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09/18/2023
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
Communication efficiency is a major challenge in federated learning (FL)...
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08/18/2023
A Lightweight Transformer for Faster and Robust EBSD Data Collection
Three dimensional electron back-scattered diffraction (EBSD) microscopy ...
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07/25/2023
Offline Reinforcement Learning with On-Policy Q-Function Regularization
The core challenge of offline reinforcement learning (RL) is dealing wit...
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07/15/2023
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Robustness has been extensively studied in reinforcement learning (RL) t...
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06/15/2023
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Diffusion models, which convert noise into new data instances by learnin...
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05/30/2023
Sharp high-probability sample complexities for policy evaluation with linear function approximation
This paper is concerned with the problem of policy evaluation with linea...
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05/26/2023
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
This paper investigates model robustness in reinforcement learning (RL) ...
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05/18/2023
The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond
When the data used for reinforcement learning (RL) are collected by mult...
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05/17/2023
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
This paper studies tabular reinforcement learning (RL) in the hybrid set...
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05/17/2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression
Achieving communication efficiency in decentralized machine learning has...
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02/02/2023
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
We propose , a preconditioned gradient descent method to tackle the low-...
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01/30/2023
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
Efficient computation of the optimal transport distance between two dist...
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12/21/2022
Deep Unfolded Tensor Robust PCA with Self-supervised Learning
Tensor robust principal component analysis (RPCA), which seeks to separa...
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11/16/2022
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Finding equilibria via gradient play in competitive multi-agent games ha...
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10/03/2022
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Multi-Agent Reinforcement Learning (MARL) – where multiple agents learn ...
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08/22/2022
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
This paper studies multi-agent reinforcement learning in Markov games, w...
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08/22/2022
Local Geometry of Nonconvex Spike Deconvolution from Low-Pass Measurements
Spike deconvolution is the problem of recovering the point sources from ...
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08/11/2022
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
This paper concerns the central issues of model robustness and sample ef...
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06/20/2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
To enable large-scale machine learning in bandwidth-hungry environments ...
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06/18/2022
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
An increasing number of data science and machine learning problems rely ...
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04/12/2022
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization
A major challenge in multi-agent systems is that the system complexity g...
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04/11/2022
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
This paper is concerned with offline reinforcement learning (RL), which ...
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02/28/2022
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Offline or batch reinforcement learning seeks to learn a near-optimal po...
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01/31/2022
BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression
Communication efficiency has been widely recognized as the bottleneck fo...
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10/09/2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Achieving sample efficiency in online episodic reinforcement learning (R...
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10/04/2021
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization
Emerging applications in multi-agent environments such as internet-of-th...
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05/31/2021
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
This paper investigates the problem of computing the equilibrium of comp...
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05/24/2021
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Policy optimization, which learns the policy of interest by maximizing t...
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05/17/2021
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Low-complexity models such as linear function representation play a pivo...
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04/29/2021
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tensors, which provide a powerful and flexible model for representing mu...
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02/26/2021
NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies
Despite being crucial to health and quality of life, sleep – especially ...
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02/22/2021
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
The softmax policy gradient (PG) method, which performs gradient ascent ...
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02/12/2021
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
Q-learning, which seeks to learn the optimal Q-function of a Markov deci...
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01/13/2021
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
Low-rank matrix estimation plays a central role in various applications ...
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12/15/2020
Spectral Methods for Data Science: A Statistical Perspective
Spectral methods have emerged as a simple yet surprisingly effective app...
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10/26/2020
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
Many problems in data science can be treated as estimating a low-rank ma...
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10/20/2020
Compressed Super-Resolution of Positive Sources
Atomic norm minimization is a convex optimization framework to recover p...
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07/13/2020
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
Natural policy gradient (NPG) methods are among the most widely used pol...
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06/04/2020
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Asynchronous Q-learning aims to learn the optimal action-value function ...
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05/26/2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
We investigate the sample efficiency of reinforcement learning in a γ-di...
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05/18/2020
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Low-rank matrix estimation is a canonical problem that finds numerous ap...
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11/25/2019
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently
Multi-channel sparse blind deconvolution, or convolutional sparse coding...
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10/09/2019
Subspace Estimation from Unbalanced and Incomplete Data Matrices: ℓ_2,∞ Statistical Guarantees
This paper is concerned with estimating the column space of an unknown l...
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10/03/2019
On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution
The stability of spike deconvolution, which aims at recovering point sou...
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09/12/2019
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking
There is a growing interest in large-scale machine learning and optimiza...
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05/29/2019
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
We study the denoising of piecewise smooth graph signals that exhibit in...
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05/29/2019
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
Stochastic variance reduced methods have gained a lot of interest recent...
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04/08/2019
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
Convex optimization recently emerges as a compelling framework for perfo...
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02/20/2019
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy low-rank matrix completion: given partial and c...
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09/25/2018
