Zhihua Zhang
70 papers · 2008–2026 · 14 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (24) π Interdisciplinary Bridge π Renaissance Researcher (5) π£ Hot Topic Early Bird
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Interdisciplinary Bridge
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Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(24)
π€
Dynamic Duo
(12)
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Grand Slam
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Deep Specialist
(15)
π
Keyword Champion
(3)
β
The Questioner
(2)
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Conference Pioneer
β‘
Prolific Year
(6)
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Unstoppable
(10)
ποΈ
Keyword Collector
(79)
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Trend Setter
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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
Keywords
markov chain monte carlo
(6)
federated learning
(5)
bayesian inference
(5)
convergence analysis
(4)
communication efficiency
(4)
convex optimization
(4)
nystrom method
(4)
matrix approximation
(4)
reinforcement learning
(4)
cur matrix decomposition
(4)
neural machine translation
(3)
non-autoregressive translation
(3)
kullback-leibler divergence
(3)
markov decision process
(3)
adversarial robustness
(3)
machine translation
(2)
masked language model
(2)
sparse learning
(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