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Yaodong Yu

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

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+10 more ↓ πŸ—ΊοΈ Taxonomy Completionist (54) πŸƒ Academic Marathon Β· Sprint 🌍 Conference Polyglot Β· Silver πŸŒ‰ Interdisciplinary Bridge Β· Bronze 🐣 Hot Topic Early Bird
πŸ”¬ Deep Specialist (10) 🀝 Dynamic Duo (16) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion (2) 🧭 Keyword Pioneer πŸ—ƒοΈ Keyword Collector (146) πŸ’Ž Century Club Β· Bronze ❓ The Questioner ⚑ Prolific Year Β· Productive

Venues

ICML (11) NeurIPS (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)

Research topics

Machine Learning (26) Deep Learning (16) Optimization & Theory Β· Machine Learning (15) Architectures (12) Core Methods (11) Artificial Intelligence (7) Learning Types Β· Machine Learning (7) Application Areas (6) Mathematics & Optimization (5) Core AI (5) Optimization (5) Optimization & Theory Β· Deep Learning (4) Computer Vision (3) Learning Types Β· Deep Learning (3) Models (3)

Keywords

representation learning (7) vision transformer (4) non-convex optimization (3) uncertainty quantification (3) adversarial example (3) token compression (2) token representation (2) neural network (2) federated learning (2) data heterogeneity (2) differential privacy (2) discriminative representation (2) sparse rate reduction (2) stochastic gradient descent (2) gradient descent (2) feature learning (2) transformer architecture (2) distribution shift (2) masked autoencoder (2) coding rate reduction (2)

Papers

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