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09/15/2023
A Bayesian Approach to Robust Inverse Reinforcement Learning
We consider a Bayesian approach to offline model-based inverse reinforce...
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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...
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05/07/2023
Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Transformer models are foundational to natural language processing (NLP)...
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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...
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03/04/2023
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Invariant risk minimization (IRM) has received increasing attention as a...
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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...
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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...
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10/08/2022
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pru...
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10/04/2022
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function...
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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...
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06/23/2022
A Framework for Understanding Model Extraction Attack and Defense
The privacy of machine learning models has become a significant concern ...
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06/13/2022
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
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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)...
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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 ...
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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...
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12/28/2021
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Machine learning has become successful in solving wireless interference ...
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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...
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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...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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02/14/2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
We consider a distributed non-convex optimization where a network of age...
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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...
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12/31/2020
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup
Asynchronous and parallel implementation of standard reinforcement learn...
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12/22/2020
Hybrid Federated Learning: Algorithms and Implementation
Federated learning (FL) is a recently proposed distributed machine learn...
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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...
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11/08/2020
Learning to Beamform in Heterogeneous Massive MIMO Networks
It is well-known that the problem of finding the optimal beamformers in ...
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08/30/2020
Imitation Privacy
In recent years, there have been many cloud-based machine learning servi...
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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 ...
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06/27/2020
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Deep learning models are increasingly popular in many machine learning a...
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06/24/2020
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
We study differentially private (DP) algorithms for stochastic non-conve...
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06/20/2020
On the Divergence of Decentralized Non-Convex Optimization
We study a generic class of decentralized algorithms in which N agents j...
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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 ...
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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...
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12/24/2019
A Communication Efficient Vertical Federated Learning Framework
One critical challenge for applying today's Artificial Intelligence (AI)...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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 ...
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02/02/2019
