Hello! I am a postdoctoral researcher at MIT CSAIL working with Stefanie Jegelka. I obtained my PhD in Applied Mathematics from Peking University (PKU) in 2023, advised by Yisen Wang, Jiansheng Yang, and Zhouchen Lin. Prior to that, I received a BS in mathematics and a BA in philosophy from PKU. I am a recipient of Best ML Paper Award of ECML-PKDD 2021 and Silver Best Paper Award of ICML 2021 AdvML workshop.
I am generally interested in developing theoretical understandings and principled designs for machine learning algorithms. Now I mainly work in the areas of unsupervised learning (representation & generation), robust learning (adversarial & OOD robustness), and graph learning (GNNs & Transformers). Feel free to shoot me an email if you are interested in working with me.
Email: yifei_w at mit.edu / Google Scholar / Github / X (Twitter)
News
- 2023.12. I have joined MIT CSAIL as a postdoc.
- 2023.09. Five papers were accepted by NeurIPS 2023! Paper and code are released. See you in New Orleans! π₯
- 2023.09. I will be serving as an area chair for ICLR 2024.
- 2023.05. Two SSL theory papers were accepted by ICML 2023, which analyze the roles of multi-modal supervision (e.g., image-text pairs in CLIP) and weak supervision (e.g., noisy labels) in self-supervised learning.
- 2023.02. One paper on fairness in adversarial training was accepted by CVPR 2023.
- One paper on equilibrium-seeking image denoisor was accepted by IEEE Transaction on Image Processing (TIP).
- Five papers about contrastive learning and graph learning were accepted by ICLR 2023, covering training dynamics, dimensional collapse, dynamic augmentation, CL-inspired normalization, and unbiased graph sampling.
Publications (* marks equal contribution)
- Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NeurIPS 2023 2023 PDF | Code
- Identifiable Contrastive Learning with Automatic Feature Importance Discovery NeurIPS 2023 2023 PDF | Code
- Adversarial Examples Are Not Real Features NeurIPS 2023 2023 PDF | Code
- Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective NeurIPS 2023 2023 PDF | Code
- Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding NeurIPS 2023 2023 PDF | Code
- On the Generalization of Multi-modal Contrastive Learning ICML 2023 2023 PDF | Code
- Rethinking Weak Supervision in Helping Contrastive Representation Learning ICML 2023 2023 PDF
- CFA: Class-wise Calibrated Fair Adversarial Training CVPR 2023 2023 PDF | Code
- Equilibrium Image Denoising with Implicit Differentiation IEEE Transactions on Image Processing (TIP) 2023 PDF
- A Message Passing Perspective on Learning Dynamics of Contrastive Learning ICLR 2023 2023 PDF | Code | Slides | Blog
- Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism ICLR 2023 2023 PDF | Code
- Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning ICLR 2023 2023 PDF | Code
- ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond ICLR 2023 2023 PDF | Code
- Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States ICLR 2023 2023 PDF
- What Contrastive Learning Learns Beyond Class-wise Features? ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) 2023 PDF
- Rethinking the Necessity of Labels in Backdoor Defense ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (BANDS) 2023 PDF
- On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization AAAI 2023 (Oral) 2023 PDF
- How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code | Slides
- Improving Out-of-distribution Robustness by Adversarial Training with Structured Priors NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code | Slides
- When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code
- Variational Energy-Based Models: A Probabilistic Framework for Contrastive Self-Supervised Learning NeurIPS 2022 SSL Workshop 2022 PDF
- AggNCE: Asymptotically Identifiable Contrastive Learning NeurIPS 2022 SSL Workshop (Oral) 2022 PDF
- Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium IEEE BigData 2022 (Long Talk) 2022 PDF
- Optimization-Induced Graph Implicit Nonlinear Diffusion ICML 2022 2022 PDF | Code
- G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters ICML 2022 2022 PDF
- Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap ICLR 2022 2022 PDF | Code | Slides
- A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training ICLR 2022 (π Silver Best Paper Award @ ICML 2021 AdvML workshop) 2022 PDF | Slides | Award
- Residual Relaxation for Multi-view Representation Learning NeurIPS 2021 2021 PDF | Slides | Blog
- Dissecting the Diffusion Process in Linear Graph Convolutional Networks NeurIPS 2021 2021 PDF | Code | Slides | Blog
- Reparameterized Sampling for Generative Adversarial Networks ECML-PKDD 2021 2021 (π Best ML Paper Award (1/685) & Invited to Machine Learning Journal) PDF | Code | Slides | Media | Talk | Award
- Train Once, and Decode as You Like COLING 2020 2020 PDF
- Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NeurIPS 2023 2023 PDF | Code
- Identifiable Contrastive Learning with Automatic Feature Importance Discovery NeurIPS 2023 2023 PDF | Code
- On the Generalization of Multi-modal Contrastive Learning ICML 2023 2023 PDF | Code
- Rethinking Weak Supervision in Helping Contrastive Representation Learning ICML 2023 2023 PDF
- A Message Passing Perspective on Learning Dynamics of Contrastive Learning ICLR 2023 2023 PDF | Code | Slides | Blog
- Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism ICLR 2023 2023 PDF | Code
- Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning ICLR 2023 2023 PDF | Code
- ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond ICLR 2023 2023 PDF | Code
- What Contrastive Learning Learns Beyond Class-wise Features? ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) 2023 PDF
- Rethinking the Necessity of Labels in Backdoor Defense ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (BANDS) 2023 PDF
- How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code | Slides
- Variational Energy-Based Models: A Probabilistic Framework for Contrastive Self-Supervised Learning NeurIPS 2022 SSL Workshop 2022 PDF
- AggNCE: Asymptotically Identifiable Contrastive Learning NeurIPS 2022 SSL Workshop (Oral) 2022 PDF
- Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap ICLR 2022 2022 PDF | Code | Slides
- A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training ICLR 2022 (π Silver Best Paper Award @ ICML 2021 AdvML workshop) 2022 PDF | Slides | Award
- Residual Relaxation for Multi-view Representation Learning NeurIPS 2021 2021 PDF | Slides | Blog
- Reparameterized Sampling for Generative Adversarial Networks ECML-PKDD 2021 2021 (π Best ML Paper Award (1/685). Invited to Machine Learning Journal) PDF | Code | Slides | Media | Talk | Award
- Adversarial Examples Are Not Real Features NeurIPS 2023 2023 PDF | Code
- Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective NeurIPS 2023 2023 PDF | Code
- CFA: Class-wise Calibrated Fair Adversarial Training CVPR 2023 2023 PDF | Code
- Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning ICLR 2023 2023 PDF
- Rethinking the Necessity of Labels in Backdoor Defense ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (BANDS) 2023 PDF
- On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization AAAI 2023 (Oral) 2023 PDF
- Improving Out-of-distribution Robustness by Adversarial Training with Structured Priors NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code | Slides
- When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture NeurIPS 2022 (Spotlight, Top 5%) 2022 PDF | Code
- A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training ICLR 2022 (π Silver Best Paper Award @ ICML 2021 AdvML workshop) 2022 PDF | Slides | Award
- Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding NeurIPS 2023 2023 PDF | Code
- Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NeurIPS 2023 2023 PDF | Code
- ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond ICLR 2023 2023 PDF
- Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States ICLR 2023 2023 PDF
- Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium IEEE BigData 2022 (Long Talk) 2022 PDF
- Optimization-Induced Graph Implicit Nonlinear Diffusion ICML 2022 2022 PDF | Code
- G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters ICML 2022 2022 PDF
- Dissecting the Diffusion Process in Linear Graph Convolutional Networks NeurIPS 2021 2021 PDF | Code | Slides | Blog
Selected Awards
Beijing Excellent Graduate Award, 2023
Excellent Graduate Award, Peking University, 2023
National Scholarship, 2021, 2022
Principal Scholarship, Peking University, 2022
Best ML Paper Award (1/685), ECML-PKDD, 2021
Silver Best Paper Award, ICML AdvML workshop, 2021
Excellent Graduate Award, Peking University, 2023
National Scholarship, 2021, 2022
Principal Scholarship, Peking University, 2022
Best ML Paper Award (1/685), ECML-PKDD, 2021
Silver Best Paper Award, ICML AdvML workshop, 2021
Professional Services
Reviewer and/or program commitee member:
- ML Conferences: NeurIPS (2022, 2023), ICML (2022), AISTATS (2024), LoG (2023), ECML-PKDD (2022)
- Other conferences: CVPR (2023, 2024), ICCV (2023), ACL (2020, 2021)
- Journal: IEEE TPAMI, TMLR
- ICLR (2024)