b'Wotao Yin'

research

∙ 08/20/2023

A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability

This paper outlines a natural conversational approach to solving persona...

0 Ming Jin, et al. ∙

research

∙ 07/16/2023

MindOpt Tuner: Boost the Performance of Numerical Software by Automatic Parameter Tuning

Numerical software is usually shipped with built-in hyperparameters. By ...

0 Mengyuan Zhang, et al. ∙

research

∙ 06/01/2023

DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural ne...

0 Lisang Ding, et al. ∙

research

∙ 05/29/2023

Towards Constituting Mathematical Structures for Learning to Optimize

Learning to Optimize (L2O), a technique that utilizes machine learning t...

0 Jialin Liu, et al. ∙

research

∙ 05/12/2023

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression

Communication compression is an essential strategy for alleviating commu...

0 Yutong He, et al. ∙

research

∙ 11/15/2022

An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods

In this paper, we revisit and improve the convergence of policy gradient...

0 Yanli Liu, et al. ∙

research

∙ 11/14/2022

Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization

Stochastic bilevel optimization, which captures the inherent nested stru...

0 Quan Xiao, et al. ∙

research

∙ 10/19/2022

On Representing Mixed-Integer Linear Programs by Graph Neural Networks

While Mixed-integer linear programming (MILP) is NP-hard in general, pra...

0 Ziang Chen, et al. ∙

research

∙ 10/14/2022

Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learni...

0 Zhuoqing Song, et al. ∙

research

∙ 09/25/2022

On Representing Linear Programs by Graph Neural Networks

Learning to optimize is a rapidly growing area that aims to solve optimi...

14 Ziang Chen, et al. ∙

research

∙ 06/08/2022

Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression

Recent advances in distributed optimization and learning have shown that...

0 Xinmeng Huang, et al. ∙

research

∙ 11/08/2021

BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning

Decentralized algorithm is a form of computation that achieves a global ...

25 Bicheng Ying, et al. ∙

research

∙ 10/29/2021

Hyperparameter Tuning is All You Need for LISTA

Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces th...

11 Xiaohan Chen, et al. ∙

research

∙ 10/26/2021

Exponential Graph is Provably Efficient for Decentralized Deep Training

Decentralized SGD is an emerging training method for deep learning known...

7 Bicheng Ying, et al. ∙

research

∙ 10/11/2021

Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection

Robust principal component analysis (RPCA) is a critical tool in modern ...

15 HanQin Cai, et al. ∙

research

∙ 06/25/2021

Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems

Stochastic nested optimization, including stochastic compositional, min-...

2 Tianyi Chen, et al. ∙

research

∙ 06/02/2021

Learn to Predict Equilibria via Fixed Point Networks

Systems of interacting agents can often be modeled as contextual games, ...

14 Howard Heaton, et al. ∙

research

∙ 05/19/2021

Accelerating Gossip SGD with Periodic Global Averaging

Communication overhead hinders the scalability of large-scale distribute...

7 Yiming Chen, et al. ∙

research

∙ 04/29/2021

Feasibility-based Fixed Point Networks

Inverse problems consist of recovering a signal from a collection of noi...

21 Howard Heaton, et al. ∙

research

∙ 04/25/2021

On the Comparison between Cyclic Sampling and Random Reshuffling

When applying a stochastic/incremental algorithm, one must choose the or...

14 Xinmeng Huang, et al. ∙

research

∙ 04/24/2021

DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training

The scale of deep learning nowadays calls for efficient distributed trai...

20 Kun Yuan, et al. ∙

research

∙ 03/23/2021

Learning to Optimize: A Primer and A Benchmark

Learning to optimize (L2O) is an emerging approach that leverages machin...

61 Tianlong Chen, et al. ∙

research

∙ 03/23/2021

Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop

A growing trend in deep learning replaces fixed depth models by approxim...

12 Samy Wu Fung, et al. ∙

research

∙ 03/22/2021

Provably Correct Optimization and Exploration with Non-linear Policies

Policy optimization methods remain a powerful workhorse in empirical Rei...

1 Fei Feng, et al. ∙

research

∙ 02/21/2021

A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization

We consider the zeroth-order optimization problem in the huge-scale sett...

5 HanQin Cai, et al. ∙

research

∙ 02/09/2021

A Single-Timescale Stochastic Bilevel Optimization Method

Stochastic bilevel optimization generalizes the classic stochastic optim...

9 Tianyi Chen, et al. ∙

research

∙ 12/31/2020

CADA: Communication-Adaptive Distributed Adam

Stochastic gradient descent (SGD) has taken the stage as the primary wor...

15 Tianyi Chen, et al. ∙

research

∙ 12/22/2020

Hybrid Federated Learning: Algorithms and Implementation

Federated learning (FL) is a recently proposed distributed machine learn...

32 Xinwei Zhang, et al. ∙

research

∙ 10/06/2020

SCOBO: Sparsity-Aware Comparison Oracle Based Optimization

We study derivative-free optimization for convex functions where we furt...

6 HanQin Cai, et al. ∙

research

∙ 08/25/2020

Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization

Stochastic compositional optimization generalizes classic (non-compositi...

0 Tianyi Chen, et al. ∙

research

∙ 08/05/2020

Projecting to Manifolds via Unsupervised Learning

We present a new framework, called adversarial projections, for solving ...

5 Howard Heaton, et al. ∙

research

∙ 07/12/2020

VAFL: a Method of Vertical Asynchronous Federated Learning

Horizontal Federated learning (FL) handles multi-client data that share ...

7 Tianyi Chen, et al. ∙

research

∙ 05/22/2020

FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data

Federated Learning (FL) has become a popular paradigm for learning from ...

8 Xinwei Zhang, et al. ∙

research

∙ 03/29/2020

Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling

We consider the problem of minimizing a high-dimensional objective funct...

1 HanQin Cai, et al. ∙

research

∙ 03/15/2020

Provably Efficient Exploration for RL with Unsupervised Learning

We study how to use unsupervised learning for efficient exploration in r...

2 Fei Feng, et al. ∙

research

∙ 03/04/2020

Safeguarded Learned Convex Optimization

Many applications require repeatedly solving a certain type of optimizat...

0 Howard Heaton, et al. ∙

research

∙ 02/26/2020

LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning

This paper targets solving distributed machine learning problems such as...

7 Tianyi Chen, et al. ∙

research

∙ 12/28/2019

Scaled Relative Graph of Normal Matrices

The Scaled Relative Graph (SRG) by Ryu, Hannah, and Yin (arXiv:1902.0978...

0 Xinmeng Huang, et al. ∙

research

∙ 12/06/2019

Does Knowledge Transfer Always Help to Learn a Better Policy?

One of the key approaches to save samples when learning a policy for a r...

21 Fei Feng, et al. ∙

research

∙ 10/24/2019

XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training

We propose XPipe, an efficient asynchronous pipeline model parallelism a...

0 Lei Guan, et al. ∙

research

∙ 05/26/2019

ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems and GANs

Despite remarkable empirical success, the training dynamics of generativ...

10 Ernest K. Ryu, et al. ∙

research

∙ 05/14/2019

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

Plug-and-play (PnP) is a non-convex framework that integrates modern den...

9 Ernest K. Ryu, et al. ∙

research

∙ 12/03/2018

AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Reinforcement Learning with Near-Optimal Sample Complexity

In this paper, we propose AsyncQVI: Asynchronous-Parallel Q-value Iterat...

0 Yibo Zeng, et al. ∙

research

∙ 11/22/2018

Markov Chain Block Coordinate Descent

The method of block coordinate gradient descent (BCD) has been a powerfu...

0 Tao Sun, et al. ∙

research

∙ 09/29/2018

Multilevel Optimal Transport: a Fast Approximation of Wasserstein-1 distances

We propose a fast algorithm for the calculation of the Wasserstein-1 dis...

1 Jialin Liu, et al. ∙

research

∙ 09/12/2018

On Markov Chain Gradient Descent

Stochastic gradient methods are the workhorse (algorithms) of large-scal...

8 Tao Sun, et al. ∙

research

∙ 08/29/2018

Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

In recent years, unfolding iterative algorithms as neural networks has b...

5 Xiaohan Chen, et al. ∙

research

∙ 05/25/2018

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

This paper presents a new class of gradient methods for distributed mach...

0 Tianyi Chen, et al. ∙

research

∙ 04/18/2018

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

This paper addresses consensus optimization problems in a multi-agent ne...

0 Xianghui Mao, et al. ∙

research

∙ 04/18/2018

A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

This paper addresses consensus optimization problem in a multi-agent net...

0 Wotao Yin, et al. ∙

Wotao Yin

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