261 results on '"Wotao Yin"'
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2. Solving General Natural-Language-Description Optimization Problems with Large Language Models.
3. Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation.
4. Rethinking the Capacity of Graph Neural Networks for Branching Strategy.
5. Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs.
6. Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.
7. Bagging Improves Generalization Exponentially.
8. Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares.
9. Learning to optimize: A tutorial for continuous and mixed-integer optimization.
10. ODE-based Learning to Optimize.
11. Alternating Projected SGD for Equality-constrained Bilevel Optimization.
12. HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent.
13. Towards Constituting Mathematical Structures for Learning to Optimize.
14. DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm.
15. Safeguarded Learned Convex Optimization.
16. On Representing Linear Programs by Graph Neural Networks.
17. On Representing Mixed-Integer Linear Programs by Graph Neural Networks.
18. A Single-Timescale Method for Stochastic Bilevel Optimization.
19. JFB: Jacobian-Free Backpropagation for Implicit Networks.
20. Attentional-Biased Stochastic Gradient Descent.
21. DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm.
22. MindOpt Tuner: Boost the Performance of Numerical Software by Automatic Parameter Tuning.
23. DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee.
24. MindOpt Adapter for CPLEX Benchmarking Performance Analysis.
25. Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression.
26. A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability.
27. Towards Constituting Mathematical Structures for Learning to Optimize.
28. Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling.
29. Scaled relative graphs: nonexpansive operators via 2D Euclidean geometry.
30. On the geometric analysis of a quartic-quadratic optimization problem under a spherical constraint.
31. Learning to Optimize: A Primer and A Benchmark.
32. Wasserstein-Based Projections with Applications to Inverse Problems.
33. DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training.
34. Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems.
35. Hyperparameter Tuning is All You Need for LISTA.
36. Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection.
37. Exponential Graph is Provably Efficient for Decentralized Deep Training.
38. An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders.
39. CADA: Communication-Adaptive Distributed Adam.
40. AutoBandit: A Meta Bandit Online Learning System.
41. An Optimal Stochastic Compositional Optimization Method with Applications to Meta Learning.
42. A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization.
43. Accelerating Gossip SGD with Periodic Global Averaging.
44. Provably Correct Optimization and Exploration with Non-linear Policies.
45. Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression.
46. Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate.
47. FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting.
48. AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity.
49. Multilevel Optimal Transport: A Fast Approximation of Wasserstein-1 Distances.
50. Communication-Adaptive Stochastic Gradient Methods for Distributed Learning.
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