Yaodong Yu

30 papers · 2018–2025 · 8 conferences · across top CS/AI conferences

Achievements

+11 more ↓

🧭 Keyword Pioneer 🌍 Conference Polyglot (8) 🗺️ Taxonomy Completionist (12) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (7)

🗺️ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (16) 👑 Triple Crown 🧬 Topic Evolution 🏆 Keyword Champion (2) ❓ The Questioner 🗃️ Keyword Collector (111) 💎 Century Club (30) ⚡ Prolific Year (9)

Conferences

ICML (11) NIPS (8) AISTATS (4) ICLR (2) JMLR (2) CVPR (1) EMNLP (1) NAACL (1)

Top co-authors

Yi Ma (16) Druv Pai (7) Michael Jordan (5) Ziyang Wu (4) Cihang Xie (3) Chong You (3) Sam Buchanan (3) Yuyin Zhou (3) Quanquan Gu (3) Shengbang Tong (2)

Keywords

representation learning (5) uncertainty quantification (3) adversarial example (3) gradient descent (2) nonconvex optimization (2) transformer architecture (2) sparse rate reduction (2) distribution shift (2) feature learning (2) discriminative representation (2) neural network (2) data heterogeneity (2) stochastic gradient descent (2) vision transformer (2) token compression (2) federated learning (2) contrastive learning (1) neural tangent kernel (1) stochastic optimization (1) model compression (1)

Papers

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation AISTATS 2025 Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More ICML 2025 Attention-Only Transformers via Unrolled Subspace Denoising ICML 2025 Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction ICLR 2025 Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency CVPR 2025 A Study on the Calibration of In-context Learning NAACL 2024 Scaling White-Box Transformers for Vision NIPS 2024 Masked Completion via Structured Diffusion with White-Box Transformers ICLR 2024 Differentially Private Representation Learning via Image Captioning ICML 2024 A Global Geometric Analysis of Maximal Coding Rate Reduction ICML 2024 ViP: A Differentially Private Foundation Model for Computer Vision ICML 2024 White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? JMLR 2024 White-Box Transformers via Sparse Rate Reduction NIPS 2023 Federated Conformal Predictors for Distributed Uncertainty Quantification ICML 2023 What You See is What You Get: Principled Deep Learning via Distributional Generalization NIPS 2022 TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels NIPS 2022 Conditional Supervised Contrastive Learning for Fair Text Classification EMNLP 2022 Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ICML 2022 Predicting Out-of-Distribution Error with the Projection Norm ICML 2022 ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction JMLR 2022 Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization AISTATS 2022 On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging AISTATS 2022 Robust Calibration with Multi-domain Temperature Scaling NIPS 2022 Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction NIPS 2020 Rethinking Bias-Variance Trade-off for Generalization of Neural Networks ICML 2020 Boundary thickness and robustness in learning models NIPS 2020 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Theoretically Principled Trade-off between Robustness and Accuracy ICML 2019 Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima NIPS 2018 A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery ICML 2018