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09/19/2023
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression
We investigate the problem of performing logistic regression on data col...
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05/30/2023
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
The study of collaborative multi-agent bandits has attracted significant...
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03/17/2023
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games
Many model-based reinforcement learning (RL) algorithms can be viewed as...
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02/14/2023
A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks
Crowdsourcing is a popular method used to estimate ground-truth labels b...
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02/08/2023
Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs
Modified policy iteration (MPI) also known as optimistic policy iteratio...
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02/02/2023
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Many policy-based reinforcement learning (RL) algorithms can be viewed a...
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01/23/2023
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation
A common technique in reinforcement learning is to evaluate the value fu...
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10/13/2022
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation
We provide performance guarantees for a variant of simulation-based poli...
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09/02/2022
MaxWeight With Discounted UCB: A Provably Stable Scheduling Policy for Nonstationary Multi-Server Systems With Unknown Statistics
Multi-server queueing systems are widely used models for job scheduling ...
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06/02/2022
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Natural actor-critic (NAC) and its variants, equipped with the represent...
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03/23/2022
Minimax Regret for Cascading Bandits
Cascading bandits model the task of learning to rank K out of L items ov...
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02/28/2022
Robust Multi-Agent Bandits Over Undirected Graphs
We consider a multi-agent multi-armed bandit setting in which n honest a...
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02/20/2022
Learning to Control Partially Observed Systems with Finite Memory
We consider the reinforcement learning problem for partially observed Ma...
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09/28/2021
The Role of Lookahead and Approximate Policy Evaluation in Policy Iteration with Linear Value Function Approximation
When the sizes of the state and action spaces are large, solving MDPs ca...
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09/12/2021
Improved Algorithms for Misspecified Linear Markov Decision Processes
For the misspecified linear Markov decision process (MLMDP) model of Jin...
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06/08/2021
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
Natural policy gradient (NPG) methods with function approximation achiev...
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05/13/2021
The Dynamics of Gradient Descent for Overparametrized Neural Networks
We consider the dynamics of gradient descent (GD) in overparameterized s...
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05/04/2021
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
We propose two algorithms for episodic stochastic shortest path problems...
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04/24/2021
Achieving Small Test Error in Mildly Overparameterized Neural Networks
Recent theoretical works on over-parameterized neural nets have focused ...
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03/02/2021
Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning
We study the dynamics of temporal-difference learning with neural networ...
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01/29/2021
Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure
We consider Markov Decision Processes (MDPs) in which every stationary p...
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12/04/2020
One-bit feedback is sufficient for upper confidence bound policies
We consider a variant of the traditional multi-armed bandit problem in w...
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11/17/2020
Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging
We consider the problem of designing an algorithm to allow a car to auto...
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10/17/2020
On the Consistency of Maximum Likelihood Estimators for Causal Network Identification
We consider the problem of identifying parameters of a particular class ...
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09/14/2020
Hellinger KL-UCB based Bandit Algorithms for Markovian and i.i.d. Settings
In the regret-based formulation of multi-armed bandit (MAB) problems, ex...
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08/20/2020
Optimal Load Balancing in Bipartite Graphs
Applications in cloud platforms motivate the study of efficient load bal...
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07/09/2020
Provably-Efficient Double Q-Learning
In this paper, we establish a theoretical comparison between the asympto...
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07/07/2020
Robust Multi-Agent Multi-Armed Bandits
There has been recent interest in collaborative multi-agent bandits, whe...
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07/02/2020
The Global Landscape of Neural Networks: An Overview
One of the major concerns for neural network training is that the non-co...
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06/30/2020
Continuous-Time Multi-Armed Bandits with Controlled Restarts
Time-constrained decision processes have been ubiquitous in many fundame...
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02/29/2020
Budget-Constrained Bandits over General Cost and Reward Distributions
We consider a budget-constrained bandit problem where each arm pull incu...
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12/31/2019
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
Traditional landscape analysis of deep neural networks aims to show that...
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08/20/2019
Optimal Search Segmentation Mechanisms for Online Platform Markets
Online platforms, such as Airbnb, hotels.com, Amazon, Uber and Lyft, can...
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07/14/2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
We study two time-scale linear stochastic approximation algorithms, whic...
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02/03/2019
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning
We consider the dynamics of a linear stochastic approximation algorithm ...
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01/25/2019
Almost Boltzmann Exploration
Boltzmann exploration is widely used in reinforcement learning to provid...
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08/06/2018
Heavy-Traffic Insensitive Bounds for Weighted Proportionally Fair Bandwidth Sharing Policies
We consider a connection-level model proposed by Massoulié and Roberts f...
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05/22/2018
Adding One Neuron Can Eliminate All Bad Local Minima
One of the main difficulties in analyzing neural networks is the non-con...
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04/10/2018
Learning Latent Events from Network Message Logs: A Decomposition Based Approach
In this communication, we describe a novel technique for event mining us...
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02/19/2018
Understanding the Loss Surface of Neural Networks for Binary Classification
It is widely conjectured that the reason that training algorithms for ne...
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10/01/2017
Delay Asymptotics and Bounds for Multi-Task Parallel Jobs
We study delay of jobs that consist of multiple parallel tasks, which is...
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10/01/2017
Asymptotic response time analysis for multi-task parallel jobs
The response time of jobs with multiple parallel tasks is a critical per...
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06/08/2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
We consider the problem of detecting out-of-distribution images in neura...
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11/02/2016
Optimal Heavy-Traffic Queue Length Scaling in an Incompletely Saturated Switch
We consider an input queued switch operating under the MaxWeight schedul...
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10/13/2016
Why Deep Neural Networks for Function Approximation?
Recently there has been much interest in understanding why deep neural n...
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04/27/2015
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
We study contextual bandits with budget and time constraints, referred t...
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02/16/2015
Clustering and Inference From Pairwise Comparisons
Given a set of pairwise comparisons, the classical ranking problem compu...
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03/06/2014
Collaborative Filtering with Information-Rich and Information-Sparse Entities
In this paper, we consider a popular model for collaborative filtering i...
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10/01/2013
Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs
In standard clustering problems, data points are represented by vectors,...
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04/25/2012