Yaoliang Yu

Demystifying Foreground-Background Memorization in Diffusion Models AAAI 2026 Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization ICLR 2025 A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD ICML 2025 Diffusion Models under Group Transformations AISTATS 2025 Last-iterate Convergence in Regularized Graphon Mean Field Game AAAI 2025 Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets ICML 2025 Disguised Copyright Infringement of Latent Diffusion Models ICML 2024 Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning ICML 2024 One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently NIPS 2024 Faster Approximation of Probabilistic and Distributional Values via Least Squares ICLR 2024 Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games AISTATS 2024 Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers ACL 2023 Batchnorm Allows Unsupervised Radial Attacks NIPS 2023 Functional Renyi Differential Privacy for Generative Modeling NIPS 2023 Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers NIPS 2023 Robust Data Valuation with Weighted Banzhaf Values NIPS 2023 Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality ICLR 2023 Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks ICML 2023 Revisiting flow generative models for Out-of-distribution detection ICLR 2022 OLALA: Object-Level Active Learning for Efficient Document Layout Annotation EMNLP 2022 Optimality and Stability in Non-Convex Smooth Games JMLR 2022 Demystifying and Generalizing BinaryConnect NIPS 2021 BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression EACL 2021 The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing IJCNLP 2021 Quantifying and Improving Transferability in Domain Generalization NIPS 2021 S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks NIPS 2021 Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training NIPS 2021 The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing ACL 2021 Posterior Differential Regularization with f-divergence for Improving Model Robustness NAACL 2021 Unsupervised Multilingual Alignment using Wasserstein Barycenter IJCAI 2020 DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference ACL 2020 Showing Your Work Doesn’t Always Work ACL 2020 Early Exiting BERT for Efficient Document Ranking EMNLP 2020 Stronger and Faster Wasserstein Adversarial Attacks ICML 2020 Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space ICML 2020 On Minimax Optimality of GANs for Robust Mean Estimation AISTATS 2020 Tails of Lipschitz Triangular Flows ICML 2020 Convergence of Gradient Methods on Bilinear Zero-Sum Games ICLR 2020 Least Squares Estimation of Weakly Convex Functions AISTATS 2019 What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons EMNLP 2019 Sum-of-Squares Polynomial Flow ICML 2019 Distributional Reinforcement Learning for Efficient Exploration ICML 2019 What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons IJCNLP 2019 Multivariate Triangular Quantile Maps for Novelty Detection NIPS 2019 Inductive Two-Layer Modeling with Parametric Bregman Transfer ICML 2018 Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters JMLR 2018 Deep Homogeneous Mixture Models: Representation, Separation, and Approximation NIPS 2018 Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction NIPS 2017 Learning Latent Space Models with Angular Constraints ICML 2017 Generalized Conditional Gradient for Sparse Estimation JMLR 2017 Efficient Multiple Instance Metric Learning Using Weakly Supervised Data CVPR 2017 On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System AISTATS 2016 Convex Two-Layer Modeling with Latent Structure NIPS 2016 Scalable and Sound Low-Rank Tensor Learning AISTATS 2016 Closed-Form Training of Mahalanobis Distance for Supervised Clustering CVPR 2016 Additive Approximations in High Dimensional Nonparametric Regression via the SALSA ICML 2016 Minimizing Nonconvex Non-Separable Functions AISTATS 2015 Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM ICML 2015 Efficient Structured Matrix Rank Minimization NIPS 2014 On Decomposing the Proximal Map NIPS 2013 Characterizing the Representer Theorem ICML 2013 Better Approximation and Faster Algorithm Using the Proximal Average NIPS 2013 Polar Operators for Structured Sparse Estimation NIPS 2013 Accelerated Training for Matrix-norm Regularization: A Boosting Approach NIPS 2012 A Polynomial-time Form of Robust Regression NIPS 2012 Convex Multi-view Subspace Learning NIPS 2012 Relaxed Clipping: A Global Training Method for Robust Regression and Classification NIPS 2010 A General Projection Property for Distribution Families NIPS 2009