← Authors

Yisen Wang

91 papers · 2016–2025 · 12 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (12) πŸ—ΊοΈ Taxonomy Completionist (20) 🐝 Cross-Pollinator (6) πŸƒ Academic Marathon (9) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (27) 🀝 Dynamic Duo (39) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (17) πŸ’Ž Century Club (91) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (10) ⚑ Prolific Year (12) πŸ—ƒοΈ Keyword Collector (50) ❓ The Questioner (5)

Conferences

ICLR (27) NIPS (27) ICML (19) CVPR (6) ICCV (3) IJCAI (3) AAAI (1) ACML (1) ECCV (1) EMNLP (1) IJCNLP (1) JMLR (1)

Top co-authors

Yifei Wang (39) Zhouchen Lin (22) Xingjun Ma (12) James Bailey (11) Shu-Tao Xia (10) Qi Zhang (10) Mingjie Li (8) Jiansheng Yang (7) Tianqi Du (6) Jingyi Cui (5)

Research topics

Optimization & Theory (1)

Keywords

adversarial training (11) contrastive learning (8) adversarial robustness (8) deep neural network (7) neural network (6) self-supervised learning (6) adversarial example (5) representation learning (5) noisy label (5) out-of-distribution generalization (4) image classification (4) graph neural network (3) node classification (3) neuromorphic computing (3) spiking neural network (3) adversarial perturbation (3) neural network robustness (3) domain generalization (3) visual representation (2) dialogue generation (2)

Papers

TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice ICLR 2025
What is Wrong with Perplexity for Long-context Language Modeling? ICLR 2025
Rethinking Invariance in In-context Learning ICLR 2025
An Augmentation Overlap Theory of Contrastive Learning JMLR 2025
Identifying and Understanding Cross-Class Features in Adversarial Training ICML 2025
Incorporating Arbitrary Matrix Group Equivariance into KANs ICML 2025
Long-Short Alignment for Effective Long-Context Modeling in LLMs ICML 2025
An Augmentation-Aware Theory for Self-Supervised Contrastive Learning ICML 2025
Projection Head is Secretly an Information Bottleneck ICLR 2025
Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD ICLR 2025
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness ICLR 2025
SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation ICLR 2025
Can In-context Learning Really Generalize to Out-of-distribution Tasks? ICLR 2025
Dissecting the Failure of Invariant Learning on Graphs NIPS 2024
Understanding the Role of Equivariance in Self-supervised Learning NIPS 2024
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors ICML 2024
Graph Neural Networks (with Proper Weights) Can Escape Oversmoothing ACML 2024
Non-negative Contrastive Learning ICLR 2024
Do Generated Data Always Help Contrastive Learning? ICLR 2024
On the Role of Discrete Tokenization in Visual Representation Learning ICLR 2024
PID: Prompt-Independent Data Protection Against Latent Diffusion Models ICML 2024
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining ICML 2024
A Canonicalization Perspective on Invariant and Equivariant Learning NIPS 2024
Fight Back Against Jailbreaking via Prompt Adversarial Tuning NIPS 2024
A Theoretical Understanding of Self-Correction through In-context Alignment NIPS 2024
Generalist: Decoupling Natural and Robust Generalization CVPR 2023
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding NIPS 2023
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective NIPS 2023
Adversarial Examples Are Not Real Features NIPS 2023
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NIPS 2023
GEQ: Gaussian Kernel Inspired Equilibrium Models NIPS 2023
Identifiable Contrastive Learning with Automatic Feature Importance Discovery NIPS 2023
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization AAAI 2023
CFA: Class-Wise Calibrated Fair Adversarial Training CVPR 2023
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks ICCV 2023
A Message Passing Perspective on Learning Dynamics of Contrastive Learning ICLR 2023
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond ICLR 2023
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank DifferentialΒ Mechanism ICLR 2023
Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States ICLR 2023
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations ICLR 2023
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning ICLR 2023
Rethinking Weak Supervision in Helping Contrastive Learning ICML 2023
On the Generalization of Multi-modal Contrastive Learning ICML 2023
Certified Adversarial Robustness Under the Bounded Support Set ICML 2022
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation CVPR 2022
Optimization-Induced Graph Implicit Nonlinear Diffusion ICML 2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture NIPS 2022
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders NIPS 2022
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors NIPS 2022
Optimization inspired Multi-Branch Equilibrium Models ICLR 2022
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap ICLR 2022
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training ICLR 2022
Self-ensemble Adversarial Training for Improved Robustness ICLR 2022
CerDEQ: Certifiable Deep Equilibrium Model ICML 2022
G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters ICML 2022
A Unified Approach to Interpreting and Boosting Adversarial Transferability ICLR 2021
Unlearnable Examples: Making Personal Data Unexploitable ICLR 2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing ICLR 2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks NIPS 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks NIPS 2021
Efficient Equivariant Network NIPS 2021
Clustering Effect of Adversarial Robust Models NIPS 2021
Gauge Equivariant Transformer NIPS 2021
MoriΓ© Attack (MA): A New Potential Risk of Screen Photos NIPS 2021
On Training Implicit Models NIPS 2021
GBHT: Gradient Boosting Histogram Transform for Density Estimation ICML 2021
Leveraged Weighted Loss for Partial Label Learning ICML 2021
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? ICML 2021
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness NIPS 2021
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks NIPS 2021
Adversarial Neuron Pruning Purifies Backdoored Deep Models NIPS 2021
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State NIPS 2021
Residual Relaxation for Multi-view Representation Learning NIPS 2021
Adversarial Weight Perturbation Helps Robust Generalization NIPS 2020
Improving Adversarial Robustness Requires Revisiting Misclassified Examples ICLR 2020
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets ICLR 2020
Normalized Loss Functions for Deep Learning with Noisy Labels ICML 2020
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles CVPR 2020
Improving Query Efficiency of Black-box Adversarial Attack ECCV 2020
Symmetric Cross Entropy for Robust Learning With Noisy Labels ICCV 2019
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation IJCNLP 2019
Hilbert-Based Generative Defense for Adversarial Examples ICCV 2019
On the Convergence and Robustness of Adversarial Training ICML 2019
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation EMNLP 2019
Iterative Learning With Open-Set Noisy Labels CVPR 2018
Dimensionality-Driven Learning with Noisy Labels ICML 2018
Decoupled Networks CVPR 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality ICLR 2018
Robust Survey Aggregation with Student-t Distribution and Sparse Representation IJCAI 2017
Student-t Process Regression with Student-t Likelihood IJCAI 2017
Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness IJCAI 2016