← Authors

Wotao Yin

49 papers · 2010–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (11) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) πŸ—ΊοΈ Taxonomy Completionist (55) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (26) πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (174) πŸ“ˆ Trend Setter ⚑ Prolific Year (12) πŸ”₯ Unstoppable (9) πŸ’Ž Century Club (49)

Conferences

NIPS (18) ICML (11) AISTATS (5) ICLR (5) JMLR (3) AAAI (2) EMNLP (2) ICCV (1) IJCAI (1) NAACL (1)

Top co-authors

Jialin Liu (12) Xiaohan Chen (10) Zhangyang Wang (9) Kun Yuan (7) Ziang Chen (6) Xinshang Wang (6) Tianyi Chen (5) Fei Feng (5) Yuejiao Sun (4) Yiming Chen (4)

Keywords

stochastic gradient descent (8) gradient descent (5) non-convex optimization (5) distributed optimization (5) convergence rate (5) communication efficiency (5) stochastic optimization (4) neural network optimization (4) sample complexity (4) reinforcement learning (3) variance reduction (3) distributed learning (3) bilevel optimization (3) sparse recovery (2) block coordinate descent (2) nonconvex optimization (2) logistic regression (2) neural network training (2) decentralized optimization (2) hyperparameter optimization (2)

Papers

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs ICML 2025
Scale Down to Speed Up: Dynamic Data Selection for Reinforcement Learning EMNLP 2025
Rethinking the Capacity of Graph Neural Networks for Branching Strategy NIPS 2024
Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares ICML 2024
Efficient Algorithms for Sum-Of-Minimum Optimization ICML 2024
Solving General Natural-Language-Description Optimization Problems with Large Language Models NAACL 2024
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ICML 2024
BC-Prover: Backward Chaining Prover for Formal Theorem Proving EMNLP 2024
On Representing Linear Programs by Graph Neural Networks ICLR 2023
On Representing Mixed-Integer Linear Programs by Graph Neural Networks ICLR 2023
Towards Constituting Mathematical Structures for Learning to Optimize ICML 2023
Safeguarded Learned Convex Optimization AAAI 2023
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent AISTATS 2023
Alternating Projected SGD for Equality-constrained Bilevel Optimization AISTATS 2023
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm ICML 2023
JFB: Jacobian-Free Backpropagation for Implicit Networks AAAI 2022
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate NIPS 2022
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting NIPS 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression NIPS 2022
A Single-Timescale Method for Stochastic Bilevel Optimization AISTATS 2022
Learning to Optimize: A Primer and A Benchmark JMLR 2022
CADA: Communication-Adaptive Distributed Adam AISTATS 2021
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems NIPS 2021
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection NIPS 2021
Exponential Graph is Provably Efficient for Decentralized Deep Training NIPS 2021
Hyperparameter Tuning is All You Need for LISTA NIPS 2021
DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training ICCV 2021
Learning A Minimax Optimizer: A Pilot Study ICLR 2021
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders NIPS 2021
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization ICML 2021
Accelerating Gossip SGD with Periodic Global Averaging ICML 2021
Provably Correct Optimization and Exploration with Non-linear Policies ICML 2021
AutoBandit: A Meta Bandit Online Learning System IJCAI 2021
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity AISTATS 2020
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods NIPS 2020
An Improved Analysis of Stochastic Gradient Descent with Momentum NIPS 2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning NIPS 2020
Acceleration of SVRG and Katyusha X by Inexact Preconditioning ICML 2019
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers ICML 2019
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning JMLR 2019
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA ICLR 2019
A2BCD: Asynchronous Acceleration with Optimal Complexity ICLR 2019
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NIPS 2018
Breaking the Span Assumption Yields Fast Finite-Sum Minimization NIPS 2018
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds NIPS 2018
On Markov Chain Gradient Descent NIPS 2018
Straggler Mitigation in Distributed Optimization Through Data Encoding NIPS 2017
Asynchronous Coordinate Descent under More Realistic Assumptions NIPS 2017
A Fast Hybrid Algorithm for Large-Scale -Regularized Logistic Regression JMLR 2010