y. Kong, Y. Yang, Y. Hwang, W. Du, S. Zohren, Z. Wang, M. Jin, and Q. Wen

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Annual Meeting of the Association for Computational Linguistics (ACL)

H. Liu, Y. Wang*, C. Li, R. Cai*, K. Wang*, W. Li, P. Molchanov, P. Wang*, and Z Wang

FlexGS: Train Once Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

P. Wang*, Y. Wang*, D. Wang, S. Mohan, Z. Fan*, L. Wu, R. Cai*, Y. Yeh, Z. Wang, Q. Liu, R. Ranjan

Steepest Descent Density Control for Compact 3D Gaussian Splatting

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

S. Zhou, H. Ren, Y. Weng, S. Zhang, Z. Wang, D. Xu*, Z. Fan*, S. You, Z. Wang, L Guibas, and A. Kadambi

Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

R. Henschel, L. Khachatryan, D. Hayrapetyan, H. Poghosyan, V. Tadevosyan, Z. Wang, S. Navasardyan, and H. Shi

StreamingT2V: Consistent Dynamic and Extendable Long Video Generation from Text

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

W. Zhao, Y. Han, J. Tang, Z. Li, Y. Song, K. Wang, Z. Wang, and Y. You

A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for accelerating Large VLMs

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

H. Zhu, Z. Zhang*, W. Cong*, X. Liu, S. Park, V. Chandra, B. Long, D. Pan, Z. Wang, and J. Lee

APOLLO: SGD-like Memory AdamW-level Performance

Conference on Machine Learning and Systems (MLSys) [Outstanding Paper Honorable Mention]

N. Bhatt*, Y. Yang, R. Siva, D. Milan, U. Topcu, and Z. Wang

Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework

Conference on Machine Learning and Systems (MLSys)

W. Redman, Z. Wang, A. Ingrosso, and S. Goldt

On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks

Conference on Parsimony and Learning (CPAL)

2025

Z. Zhang*, A. Jaiswal*, L. Yin*, S. Liu*, J. Zhao, Y. Tian, and Z. Wang

Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients

Conference on Parsimony and Learning (CPAL)

P. Wang*, R. Cai*, Y. Wang*, J. Zhu*, P. Srivastava, Z. Wang, and P. Li

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

International Conference on Learning Representations (ICLR)

H. Yang*, Z. Wang, J. Lee, and Y. Liang

Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow

International Conference on Learning Representations (ICLR)

2025

R. Cai*, S. Muralidharan, H. Yin, Z. Wang, J. Kautz, and P. Molchanov

LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing

International Conference on Learning Representations (ICLR)

2025

Z. Zhang*, Z. Liu, Y. Tian, H. Khaitan, Z. Wang, and S. Li

R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference

International Conference on Learning Representations (ICLR)

T. Huang, Z. Zhu, G. Jin, L. Liu, Z. Wang, and S. Liu*

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

International Conference on Learning Representations (ICLR)

R. Li, P. Pan, B. Yang, D. Xu*, S. Zhou, X. Zhang, Z. Li, A. Kadambi, Z. Wang, Z. Tu, Z. Fan*

4K4DGen: Panoramic 4D Generation at 4K Resolution

International Conference on Learning Representations (ICLR) [Spotlight]

H. Manukyan, A. Sargsyan, B. Atanyan, Z. Wang, S. Navasardyan, and H. Shi

HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models

International Conference on Learning Representations (ICLR)

P. Wang*, Z. Fan*, D. Xu*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity

International Conference on Artificial Intelligence and Statistics (AISTATS)

Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang

Sparse Transfer Learning Accelerates and Enhances Certified Robustness

AAAI Conference on Artificial Intelligence (AAAI)

2025

S. Xing, H. Hua, X. Gao, S. Zhu, R. Li, K. Tian, X. Li, H. Huang, T. Yang, Z. Wang, Y. Zhou, H, Yao, Z. Tu

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

Transactions on Machine Learning Research (TMLR)

A. Velasquez, N. Bhatt*, U. Topcu, Z. Wang, K. Sycara, S. Stepputtis, S. Neemad, and G. Vallabhae

Neuro-symbolic AI as an Antithesis to Scaling Laws

Proceedings of the National Academy of Sciences Nexus (PNAS Nexus)

2025

Z. Yang*, X. Chen*, B. Zhu, T. Chen*, and Z. Wang

Deep Learning for Accurate Diagnosis of Viral Infections through scRNA-seq Analysis: A Comprehensive Benchmark Study

Journal of Data-centric Machine Learning Research (DMLR)

Z. Fan*, K. Wang*, K. Wen, Z. Zhu*, D. Xu*, and Z. Wang

LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS

Advances in Neural Information Processing Systems (NeurIPS) [Spotlight]

H. Hu*, Z. Fan*, T. Wu, Y. Xi*, S. Lee*, G. Pavlakos, and Z. Wang

Expressive Gaussian Human Avatars from Monocular RGB Video

Advances in Neural Information Processing Systems (NeurIPS)

R. Cai*, Y. Ro*, G. Kim, P. Wang*, B. Bejnordi, A. Akella, and Z. Wang

Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design

Advances in Neural Information Processing Systems (NeurIPS)

Z. Zhang*, R. Chen*, S. Liu*, Z. Yao, O. Ruwase, B. Chen, X. Wu, and Z. Wang

Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding

Advances in Neural Information Processing Systems (NeurIPS)

Z. Fan*, J. Zhang, W. Cong*, P. Wang*, R. Li, K. Wen, S. Zhou, A Kadambi, Z. Wang, D. Xu, B. Ivanovic, M. Pavone, and Y. Wang

Large Spatial Model: End-to-end Unposed Images to Semantic 3D

Advances in Neural Information Processing Systems (NeurIPS)

H. Yang*, B. Kailkhura, Z. Wang, and Y. Liang

Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis

Advances in Neural Information Processing Systems (NeurIPS)

2024

H. Liang, Y. Yin, D. Xu*, H. Liang*, Z. Wang, K. Plataniotis, Y. Zhao, and Y. Wei

Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models

Advances in Neural Information Processing Systems (NeurIPS)

H. Lu, Y. Zhou, S. Liu*, Z. Wang, M. Mahoney, and Y. Yang

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Advances in Neural Information Processing Systems (NeurIPS)

X. Zhao, G. Sun, R. Cai*, Y. Zhou, P. Li, P. Wang*, B. Tan, Y. He, L. Chen, Y. Liang, B. Chen, B. Yuan, H. Wang, A. Li, Z. Wang, and T. Chen*

Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild

Advances in Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS D & B)

Z. Zhu*, Z. Fan*, Y. Jiang*, and Z. Wang

FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting

European Conference on Computer Vision (ECCV)

S. Zhou, Z. Fan*, D. Xu*, H. Chang, P. Chari, T. Bharadwaj, S. You, Z. Wang, and A. Kadambi

DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting

European Conference on Computer Vision (ECCV)

R. Li, Z. Fan*, B. Wang, P. Wang*, Z. Wang, and X. Wu

VersatileGaussian: Real-time Neural Rendering for Versatile Tasks using Gaussian Splatting

European Conference on Computer Vision (ECCV)

Q. Li, J. Hong*, C. Xie, J. Tan, R. Xin, J. Hou, X. Yin, Z. Wang, D. Hendrycks, Z. Wang, B. Li, B. He, and D. Song

LLM-PBE: Assessing Data Privacy in Large Language Models

International Conference on Very Large Data Bases (VLDB) [Best Paper Finalist]

L. Sun*, N. Bhatt*, J. Liu*, Z. Fan*, Z. Wang, T. Humphreys, and U. Topcu

MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision Depth and Inertial Measurements

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [Oral Pitch Finalist]

R. Cai*, S. Muralidharan, G. Heinrich, H. Yin, Z. Wang, J. Kautz, and P. Molchanov

Flextron: Many-in-One Flexible Large Language Model

International Conference on Machine Learning (ICML) [Oral]

2024

R. Cai*, Y. Tian, Z. Wang, and B. Chen

LoCoCo: Dropping In Convolutions for Long Context Compression

International Conference on Machine Learning (ICML)

L. Yin*, A. Jaiswal*, S. Liu*, S. Kundu, and Z. Wang

Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs Difficult Downstream Tasks in LLMs

International Conference on Machine Learning (ICML)

L. Yin*, Y. Wu, Z. Zhang*, C. Hsieh, Y. Wang, Y. Jia, G. Li, A. Jaiswal*, M. Pechenizkiy, Y. Liang, M. Bendersky, Z. Wang, and S. Liu*

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

International Conference on Machine Learning (ICML)

R. Chen*, T. Zhao, A. Jaiswal*, N. Shah, and Z. Wang

LLaGA: Large Language and Graph Assistant

International Conference on Machine Learning (ICML)

J. Hong*, J. Duan, C. Zhang, Z. Li*, C. Xie, K. Lieberman, J. Diffenderfer, B. Bartoldson, A. Jaiswal*, K. Xu, B. Kailkhura, D. Hendrycks, D. Song, Z. Wang, and B. Li

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

International Conference on Machine Learning (ICML)

Z. Li*, S. Liu*, T. Chen*, A. Jaiswal*, Z. Zhang*, D. Wang, R. Krishnamoorthi, S. Chang, Z. Wang

Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once

International Conference on Machine Learning (ICML)

J. Zhao, Z. Zhang*, B. Chen, Z. Wang, A. Anandkumar, and Y. Tian

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

International Conference on Machine Learning (ICML) [Oral]

H. Dong, X. Yang, Z. Zhang*, Z. Wang, Y. Chi, and B. Chen

Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference

International Conference on Machine Learning (ICML)

Y. Zhang, P. Li, J. Hong*, J. Li, Y. Zhang, W. Zheng*, P. Chen, J. Lee, W. Yin, M. Hong, Z. Wang, S. Liu, and T. Chen*

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

International Conference on Machine Learning (ICML)

P. Wang*, D. Xu*, Z. Fan*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra

Taming Mode Collapse in Score Distillation for Text-to-3D Generation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

M. Varma, P. Wang*, Z. Fan*, Z. Wang, H. Su, and R. Ramamoorthi

Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

S. Zhou, H. Chang, S. Jiang, Z. Fan*, Z. Zhu*, D. Xu*, P. Chari, S. You, Z. Wang, and A. Kadambi

Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight]

V. Goel, E. Peruzzo, Y. Jiang*, D. Xu*, X. Xu, N. Sebe, T. Darrell, Z. Wang, H. Shi

PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

M. Ohanyan, H. Manukyan, Z. Wang, S. Navasardyan, and H. Shi

Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

X. Xu, J. Guo, Z. Wang, G. Huang, I. Essa, and H. Shi

Prompt-Free Diffusion: Taking 'Text' out of Text-to-Image Diffusion Models

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

M. D'Incà, E. Peruzzo, M. Mancini, D. Xu*, V. Goel, X. Xu, Z. Wang, H. Shi, and N. Sebe

OpenBias: Open-set Bias Detection in Generative Models

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight]

Z. Zhang*, S. Liu*, R. Chen*, B. Kailkhura, B. Chen, and Z. Wang

Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache

Conference on Machine Learning and Systems (MLSys)

Y. Yang, N. Bhatt*, T. Ingebrand, W. Ward, S. Carr, Z. Wang, and U. Topcu

Fine-Tuning Language Models Using Formal Methods Feedback

Conference on Machine Learning and Systems (MLSys)

2024

A. Jaiswal*, Z. Gan, X. Du, B. Zhang, Z. Wang, and Y. Yang

Compressing LLMs: The Truth is Rarely Pure and Never Simple

International Conference on Learning Representations (ICLR)

J. Hong*, J. Wang, C. Zhang, Z. LI*, B. Li, and Z. Wang

DP-OPT: Make Large Language Model Your Differentially-Private Prompt Engineer

International Conference on Learning Representations (ICLR) [Spotlight]

Y. Jiang*, H. Tang, J. Chang, L. Song, Z. Wang, and L. Cao

Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day

International Conference on Learning Representations (ICLR)

2024

W. Chen*, J. Wu*, Z. Wang, and B. Hanin

Principled Architecture-aware Scaling of Hyperparameters

International Conference on Learning Representations (ICLR)

P. Wang*, S. Yang, S. Li, Z. Wang, and P. Li

Polynomial Width is Sufficient for Set Representation with High-dimensional Features

International Conference on Learning Representations (ICLR)

2024

X. Chen*, Y. Yang, Z. Wang, and B. Mirzasoleiman

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

International Conference on Learning Representations (ICLR)

Y. You*, R. Zhou, J. Park, H. Xu, C. Tian, Z. Wang, and Y. Shen

Latent 3D Graph Diffusion

International Conference on Learning Representations (ICLR)

A. Isajanyan, A. Shatveryan, D. Kocharian, Z. Wang, and H. Shi

Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community

International Conference on Learning Representations (ICLR) [Spotlight]

S. Yu, J. Hong*, H. Zhang, H. Wang*, Z. Wang, and J. Zhou

Safe and Robust Watermark Injection with a Single OoD Image

International Conference on Learning Representations (ICLR)

D. Sow, S. Lin, Z. Wang, and Y. Liang

Doubly Robust Instance-Reweighted Adversarial Training

International Conference on Learning Representations (ICLR)

2024

E. Oikonomou, G. Holste*, N. Yuan, A. Coppi, R. McNamara, N. Haynes, A. Vora, E. Velazquez, F. Li, V. Menon, S. Kapadia, T. Gill, G. Nadkarni, H. Krumholz, Z. Wang, D. Ouyang, and R. Khera

A Multimodality Video-Based AI Biomarker for Aortic Stenosis Development and Progression

JAMA Cardiology

2024

G. Li, D. Hoang*, K. Bhardwaj, M. Lin, Z. Wang, and R. Marculescu

Zero-Shot Neural Architecture Search: Challenges Solutions and Opportunities

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

G. Holste*, Y. Zhou, S. Wang, A. Jaiswal, M. Lin, S. Zhuge, Y. Yang, D. Kim, T. Nguyen-Mau, M. Tran, J. Jeong, W. Park, J. Ryu, F. Hong, A. Verma, Y. Yamagishi, C. Kim, H. Seo, M. Kang, L. Celi, Z. Lu, R. Summers, G. Shih, Z. Wang, and Y. Peng

Towards Long-tailed Multi-label Disease Classification from Chest X-ray

Medical Image Analysis

G. Holste*, M. Lin, R. Zhou, F. Wang, L. Liu, Q. Yan, S. Tassel, K. Kovacs, E. Chew, Z. Lu, Z. Wang, and Y. Peng

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

npj Digital Medicine

2024

D. Xu*, Y. Yuan, M. Mardani, S. Liu, J. Song, Z. Wang, and A. Vahdat

AGG: Amortized Generative 3D Gaussians for Single Image to 3D

Transactions on Machine Learning Research (TMLR)

H. Yang*, Z. Jiang*, R. Zhang, Y. Liang, and Z. Wang

Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK

Journal of Machine Learning Research (JMLR)

2024

H. Yang*, Y. Liang, X. Guo, L. Wu, and Z. Wang

Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

Journal of Machine Learning Research (JMLR)

2024

W. Zheng*, S. Sharan*, Z. Fan*, K. Wang*, Y. Xi*, and Z. Wang

Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

E. Oikonomou, A. Vaid, G. Holste*, A. Coppi, R. McNamara, C. Baloescu, H. Krumholz, Z. Wang, D. Apakama, G. Nadkarni, and R. Khera

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study

Lancet Digital Health

2024

A. Jaiswal*, S. Liu*, T. Chen*, and Z. Wang

The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter

Advances in Neural Information Processing Systems (NeurIPS)

Z. Zhang*, Y. Sheng, T. Zhou, T. Chen*, L. Zheng, R. Cai*, Z. Song, Y. Tian, C. Ré, C. Barrett, Z. Wang, and B. Chen

H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Advances in Neural Information Processing Systems (NeurIPS)

D. Hoang*, S. Kundu, S. Liu*, and Z. Wang

Don't Just Prune by Magnitude! Your Mask Topology is A Secret Weapon

Advances in Neural Information Processing Systems (NeurIPS)

H. Wang*, Z. Jiang*, Y. You*, Y. Han*, G. Liu, J. Srinivasa, R. Kompella, Z. Wang

Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling

Advances in Neural Information Processing Systems (NeurIPS)

Z. Wang, Y. Jiang*, Y. Lu, Y. Shen, P. He, W. Chen, Z. Wang, M. Zhou

In-Context Learning Unlocked for Diffusion Models

Advances in Neural Information Processing Systems (NeurIPS) [Spotlight]

Z. Wang, Y. Jiang*, H. Zheng, P. Wang*, P. He, Z. Wang, W. Chen, M. Zhou

Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models

Advances in Neural Information Processing Systems (NeurIPS)

L. Yin*, G. Li, M. Fang, L. Shen, T. Huang, Z. Wang, V. Menkovski, X. Ma, M. Pechenizkiy, and S. Liu*

Dynamic Sparsity Is Channel-Level Sparsity Learner

Advances in Neural Information Processing Systems (NeurIPS)

W. Cong*, H. Liang*, P. Wang*, Z. Fan*, T. Chen*, M. Varma*, Y. Wang*, and Z. Wang

Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

IEEE International Conference on Computer Vision (ICCV)

A. Jaiswal*, X. Zhang, S. Chan, and Z. Wang

Physics-Driven Turbulence Image Restoration with Stochastic Refinement

IEEE International Conference on Computer Vision (ICCV)

Y. Han*, P. Wang*, S. Kundu, Y. Ding, and Z. Wang

Vision HGNN: An Image is More than a Graph of Nodes

IEEE International Conference on Computer Vision (ICCV) [Oral]

T. Chen*, X. Chen*, X. Du, A. Rashwan, F. Yang, H. Chen, Z. Wang, and Y. Li

AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts

IEEE International Conference on Computer Vision (ICCV)

C. Li, B. Feng, Z. Fan*, P. Pan, and Z. Wang

StegaNeRF: Embedding Invisible Information within Neural Radiance Fields

IEEE International Conference on Computer Vision (ICCV)

X. Xu, Z. Wang, G. Zhang, K. Wang, and H. Shi

Versatile Diffusion: Text Images and Variations All in One Diffusion Model

IEEE International Conference on Computer Vision (ICCV)

Y. Zhang, R. Cai*, T. Chen*, G. Zhang, H. Zhang, P. Chen, S. Chang, Z. Wang, and S. Liu

Robust Mixture-of-Expert Training for Convolutional Neural Networks

IEEE International Conference on Computer Vision (ICCV) [Oral]

L. Khachatryan, A. Movsisyan, V. Tadevosyan, R. Henschel, Z. Wang, S. Navasardyan, and H. Shi

Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators

IEEE International Conference on Computer Vision (ICCV) [Oral]

G. Holste*, Z. Jiang*, A. Jaiswal*, M. Hanna, S. Minkowitz, A. Legasto, J. Escalon, S. Steinberger, M. Bittman, T. Shen, Y. Ding, R. Summers, G. Shih, Y. Peng, and Z. Wang

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Medical Image Computing and Computer Assisted Interventions (MICCAI)

W. Chen*, W. Huang, and Z. Wang

No Free Lunch in Neural Architectures? A Joint Analysis of Expressivity Convergence and Generalization

International Conference on Automated Machine Learning (AutoML-Conf)

X. Chen*, T. Chen*, W. Chen, A. Awadallah, Z. Wang, and Y. Cheng

DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

Annual Meeting of the Association for Computational Linguistics (ACL)

A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang

Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models

International Conference on Machine Learning (ICML) [Oral]

A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang

Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication

International Conference on Machine Learning (ICML)

R. Cai*, Z. Zhang*, and Z. Wang

Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?

International Conference on Machine Learning (ICML)

P. Wang*, R. Panda, and Z. Wang

Data Efficient Neural Scaling Law via Model Reusing

International Conference on Machine Learning (ICML)

W. Zheng*, S. Sharan*, A. Jaiswal*, K. Wang*, Y. Xi*, D. Xu*, and Z. Wang

Outline Then Details: Syntactically Guided Coarse-To-Fine Code Generation

International Conference on Machine Learning (ICML)

X. Chen*, N. Vadori, T. Chen*, and Z. Wang

Learning to Optimize Differential Games

International Conference on Machine Learning (ICML)

T. Huang, L. Yin*, Z. Zhang*, L. Shen, M. Fang, M. Pechenizkiy, Z. Wang, and S. Liu*

Are Large Kernels Better Teachers than Transformers for ConvNets?

International Conference on Machine Learning (ICML)

Y. Ro*, Z. Wang, V. Chidambaram, and A. Akella

Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit

International Conference on Machine Learning (ICML)

J. Liu, X. Chen*, Z. Wang, W. Yin, and H. Cai

Towards Constituting Mathematical Structures for Learning to Optimize

International Conference on Machine Learning (ICML)

D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, Y. Wang*, and Z. Wang

NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360? Views

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight]

Y. Jiang*, P. Hedman, B. Mildenhall, D. Xu*, J. Barron, Z. Wang, and T. Xue

AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

X. Gong*, S. Mohan, N. Dhingra, J. Bazin, Y. Li, Z. Wang, and R. Ranjan

MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

H. Lu*, H. Tunanyan, K. Wang, S. Navasardyan, Z. Wang, and H. Shi

Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

D. Hoang*, S. Liu*, R. Marculescu, and Z. Wang

Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph

International Conference on Learning Representations (ICLR) [Oral]

S. Liu*, T. Chen*, Z. Zhang*, X. Chen*, T. Huang, A. Jaiswal*, and Z. Wang

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

International Conference on Learning Representations (ICLR) [Spotlight]

T. Chen*, Z. Zhang*, A. Jaiswal*, S. Liu*, and Z. Wang

Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers

International Conference on Learning Representations (ICLR) [Spotlight]

P. Wang*, R. Panda, L. Hennigen, P. Greengard, L. Karlinsky, R. Feris, D. Cox, Z. Wang, and Y. Kim

Learning to Grow Pretrained Models for Efficient Transformer Training

International Conference on Learning Representations (ICLR) [Spotlight]

S. Yu, J. Hong, H. Wang*, Z. Wang, and J. Zhou

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

International Conference on Learning Representations (ICLR) [Spotlight]

S. Liu*, T. Chen*, X. Chen*, X. Chen*, Q. Xiao, B. Wu, T. Karkkainen, M. Pechenizkiy, D. Mocanu, and Z. Wang

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

International Conference on Learning Representations (ICLR)

M. Varma*, P. Wang*, X. Chen*, T. Chen*, S. Venugopalan, and Z. Wang

Is Attention All That NeRF Needs?

International Conference on Learning Representations (ICLR)

Z. Fan*, P. Wang*, Y. Jiang*, X. Gong*, D. Xu*, and Z. Wang

NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes

International Conference on Learning Representations (ICLR)

Z. Jiang*, Y. Chen, M. Liu, D. Chen, X. Dai, L. Yuan, Z. Liu, and Z. Wang

Layer Grafted Pre-training: Bridging Contrastive Learning and Masked Image Modeling For Label-Efficient Representations

International Conference on Learning Representations (ICLR)

T. Chen*, C. Gong, D. Diaz, X. Chen*, J. Wells, Q. Liu, Z. Wang, A. Ellington, A. Dimakis, and A. Klivans

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing

International Conference on Learning Representations (ICLR)

P. Wang*, S. Yang, Y. Liu, Z. Wang, and P. Li

Equivariant Hypergraph Diffusion Neural Operators

International Conference on Learning Representations (ICLR)

Y. You*, T. Chen*, Z. Wang, and Y. Shen

Graph Domain Adaptation via Theory-Grounded Spectral Regularization

International Conference on Learning Representations (ICLR)

J. Yang, X. Chen*, T. Chen*, Z. Wang, and Y. Liang

M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation

International Conference on Learning Representations (ICLR)

H. Fan, Z. Wang, Y. Yang, and M. Kankanhalli

Continuous-Discrete Convolution for (3+1)D Geometry-Sequence Modeling in Proteins

International Conference on Learning Representations (ICLR)

H. Yang*, and Z. Wang

On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks

International Conference on Artificial Intelligence and Statistics (AISTATS)

J. Yang, T. Chen*, M. Zhu*, F. He, D. Tao, Y. Liang, and Z. Wang

Learning to Generalize Provably in Learning to Optimize

International Conference on Artificial Intelligence and Statistics (AISTATS)

H. Heaton, X. Chen*, Z. Wang, and W. Yin

Safeguarded Learned Convex Optimization

AAAI Conference on Artificial Intelligence (AAAI)

2023

J. Hong, H. Wang*, Z. Wang, and J. Zhou

Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning

AAAI Conference on Artificial Intelligence (AAAI)

2023

Z. Kong, H. Ma, G. Yuan, M. Sun, Y. Xie, P. Dong, X. Meng, X. Shen, H. Tang, M. Qin, T. Chen*, X. Ma, X. Xie, Z. Wang, and Y. Wang

Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training

AAAI Conference on Artificial Intelligence (AAAI)

Y. Han*, E. Huang, W. Zheng*, N. Rao, Z. Wang, and K. Subbian

Search Behavior Prediction: A Hypergraph Perspective

ACM International Conference on Web Search and Data Mining (WSDM)

P. Narayanan, X. Hu, Z. Wu*, M. Thielke, J. Rogers, A. Harrison, J. D'Agostino, J. Brown, L. Quang, J. Uplinger, H. Kwon, and Z. Wang

A Multi-Purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth

IEEE Transactions on Image Processing (TIP)

H. Wang*, J. Hong, J. Zhou, and Z. Wang

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts

Transactions on Machine Learning Research (TMLR)

2023

Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang

Can Pruning Improve Certified Robustness of Neural Networks?

Transactions on Machine Learning Research (TMLR)

X. Yang, Z. Wang, S. Hu, C. Kim, S. Yu, M. Pajic, R. Manohar, Y. Chen, and H. Li

Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design

IEEE Transactions on Circuits and Systems II (TCAS-II)

2023

W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian

You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction

Transactions on Machine Learning Research (TMLR)

G. Holste*, E. Oikonomou, B. Mortazavi, A. Coppi, K. Faridi, E. Miller, J. Forrest, R. McNamara, L. Ohno-Machado, N. Yuan, A. Gupta, D. Ouyang, H. Krumholz, Z. Wang, and R. Khera

Severe Aortic Stenosis Detection by Deep Learning Applied to Echocardiography

European Heart Journal (EHJ) [Editor's Pick]

W. Zheng*, H. Yang, J. Cai, P. Wang*, X. Jiang, S. Du, Y. Wang, and Z. Wang

Integrating the Traffic Science with Representation Learning for City-Wide Network Congestion Prediction

Elsevier Information Fusion

Q. Wu*, X. Chen*, Y. Jiang*, and Z. Wang

Chasing Better Deep Image Priors between Over- and Under-Parameterization

Transactions on Machine Learning Research (TMLR)

M. Lin, T. Li, Y. Yang, G. Holste*, Y. Ding, S. Tassel, K. Kovacs, G. Shih, Z. Wang, Z. Lu, F. Wang, and Y. Peng

Improving Model Fairness in Image-based Computer-Aided Diagnosis

Nature Communications

Z. Jiang*, G. Zheng, Y. Cheng, A. Awadallah, and Z. Wang

CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning

Transactions on Machine Learning Research (TMLR)

W. Chen*, X. Gong*, J. Wu*, Y. Wei, H. Shi, Z. Yan, Y. Yang, and Z. Wang

Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

T. Huang, T. Chen*, M. Fang, V. Menkovski, J. Zhao, L. Yin, Y. Pei, D. Mocanu, Z. Wang, M. Pechenizkiy, and S. Liu*

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained Graph Tickets

Learning on Graphs Conference (LoG) [Oral & Best Paper Award]

W. Chen*, W. Huang, X. Gong*, B. Hanin, and Z. Wang

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis

Advances in Neural Information Processing Systems (NeurIPS)

D. Xu*, P. Wang*, Y. Jiang*, Z. Fan*, and Z. Wang

Signal Processing for Implicit Neural Representations

Advances in Neural Information Processing Systems (NeurIPS)

H. Liang*, Z. Fan*, R. Sarkar, Z. Jiang*, T. Chen*, K. Zou, Y. Cheng, C. Hao, and Z. Wang

M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design

Advances in Neural Information Processing Systems (NeurIPS)

Z. Jiang*, X. Chen*, X. Huang, X. Du, D. Zhou, and Z. Wang

Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropogation

Advances in Neural Information Processing Systems (NeurIPS)

R. Cai*, Z. Zhang*, T. Chen*, X. Chen*, and Z. Wang

Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets

Advances in Neural Information Processing Systems (NeurIPS)

S. Sharan*, W. Zheng*, K. Hsu, J. Xiong, A. Chen, and Z. Wang

Symbolic Distillation for Learned TCP Congestion Control

Advances in Neural Information Processing Systems (NeurIPS)

A. Jaiswal*, P. Wang*, T. Chen*, J. Rousseau, Y. Ding, and Z. Wang

Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again

Advances in Neural Information Processing Systems (NeurIPS)

H. Wang*, J. Hong, A. Zhang, J. Zhou, and Z. Wang

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

Advances in Neural Information Processing Systems (NeurIPS)

J. Wu*, Y. Liang, F. Han, H. Akbari, Z. Wang, and C. Yu

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

Advances in Neural Information Processing Systems (NeurIPS)

2022

M. Varma*, X. Chen*, Z. Zhang*, T. Chen*, S. Venugopalan, and Z. Wang

Sparse Winning Tickets are Data-Efficient Image Recognizers

Advances in Neural Information Processing Systems (NeurIPS)

T. Wei, Y. You*, T. Chen*, Y. Shen, J. He, and Z. Wang

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

Advances in Neural Information Processing Systems (NeurIPS)

K. Duan, Z. Liu, P. Wang*, W. Zheng*, K. Zhou, T. Chen*, X. Hu, and Z. Wang

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

Advances in Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS D & B)

D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, H. Shi, and Z. Wang

SinNeRF: Training Neural Radiance Field on Complex Scenes from a Single Image

European Conference on Computer Vision (ECCV)

Z. Fan*, Y. Jiang*, P. Wang*, X. Gong*, D. Xu*, and Z. Wang

Unified Implicit Neural Stylization

European Conference on Computer Vision (ECCV)

X. Chen*, T. Chen*, Y. Cheng, W. Chen, A. Awadallah, and Z. Wang

Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models

European Conference on Computer Vision (ECCV)

H. Liang*, H. Fan*, Z. Fan*, Y. Wang*, T. Chen*, Y. Cheng, and Z. Wang

Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction

European Conference on Computer Vision (ECCV)

Z. Jiang*, T. Chen*, X. Chen*, Y. Cheng, L. Zhou, L. Yuan, A. Awadallah, and Z. Wang

DnA: Improving Few-shot Transfer Learning with Low-Rank Decomposition and Alignment

European Conference on Computer Vision (ECCV)

Y. Jiang*, B. Wronski, B. Mildenhall, J. Barron, Z. Wang, and T. Xue

Fast and High Quality Image Denoising via Malleable Convolution

European Conference on Computer Vision (ECCV)

W. Chen*, X. Du, F. Yang, L. Beyer, X. Zhai, T. Lin, H. Chen, J. Li, X. Song, Z. Wang, and D. Zhou

A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation

European Conference on Computer Vision (ECCV)

2022

Z. Mao, A. Jaiswal*, Z. Wang, and S. Chan

Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

European Conference on Computer Vision (ECCV)

H. Wang*, A. Zhang, Y. Zhu, S. Zheng, M. Li, A. Smola, and Z. Wang

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition

International Conference on Machine Learning (ICML) [Oral]

H. Wang*, A. Zhang, S. Zheng, X. Shi, M. Li, and Z. Wang

Removing Batch Normalization Boosts Adversarial Training

International Conference on Machine Learning (ICML)

P. Wang*, Z. Fan*, T. Chen*, and Z. Wang

Neural Implicit Dictionary Learning via Mixture-of-Expert Training

International Conference on Machine Learning (ICML)

A. Jaiswal*, H. Ma, T. Chen*, Y. Ding, and Z. Wang

Training Your Sparse Neural Network Better with Any Mask

International Conference on Machine Learning (ICML)

T. Chen*, H. Zhang, Z. Zhang*, S. Chang, S. Liu, P. Chen, and Z. Wang

Linearity Grafting: How Neuron Pruning Helps Certifiable Robustness

International Conference on Machine Learning (ICML)

T. Chen*, X. Chen*, X. Ma, Y. Wang, and Z. Wang

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

International Conference on Machine Learning (ICML)

T. Chen*, Z. Zhang*, S. Liu, Y. Zhang, S. Chang, and Z. Wang

Data-Efficient Double-Win Lottery Tickets from Robust Pre-training

International Conference on Machine Learning (ICML)

W. Redman, T Chen*, Z. Wang, and A. Dogra

Universality of Winning Tickets: A Renormalization Group Perspective

International Conference on Machine Learning (ICML)

2022

R. Ardywibowo, Z. Huo, Z. Wang, B. Mortazavi, S. Huang, and X. Qian

VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty

International Conference on Machine Learning (ICML)

2022

D. Hoang*, K. Zhou, T. Chen*, X. Hu, and Z. Wang

AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks

International Conference on Automated Machine Learning (AutoML-Conf)

J. Hong, Z. Wang, and J. Zhou

Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent

ACM Conference on Fairness Accountability and Transparency (FAccT)

2022

T. Chen*, Z. Zhang*, Y. Cheng, A. Awadallah, and Z. Wang

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

T. Chen*, P. Wang*, Z. Fan*, and Z. Wang

Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Z. Fan*, T. Chen*, P. Wang*, and Z. Wang

CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawing

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Oral]

T. Chen*, Z. Zhang*, Y. Zhang, S. Chang, S. Liu, and Z. Wang

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

X. Sun, A. Hassani, Z. Wang, G. Huang, and H. Shi

DiSparse: Disentangled Sparsification for Multitask Model Compression

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Z. Chen, Y. Chen, J. Liu, X. Xu, V. Goel, Z. Wang, H. Shi, and X. Wang

VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

H. Ma, H. Zhao, Z. Lin, A. Kale, Z. Wang, T. Yu, J. Gu, S. Choudhary, and X. Xie

EI-CLIP: Entity-aware Interventional Contrastive Learning for E-commerce Cross-modal Retrieval

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2022

W. Zheng*, T. Chen*, T. Hu*, and Z. Wang

Symbolic Learning to Optimize: Towards Interpretability and Scalability

International Conference on Learning Representations (ICLR)

X. Chen*, J. Zhang*, and Z. Wang

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network and How to Find It Efficiently

International Conference on Learning Representations (ICLR)

T. Huang*, T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang

Optimizer Amalgamation

International Conference on Learning Representations (ICLR)

T. Chen*, Z. Zhang*, P. Wang, S. Balachandra*, H. Ma, Z. Wang, and Z. Wang

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

International Conference on Learning Representations (ICLR)

P. Wang*, W. Zheng*, T. Chen*, and Z. Wang

Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

International Conference on Learning Representations (ICLR)

W. Chen*, W Huang, X. Du, X. Song, Z. Wang, and D. Zhou

Auto-Scaling Vision Transformers without Training

International Conference on Learning Representations (ICLR)

S. Yu*, T. Chen*, J. Shen*, H. Yuan, J. Tian, S. Yang, J. Liu, and Z. Wang

Unified Visual Transformer Compression

International Conference on Learning Representations (ICLR)

M. Lu*, X. Luo*, T. Chen*, W. Chen*, D. Liu, and Z. Wang

Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot Any-Sparsity And No Retraining

International Conference on Learning Representations (ICLR) [Spotlight]

W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

International Conference on Learning Representations (ICLR)

S. Ding, T. Chen*, and Z. Wang

Audio Lottery: Speech Recognition Made Ultra-Lightweight Noise-Robust and Transferable

International Conference on Learning Representations (ICLR)

J. Hong, H. Wang*, Z. Wang, and J. Zhou

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

International Conference on Learning Representations (ICLR)

S. Liu, T. Chen*, Z. Atashgahi, X. Chen*, G. Sokar, E. Mocanu, M. Pechenizkiy, Z. Wang, and D. Mocanu

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

International Conference on Learning Representations (ICLR)

S. Liu, T. Chen*, X. Chen*, L. Shen, D. Mocanu, Z. Wang, and M. Pechenizkiy

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

International Conference on Learning Representations (ICLR)

Y. You*, Y. Cao, T. Chen*, Z. Wang, and Y. Shen

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What Why and How

International Conference on Learning Representations (ICLR)

R. Ardywibowo, S. Boluki, Z. Wang, B. Mortazavi, S. Huang, and X. Qian

VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition

International Conference on Artificial Intelligence and Statistics (AISTATS)

2022

S. Bibikar, H. Vikalo, Z. Wang, and X. Chen*

Federated Dynamic Sparse Training: Computing Less Communicating Less Yet Learning Better

AAAI Conference on Artificial Intelligence (AAAI)

Y. You*, T. Chen*, Z. Wang and Y. Shen

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

ACM International Conference on Web Search and Data Mining (WSDM)

T. Chen*, X. Chen*, W. Chen*, H. Heaton, J. Liu, Z. Wang, and W. Yin

Learning to Optimize: A Primer and A Benchmark

Journal of Machine Learning Research (JMLR)

T. Chen*, K. Zhou, K. Duan, W. Zheng*, P. Wang*, X. Hu, and Z. Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang

Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Transactions on Machine Learning Research (TMLR) [Featured Certification]

S. Mohseni, H. Wang*, Z. Yu, C. Xiao, Z. Wang, J. Yadawa

Taxonomy of Machine Learning Safety: A Survey and Primer

ACM Computing Surveys (CSUR)

2022

T. Chen*, Y. Cheng, Z. Gan, J. Wang, L. Wang, J. Liu, and Z. Wang

Adversarial Feature Augmentation and Normalization for Visual Recognition

Transactions on Machine Learning Research (TMLR)

Y. Han*, G. Holste*, Y. Ding, A. Tewfik, Y. Peng, and Z. Wang

Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays

IEEE Transactions on Medical Imaging (TMI)

T. Chen*, Z. Zhang*, J. Wu, R. Huang, S. Liu, S. Chang, and Z. Wang

Can You Win Everything with A Lottery Ticket?

Transactions on Machine Learning Research (TMLR)

Y. Jiang*, S. Chang, and Z. Wang

TransGAN: Two Pure Transformers Can Make One Strong GAN and That Can Scale Up

Advances in Neural Information Processing Systems (NeurIPS)

H. Wang*, C. Xiao, J. Kossaifi, Z. Yu, A. Anandkumar, and Z. Wang

AugMax: Adversarial Composition of Random Augmentations for Robust Training

Advances in Neural Information Processing Systems (NeurIPS)

T. Chen*, Y. Cheng, Z. Gan, J. Liu, and Z. Wang

Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective

Advances in Neural Information Processing Systems (NeurIPS)

X. Chen*, Y. Cheng, S. Wang, Z. Gan, J. Liu, and Z. Wang

The Elastic Lottery Ticket Hypothesis

Advances in Neural Information Processing Systems (NeurIPS)

T. Chen*, Y. Cheng, Z. Gan, L. Yuan, L. Zhang, and Z. Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

Advances in Neural Information Processing Systems (NeurIPS)

W. Zheng*, Q. Guo, H. Yang, P. Wang*, and Z. Wang

Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems

Advances in Neural Information Processing Systems (NeurIPS)

X. Chen*, T. Chen*, Z. Zhang*, and Z. Wang

You Are Caught Stealing My Winning Lottery Ticket! Making a Lottery Ticket Claim its Ownership

Advances in Neural Information Processing Systems (NeurIPS)

Z. Jiang*, T. Chen*, T. Chen, and Z. Wang

Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling

Advances in Neural Information Processing Systems (NeurIPS)

X. Chen*, J. Liu, Z. Wang, W. Yin

Hyperparameter Tuning is All You Need for LISTA

Advances in Neural Information Processing Systems (NeurIPS)

J. Wu*, X. Dai, D. Chen, Y. Chen, M. Liu, Y. Yu, Z. Wang, Z. Liu, M. Chen, and L. Yuan

Stronger NAS with Weaker Predictors

Advances in Neural Information Processing Systems (NeurIPS)

B. Pan, R. Panda, Y. Jiang*, Z. Wang, R. Feris, and A. Oliva

IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers

Advances in Neural Information Processing Systems (NeurIPS)

S. Liu, T. Chen*, X. Chen*, Z. Atashgahi, L. Yin, H. Kou, L. Shen, M. Pechenizkiy, Z. Wang, and D. Mocanu

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration

Advances in Neural Information Processing Systems (NeurIPS)

X. Ma, G. Yuan, X. Shen, T. Chen*, X. Chen*, X. Chen*, N. Liu, M. Qin, S. Liu, Z. Wang, and Y. Wang

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

Advances in Neural Information Processing Systems (NeurIPS)

Y. Jiang*, H. Zhang, J. Zhang, Y. Wang, Z. Lin, K. Sunkavalli, S. Chen, S. Amirghods, S. Kong, and Z. Wang

SSH: A Self-Supervised Framework for Image Harmonization

IEEE International Conference on Computer Vision (ICCV)

X. Gong*, H. Wang, M. Shou, M. Feiszli, Z. Wang, and Z. Yan

Searching for Two-Stream Models in Multivariate Space for Video Recognition

IEEE International Conference on Computer Vision (ICCV)

2021

Y. Guo, H. Yuan, J. Tan, Z. Wang, S. Yang, and J. Liu

GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

IEEE International Conference on Computer Vision (ICCV)

2021

Y. You*, T. Chen*, Y. Shen, and Z. Wang

Graph Contrastive Learning Automated

International Conference on Machine Learning (ICML) [Oral]

M. Zhu*, T. Chen*, and Z. Wang

Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

International Conference on Machine Learning (ICML) [Oral]

T. Chen*, Y. Sui, X. Chen*, A. Zhang, and Z. Wang

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

International Conference on Machine Learning (ICML)

Z. Jiang*, T. Chen*, B. Mortazavi, and Z. Wang

Self-Damaging Contrastive Learning

International Conference on Machine Learning (ICML)

Z. Zhang*, X. Chen*, T. Chen*, and Z. Wang

Efficient Lottery Ticket Finding: Less Data is More

International Conference on Machine Learning (ICML)

X. Chen*, Y. Cheng, S. Wang, Z. Gan, Z. Wang, and J. Liu

EarlyBERT: Efficient BERT Training via Early-Bird Lottery Tickets

Annual Meeting of the Association for Computational Linguistics (ACL)

J. Hong, Z. Zhu, S. Yu, Z. Wang, H. Dodge, and J. Zhou

Federated Adversarial Debiasing for Fair and Transferable Representations

ACM Conference on Knowledge Discovery and Data Mining (KDD)

T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Z. Wang, H. Wang*, T. Chen*, Z. Wang, and K. Ma

Troubleshooting Blind Image Quality Models in the Wild

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

P. Cao, Z. Wang, and K. Ma

Debiased Subjective Assessment of Real-World Image Enhancement

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2021

H. Ma, T. Chen*, T. Hu*, C. You, X. Xie, and Z. Wang

Undistillable: Making A Nasty Teacher That CANNOT Teach Students

International Conference on Learning Representations (ICLR) [Spotlight]

T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang

Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning

International Conference on Learning Representations (ICLR)

T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang

Robust Overfitting May be Mitigated by Properly Learned Smoothening

International Conference on Learning Representations (ICLR)

W. Chen*, X. Gong*, and Z. Wang

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

International Conference on Learning Representations (ICLR)

W. Chen*, Z. Yu, S. Mello, S. Liu, J. Alvarez, Z. Wang, and A. Anandkumar

Contrastive Syn-to-Real Generalization

International Conference on Learning Representations (ICLR)

T. Meng, X. Chen*, Y. Jiang*, and Z. Wang

A Design Space Study for LISTA and Beyond

International Conference on Learning Representations (ICLR)

J. Shen*, X. Chen*, H. Heaton, T. Chen*, J. Liu, W. Yin, and Z. Wang

Learning A Minimax Optimizer: A Pilot Study

International Conference on Learning Representations (ICLR)

J. Shen*, H. Wang*, S. Gui, J. Tan, Z. Wang, and J. Liu

UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems

International Conference on Learning Representations (ICLR)

J. Hong, H. Wang*, Z. Wang, and J. Zhou

Learning Model-Based Privacy Protection under Budget Constraints

AAAI Conference on Artificial Intelligence (AAAI)

2021

Y. Jiang*, X. Gong*, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang

EnlightenGAN: Deep Light Enhancement without Paired Supervision

IEEE Transactions on Image Processing (TIP) (IEEE SPS Young Author Best Paper Award, 2024)

S. Yang*, Z. Wang, and J. Liu

Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2021

J. Yan, Y. Zhong, Y. Fang, Z. Wang, and K. Ma

Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition

International Journal of Computer Vision (IJCV)

S. Yang*, Z. Wang, J. Jiu, and Z. Guo

Controllable Sketch-to-Image Translation for Robust Face Synthesis

IEEE Transactions on Image Processing (TIP)

X. Chen*, Y. Zhao, Y. Wang, P. Xu, H. You, C. Li, Y. Fu, Y. Lin, and Z. Wang

SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

T. Hu*, F. Gama, T. Chen*, W. Zheng*, Z. Wang, A. Ribeiro, and B. Sadler

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

IEEE Transactions on Signal and Information Processing over Networks (TSIPN)

T. Chen*, W. Zhang, J. Zhou, S. Chang, S. Liu, L. Amini, and Z. Wang

Training Stronger Baselines for Learning to Optimize

Advances in Neural Information Processing Systems (NeurIPS) [Spotlight]

H. Wang*, T. Chen*, S. Gui, T. Hu*, J. Liu, and Z. Wang

Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free

Advances in Neural Information Processing Systems (NeurIPS)

T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin

The Lottery Ticket Hypothesis for Pre-trained BERT Networks

Advances in Neural Information Processing Systems (NeurIPS)

X. Chen*, Z. Wang, S. Tang, and K. Muandet

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

Advances in Neural Information Processing Systems (NeurIPS)

Z. Jiang*, T. Chen*, T. Chen, and Z. Wang

Robust Pre-Training by Adversarial Contrastive Learning

Advances in Neural Information Processing Systems (NeurIPS)

Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, and Y. Shen

Graph Contrastive Learning with Augmentations

Advances in Neural Information Processing Systems (NeurIPS)

H. You, X. Chen*, Y. Zhang, C. Li, S. Li, Z. Liu, Z. Wang, and Y. Lin

ShiftAddNet: A Hardware-Inspired Deep Network

Advances in Neural Information Processing Systems (NeurIPS)

Y. Fu, H. You, Y. Zhao, Y. Wang, C. Li, K. Gopalakrishnan, Z. Wang, and Y. Lin

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Advances in Neural Information Processing Systems (NeurIPS)

H. Wang*, S. Gui, H. Yang, J. Liu, and Z. Wang

GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework

European Conference on Computer Vision (ECCV) [Spotlight]

S. Yang*, Z. Wang, J. Liu, and Z. Guo

Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches

European Conference on Computer Vision (ECCV)

C. Li, T. Chen*, H. You, Z. Wang, and Y Lin

HALO: Hardware-Aware Learning to Optimize

European Conference on Computer Vision (ECCV)

Z. Huo, A. PakBin, X. Chen*, N. Hurley, Y. Yuan*, X. Qian, Z. Wang, S. Huang, and B. Mortazavi

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

International Conference on Artificial Intelligence and Statistics (AISTATS)

2020

W. Chen*, Z. Yu, Z. Wang, and A. Anandkumar

Automated Synthetic-to-Real Generalization

International Conference on Machine Learning (ICML)

X. Chen*, W. Chen*, T. Chen*, Y. Yuan*, C. Gong, K. Chen, and Z. Wang

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

International Conference on Machine Learning (ICML)

Y. You*, T. Chen*, Z. Wang, and Y. Shen

When Does Self-Supervision Help Graph Convolutional Networks?

International Conference on Machine Learning (ICML)

R. Oftadeh, J. Shen*, Z. Wang, and D. Shell

Eliminating the Invariance on the Loss Landscape of Linear Autoencoders

International Conference on Machine Learning (ICML)

2020

Y. Fu, W. Chen*, H. Wang*, H. Li, Y. Lin, and Z. Wang

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

International Conference on Machine Learning (ICML)

R. Ardywibowo, S. Boluki, X. Gong*, Z. Wang, and X. Qian

NADS: Neural Architecture Distribution Search for Uncertainty Awareness

International Conference on Machine Learning (ICML)

Y. Zhao, X. Chen*, Y. Wang, C. Li, Y. Xie, Z. Wang, and Y. Lin

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

IEEE/ACM International Symposium on Computer Architecture (ISCA)

2020

T. Chen*, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang

Adversarial Robustness: From Self-Supervised Pretraining to Fine-Tuning

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Z. Jiang*, B. Liu, S. Schulter, Z. Wang, and M. Chandraker

Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Oral]

2020

Y. You*, T. Chen*, Z. Wang, and Y. Shen

L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

T. Hu*, T. Chen*, H. Wang*, and Z. Wang

Triple Wins: Boosting Accuracy Robustness and Efficiency Together by Enabling Input-Adaptive Inference

International Conference on Learning Representations (ICLR)

W. Chen*, X. Gong*, X. Liu, Q. Zhang, Y. Li and Z. Wang

FasterSeg: Searching for Faster Real-time Semantic Segmentation

International Conference on Learning Representations (ICLR)

H. Wang*, T. Chen*, Z. Wang, and K. Ma

I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

International Conference on Learning Representations (ICLR)

H. You, C. Li, P. Xu, Y. Fu, Y. Wang, X. Chen*, R. Baraniuk, Z. Wang, and Y. Lin

Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks

International Conference on Learning Representations (ICLR) [Spotlight]

J. Shen*, Y. Wang*, P. Xu, Y. Fu, Z. Wang, and Y. Lin

Fractional Skipping: Toward Finer-Grained Dynamic Inference

AAAI Conference on Artificial Intelligence (AAAI)

S. Mohseni*, M. Pitale, J. Yadawa, and Z. Wang

Self-Supervised Learning for Generalizable Out-of-Distribution Detection

AAAI Conference on Artificial Intelligence (AAAI)

2020

Z. Wu*, H. Wang*, Z. Wang, H. Jin, and Z. Wang

Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

M. Karimi, D. Wu, Z. Wang, and Y. Shen

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

Journal of Chemical Information and Modeling (JCIM)

2020

S. Li, W. Ren, F. Wang, I. Araujo*, E. K. Tokuda*, R. Hirata, R. Cesar, Z. Wang, and X. Cao

A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives

International Journal of Computer Vision (IJCV)

2020

Y. Yuan*, W. Yang, W. Ren, J Liu, W. J. Scheirer, and Z. Wang, et al.

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

IEEE Transactions on Image Processing (TIP)

2020

R. G. VidalMata, ... Y. Yuan*, J. Wu*, Z. Wang, ... et al.

Bridging the Gap Between Computational Photography and Visual Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Z. Jiang*, Y. Wang*, X. Chen*, P. Xu, Y. Zhao, Y. Lin, and Z. Wang

E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings

Advances in Neural Information Processing Systems (NeurIPS)

S. Gui, H. Wang*, H. Yang, C. Yu, Z. Wang, and J. Liu

Model Compression with Adversarial Robustness: A Unified Optimization Framework

Advances in Neural Information Processing Systems (NeurIPS)

Y. Cao, T. Chen*, Z. Wang, and Y. Shen

Learning to Optimize in Swarms

Advances in Neural Information Processing Systems (NeurIPS)

X. Jia, S. Wang*, X. Liang, A. Balagopal, D. Nguyen, M. Yang, Z. Wang, X. Qian, X. Ji, and S. Jiang

Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation

Medical Image Computing and Computer Assisted Interventions (MICCAI)

2019

R. Ardywibowo, G. Zhao, Z. Wang, B. Mortazavi, S. Huang, and X. Qian

Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

International Conference on Artificial Intelligence and Statistics (AISTATS)

2019

S. Yang*, Z. Wang, Z Wang, N. Xu, J. Liu, and Z. Guo

Controllable Artistic Text Style Transfer via Shape-Matching GAN

IEEE International Conference on Computer Vision (ICCV) [Oral]

Z. Wu*, K. Suresh, P. Narayanan, H. Xu, H. Kwon, and Z. Wang

Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

IEEE International Conference on Computer Vision (ICCV)

X. Gong*, S. Chang, Y. Jiang*, and Z. Wang

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

IEEE International Conference on Computer Vision (ICCV)

T. Chen*, S. Ding, J. Xie, Y. Yuan*, W. Chen*, Y. Yang, Z. Ren, and Z. Wang

ABD-Net: Attentive but Diverse Person Re-Identification

IEEE International Conference on Computer Vision (ICCV)

O. Kupyn, T. Martyniuk, J. Wu*, and Z. Wang

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

IEEE International Conference on Computer Vision (ICCV)

E. Ryu, J. Liu, S. Wang*, X. Chen*, Z. Wang, and W. Yin

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

International Conference on Machine Learning (ICML)

W. Chen*, Z. Jiang*, Z. Wang, K. Cui, and X. Qian

Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-high Resolution Images

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Oral]

S. Li, I. B. Araujo*, W. Ren, Z. Wang, E. K. Tokuda*, R. Hirata, R. Cesar, J. Zhang, X. Guo, and X. Cao

Single Image Deraining: A Comprehensive Benchmark Analysis

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

J. Liu, X. Chen*, Z. Wang, and W. Yin

ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA

International Conference on Learning Representations (ICLR)

M. Karimi, D. Wu, Z. Wang and Y. Shen

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

Oxford Bioinformatics

B. Li*, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang

Benchmarking Single Image Dehazing and Beyond

IEEE Transactions on Image Processing (TIP)

X. Chen*, J. Liu, Z. Wang, and W. Yin

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds

Advances in Neural Information Processing Systems (NeurIPS) [Spotlight]

N. Bansal*, X. Chen*, and Z. Wang

Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

Advances in Neural Information Processing Systems (NeurIPS)

Z. Wu*, Z. Wang, Z. Wang, and H. Jin

Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

European Conference on Computer Vision (ECCV)

M. Sun, I. Baytas, L. Zhan, Z. Wang, and J. Zhou

Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases

ACM Conference on Knowledge Discovery and Data Mining (KDD)

J. Wu*, Y. Wang*, Z. Wu*, Z. Wang, A. Veeraraghavan, and Y. Lin

Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions

International Conference on Machine Learning (ICML)