b'Mingyi Hong'

research

∙ 09/15/2023

A Bayesian Approach to Robust Inverse Reinforcement Learning

We consider a Bayesian approach to offline model-based inverse reinforce...

0 Ran Wei, et al. ∙

research

∙ 08/01/2023

An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning

Recently, bi-level optimization (BLO) has taken center stage in some ver...

0 Yihua Zhang, et al. ∙

research

∙ 05/07/2023

Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens

Transformer models are foundational to natural language processing (NLP)...

0 Zhanpeng Zeng, et al. ∙

research

∙ 03/16/2023

GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

Vertical federated learning (VFL) is a distributed learning paradigm, wh...

0 Xinwei Zhang, et al. ∙

research

∙ 03/04/2023

What Is Missing in IRM Training and Evaluation? Challenges and Solutions

Invariant risk minimization (IRM) has received increasing attention as a...

11 Yihua Zhang, et al. ∙

research

∙ 02/15/2023

Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

Offline inverse reinforcement learning (Offline IRL) aims to recover the...

0 Siliang Zeng, et al. ∙

research

∙ 10/21/2022

When Expressivity Meets Trainability: Fewer than n Neurons Can Work

Modern neural networks are often quite wide, causing large memory and co...

0 Jiawei Zhang, et al. ∙

research

∙ 10/08/2022

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pru...

9 Yihua Zhang, et al. ∙

research

∙ 10/04/2022

Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees

Inverse reinforcement learning (IRL) aims to recover the reward function...

0 Siliang Zeng, et al. ∙

research

∙ 10/04/2022

Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees

We consider the task of estimating a structural model of dynamic decisio...

0 Siliang Zeng, et al. ∙

research

∙ 06/23/2022

A Framework for Understanding Model Extraction Attack and Defense

The privacy of machine learning models has become a significant concern ...

0 Xun Xian, et al. ∙

research

∙ 06/13/2022

Distributed Adversarial Training to Robustify Deep Neural Networks at Scale

Current deep neural networks (DNNs) are vulnerable to adversarial attack...

15 Gaoyuan Zhang, et al. ∙

research

∙ 06/11/2022

Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Machine Learning

This work revisits the joint beamforming (BF) and antenna selection (AS)...

0 Sagar Shrestha, et al. ∙

research

∙ 04/27/2022

Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective

Distributed algorithms have been playing an increasingly important role ...

0 Xinwei Zhang, et al. ∙

research

∙ 03/27/2022

How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

The lack of adversarial robustness has been recognized as an important i...

2 Yimeng Zhang, et al. ∙

research

∙ 12/28/2021

To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?

Machine learning has become successful in solving wireless interference ...

3 Bingqing Song, et al. ∙

research

∙ 12/23/2021

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

Adversarial training (AT) has become a widely recognized defense mechani...

11 Yihua Zhang, et al. ∙

research

∙ 10/11/2021

Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees

Multi-agent reinforcement learning (MARL) has attracted much research at...

13 Siliang Zeng, et al. ∙

research

∙ 10/04/2021

Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima

To regulate a social system comprised of self-interested agents, economi...

0 Boyi Liu, et al. ∙

research

∙ 06/25/2021

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

Providing privacy protection has been one of the primary motivations of ...

0 Xinwei Zhang, et al. ∙

research

∙ 06/19/2021

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

Federated Learning (FL) refers to the paradigm where multiple worker nod...

5 Prashant Khanduri, et al. ∙

research

∙ 05/03/2021

Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective

There has been a growing interest in developing data-driven, and in part...

0 Haoran Sun, et al. ∙

research

∙ 05/01/2021

Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models

The spectrum cartography (SC) technique constructs multi-domain (e.g., f...

6 Sagar Shrestha, et al. ∙

research

∙ 04/29/2021

Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses

This work considers low-rank canonical polyadic decomposition (CPD) unde...

0 Wenqiang Pu, et al. ∙

research

∙ 02/25/2021

On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations

Although deep learning (DL) has received much attention in accelerated M...

8 Chi Zhang, et al. ∙

research

∙ 02/15/2021

A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization

This paper proposes a new algorithm – the Momentum-assisted Single-times...

0 Prashant Khanduri, et al. ∙

research

∙ 02/14/2021

Decentralized Riemannian Gradient Descent on the Stiefel Manifold

We consider a distributed non-convex optimization where a network of age...

0 Shixiang Chen, et al. ∙

research

∙ 01/22/2021

On the Local Linear Rate of Consensus on the Stiefel Manifold

We study the convergence properties of Riemannian gradient method for so...

0 Shixiang Chen, et al. ∙

research

∙ 12/31/2020

Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup

Asynchronous and parallel implementation of standard reinforcement learn...

3 Han Shen, 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

∙ 11/16/2020

Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

There has been a growing interest in developing data-driven and in parti...

0 Haoran Sun, et al. ∙

research

∙ 11/08/2020

Learning to Beamform in Heterogeneous Massive MIMO Networks

It is well-known that the problem of finding the optimal beamformers in ...

0 Minghe Zhu, et al. ∙

research

∙ 08/30/2020

Imitation Privacy

In recent years, there have been many cloud-based machine learning servi...

0 Xun Xian, et al. ∙

research

∙ 07/10/2020

A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

This paper analyzes a two-timescale stochastic algorithm for a class of ...

0 Mingyi Hong, et al. ∙

research

∙ 06/27/2020

Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Deep learning models are increasingly popular in many machine learning a...

0 Xiangyi Chen, et al. ∙

research

∙ 06/24/2020

Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds

We study differentially private (DP) algorithms for stochastic non-conve...

0 Yingxue Zhou, et al. ∙

research

∙ 06/20/2020

On the Divergence of Decentralized Non-Convex Optimization

We study a generic class of decentralized algorithms in which N agents j...

0 Mingyi Hong, 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

∙ 01/14/2020

Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond

Distributed learning has become a critical enabler of the massively conn...

0 Tsung-Hui Chang, et al. ∙

research

∙ 12/24/2019

A Communication Efficient Vertical Federated Learning Framework

One critical challenge for applying today's Artificial Intelligence (AI)...

23 Yang Liu, et al. ∙

research

∙ 12/16/2019

Dense Recurrent Neural Networks for Inverse Problems: History-Cognizant Unrolling of Optimization Algorithms

Inverse problems in medical imaging applications incorporate domain-spec...

11 Seyed Amir Hossein Hosseini, et al. ∙

research

∙ 10/15/2019

ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

The adaptive momentum method (AdaMM), which uses past gradients to updat...

16 Xiangyi Chen, et al. ∙

research

∙ 10/13/2019

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach

Many modern large-scale machine learning problems benefit from decentral...

0 Haoran Sun, et al. ∙

research

∙ 07/14/2019

On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Despite the empirical success of the actor-critic algorithm, its theoret...

0 Zhuoran Yang, et al. ∙

research

∙ 07/09/2019

SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems

This paper proposes low-complexity algorithms for finding approximate se...

0 Songtao Lu, et al. ∙

research

∙ 06/10/2019

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Graph neural networks (GNNs) which apply the deep neural networks to gra...

4 Kaidi Xu, et al. ∙

research

∙ 06/10/2019

Learned Conjugate Gradient Descent Network for Massive MIMO Detection

In this work, we consider the use of model-driven deep learning techniqu...

0 Yi Wei, et al. ∙

research

∙ 06/04/2019

Distributed Training with Heterogeneous Data: Bridging Median and Mean Based Algorithms

Recently, there is a growing interest in the study of median-based algor...

0 Xiangyi Chen, et al. ∙

research

∙ 02/21/2019

Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

The min-max problem, also known as the saddle point problem, is a class ...

1 Songtao Lu, et al. ∙

research

∙ 02/02/2019

Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

We consider a multi-user video streaming service optimization problem ov...

0 Kexin Tang, et al. ∙

Mingyi Hong

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