← Authors

Zhihua Zhang

70 papers · 2008–2026 · 14 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (17) 🌍 Conference Polyglot (14) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (24) 🀝 Dynamic Duo (12) πŸ† Grand Slam πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion (3) ❓ The Questioner (2) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ”₯ Unstoppable (10) πŸ—ƒοΈ Keyword Collector (79) πŸ“ˆ Trend Setter πŸ’Ž Century Club (69)

Conferences

NIPS (18) JMLR (14) AISTATS (8) ICML (8) SEMEVAL (7) IJCAI (4) AAAI (2) ACL (2) EMNLP (2) COLING (1) COLT (1) EACL (1) ICLR (1) IJCNLP (1)

Top co-authors

Shusen Wang (12) Xiang Li (9) Michael I. Jordan (8) Luo Luo (8) Man Lan (7) Haishan Ye (6) Dachao Lin (6) Wenhao Yang (6) Guang Dai (6) Yuze Han (5)

Keywords

markov chain monte carlo (6) federated learning (5) bayesian inference (5) nystrom method (4) communication efficiency (4) convex optimization (4) matrix approximation (4) convergence analysis (4) reinforcement learning (4) cur matrix decomposition (4) neural machine translation (3) non-autoregressive translation (3) markov decision process (3) kullback-leibler divergence (3) adversarial robustness (3) masked language model (2) machine translation (2) sample complexity (2) stochastic optimization (2) model selection (2)

Papers

CAST-LUT: Tokenizer-Guided HSV Look-Up Tables for Purple Flare Removal AAAI 2026
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes JMLR 2025
Statistical Efficiency of Distributional Temporal Difference Learning NIPS 2024
A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness JMLR 2024
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction JMLR 2024
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes ICML 2023
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning AISTATS 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis NIPS 2023
Entropy-based Training Methods for Scalable Neural Implicit Samplers NIPS 2023
Enhancing Adversarial Robustness via Score-Based Optimization NIPS 2023
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models NIPS 2023
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation ACL 2022
Federated Reinforcement Learning with Environment Heterogeneity AISTATS 2022
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods JMLR 2022
On Non-local Convergence Analysis of Deep Linear Networks ICML 2022
A Statistical Online Inference Approach in Averaged Stochastic Approximation NIPS 2022
Semi-infinitely Constrained Markov Decision Processes NIPS 2022
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness NIPS 2022
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective NIPS 2022
Con-NAT: Contrastive Non-autoregressive Neural Machine Translation EMNLP 2022
Statistical Estimation and Online Inference via Local SGD COLT 2022
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence NIPS 2021
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data NIPS 2021
Memory-Efficient Differentiable Transformer Architecture Search ACL 2021
Multi-split Reversible Transformers Can Enhance Neural Machine Translation EACL 2021
Communication-Efficient Distributed SVD via Local Power Iterations ICML 2021
Memory-Efficient Differentiable Transformer Architecture Search IJCNLP 2021
Approximate Newton Methods JMLR 2021
Nesterov's Acceleration for Approximate Newton JMLR 2020
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems ICML 2020
Efficient Spectrum-Revealing CUR Matrix Decomposition AISTATS 2020
Train Once, and Decode As You Like COLING 2020
Active Learning Approaches to Enhancing Neural Machine Translation EMNLP 2020
Do Subsampled Newton Methods Work for High-Dimensional Data? AAAI 2020
On the Convergence of FedAvg on Non-IID Data ICLR 2020
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning NIPS 2019
Robust Frequent Directions with Application in Online Learning JMLR 2019
Lipschitz Generative Adversarial Nets ICML 2019
CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC AISTATS 2017
Approximate Newton Methods and Their Local Convergence ICML 2017
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition JMLR 2016
ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews SEMEVAL 2016
Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA IJCAI 2016
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking SEMEVAL 2016
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter SEMEVAL 2016
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions JMLR 2016
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets SEMEVAL 2016
ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features SEMEVAL 2015
Support Matrix Machines ICML 2015
ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews SEMEVAL 2015
A Scalable Community Detection Algorithm for Large Graphs Using Stochastic Block Models IJCAI 2015
ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification SEMEVAL 2014
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis NIPS 2014
Efficient Algorithms and Error Analysis for the Modified Nystrom Method AISTATS 2014
Making Fisher Discriminant Analysis Scalable ICML 2014
Nonconvex Relaxation Approaches to Robust Matrix Recovery IJCAI 2013
A Scalable Approach to Column-Based Low-Rank Matrix Approximation IJCAI 2013
Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling JMLR 2013
EP-GIG Priors and Applications in Bayesian Sparse Learning JMLR 2012
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound NIPS 2012
Nonconvex Penalization Using Laplace Exponents and Concave Conjugates NIPS 2012
An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling AISTATS 2012
Coherence Functions with Applications in Large-Margin Classification Methods JMLR 2012
Bayesian Generalized Kernel Mixed Models JMLR 2011
Regularized Discriminant Analysis, Ridge Regression and Beyond JMLR 2010
Matrix-Variate Dirichlet Process Mixture Models AISTATS 2010
Bayesian Generalized Kernel Models AISTATS 2010
Probabilistic Relational PCA NIPS 2009
Optimal Scoring for Unsupervised Learning NIPS 2009
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice NIPS 2008