Tuo Zhao

OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment ACL 2026 DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment EMNLP 2025 Deep Reinforcement Learning from Hierarchical Preference Design ICML 2025 Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data ICML 2025 RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization ACL 2025 Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult JMLR 2025 Data Diversity Matters for Robust Instruction Tuning EMNLP 2024 BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering EMNLP 2024 RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning EMNLP 2024 Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds JMLR 2024 Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces JMLR 2024 Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process ICML 2024 To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO ICML 2024 Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs ICLR 2024 LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models ICLR 2024 Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks NIPS 2024 Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks NIPS 2024 Adaptive Preference Scaling for Reinforcement Learning with Human Feedback NIPS 2024 Robust Reinforcement Learning from Corrupted Human Feedback NIPS 2024 Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks ICLR 2023 LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation ICML 2023 Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms NIPS 2023 Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms NIPS 2023 Module-wise Adaptive Distillation for Multimodality Foundation Models NIPS 2023 Less is More: Task-aware Layer-wise Distillation for Language Model Compression ICML 2023 Context-Aware Query Rewriting for Improving Users’ Search Experience on E-commerce Websites ACL 2023 HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference EMNLP 2023 Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories ICML 2023 Machine Learning Force Fields with Data Cost Aware Training ICML 2023 Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning ICLR 2023 Pivotal Estimation of Linear Discriminant Analysis in High Dimensions JMLR 2023 Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data ICML 2023 SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process ICML 2023 HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers ICLR 2023 Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer EMNLP 2023 Reinforcement Learning for Adaptive Mesh Refinement AISTATS 2023 PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance ICML 2022 Steering vector correction in MVDR beamformer for speech enhancement INTERSPEECH 2022 Self-Training with Differentiable Teacher NAACL 2022 MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation NAACL 2022 On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds NIPS 2022 CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing ACL 2022 CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data NAACL 2022 Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably AISTATS 2022 Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect ICLR 2022 No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models ICLR 2022 Taming Sparsely Activated Transformer with Stochastic Experts ICLR 2022 Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits ICLR 2022 Adversarially Regularized Policy Learning Guided by Trajectory Optimization L4DC 2022 Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint ICML 2022 Learning to Defend by Learning to Attack AISTATS 2021 A Hypergradient Approach to Robust Regression without Correspondence ICLR 2021 Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks ICML 2021 How Important is the Train-Validation Split in Meta-Learning? ICML 2021 Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL NIPS 2021 Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization IJCNLP 2021 Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach NAACL 2021 Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach EMNLP 2021 Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach EMNLP 2021 Token-wise Curriculum Learning for Neural Machine Translation EMNLP 2021 ARCH: Efficient Adversarial Regularized Training with Caching EMNLP 2021 Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization AISTATS 2021 Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data IJCNLP 2021 Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization ACL 2021 Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data ACL 2021 On Generalization Bounds of a Family of Recurrent Neural Networks AISTATS 2020 SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization ACL 2020 Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing ACL 2020 Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data EMNLP 2020 On Computation and Generalization of Generative Adversarial Imitation Learning ICLR 2020 Implicit Bias of Gradient Descent based Adversarial Training on Separable Data ICLR 2020 Towards Understanding Hierarchical Learning: Benefits of Neural Representations NIPS 2020 Differentiable Top-k with Optimal Transport NIPS 2020 Deep Reinforcement Learning with Robust and Smooth Policy ICML 2020 Transformer Hawkes Process ICML 2020 Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective NIPS 2020 On Computation and Generalization of Generative Adversarial Networks under Spectrum Control ICLR 2019 Meta Learning with Relational Information for Short Sequences NIPS 2019 Towards Understanding the Importance of Shortcut Connections in Residual Networks NIPS 2019 Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds NIPS 2019 On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition AISTATS 2019 On Scalable and Efficient Computation of Large Scale Optimal Transport ICML 2019 Toward Understanding the Importance of Noise in Training Neural Networks ICML 2019 Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python JMLR 2019 On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function UAI 2019 Online Factorization and Partition of Complex Networks by Random Walk UAI 2019 The Physical Systems Behind Optimization Algorithms NIPS 2018 Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization NIPS 2018 Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization NIPS 2018 Provable Gaussian Embedding with One Observation NIPS 2018 On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization JMLR 2018 On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning NIPS 2017 Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability ICML 2017 Deep Hyperspherical Learning NIPS 2017 The Opensesame NIST 2016 Speaker Recognition Evaluation System INTERSPEECH 2017 Parametric Simplex Method for Sparse Learning NIPS 2017 An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization AISTATS 2016 NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization NIPS 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016 The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R JMLR 2015 A Nonconvex Optimization Framework for Low Rank Matrix Estimation NIPS 2015 Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery JMLR 2015 Multivariate Regression with Calibration NIPS 2014 Accelerated Mini-batch Randomized Block Coordinate Descent Method NIPS 2014 CODA: High Dimensional Copula Discriminant Analysis JMLR 2013 Sparse Inverse Covariance Estimation with Calibration NIPS 2013 The huge Package for High-dimensional Undirected Graph Estimation in R JMLR 2012 Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation NIPS 2012 Sparse Additive Machine AISTATS 2012