b'R. Srikant'

research

∙ 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...

0 Ameya Anjarlekar, et al. ∙

research

∙ 05/30/2023

Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

The study of collaborative multi-agent bandits has attracted significant...

0 Ronshee Chawla, et al. ∙

research

∙ 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...

0 Anna Winnicki, et al. ∙

research

∙ 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...

0 Seo Taek Kong, et al. ∙

research

∙ 02/08/2023

Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs

Modified policy iteration (MPI) also known as optimistic policy iteratio...

0 Yashaswini Murthy, et al. ∙

research

∙ 02/02/2023

Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms

Many policy-based reinforcement learning (RL) algorithms can be viewed a...

0 Yashaswini Murthy, et al. ∙

research

∙ 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...

0 Anna Winnicki, et al. ∙

research

∙ 10/13/2022

Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation

We provide performance guarantees for a variant of simulation-based poli...

0 Anna Winnicki, et al. ∙

research

∙ 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 ...

0 Zixian Yang, et al. ∙

research

∙ 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...

0 Semih Cayci, et al. ∙

research

∙ 03/23/2022

Minimax Regret for Cascading Bandits

Cascading bandits model the task of learning to rank K out of L items ov...

0 Daniel Vial, et al. ∙

research

∙ 02/28/2022

Robust Multi-Agent Bandits Over Undirected Graphs

We consider a multi-agent multi-armed bandit setting in which n honest a...

0 Daniel Vial, et al. ∙

research

∙ 02/20/2022

Learning to Control Partially Observed Systems with Finite Memory

We consider the reinforcement learning problem for partially observed Ma...

0 Semih Cayci, et al. ∙

research

∙ 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...

0 Anna Winnicki, et al. ∙

research

∙ 09/12/2021

Improved Algorithms for Misspecified Linear Markov Decision Processes

For the misspecified linear Markov decision process (MLMDP) model of Jin...

0 Daniel Vial, et al. ∙

research

∙ 06/08/2021

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation

Natural policy gradient (NPG) methods with function approximation achiev...

0 Semih Cayci, et al. ∙

research

∙ 05/13/2021

The Dynamics of Gradient Descent for Overparametrized Neural Networks

We consider the dynamics of gradient descent (GD) in overparameterized s...

0 Siddhartha Satpathi, et al. ∙

research

∙ 05/04/2021

Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation

We propose two algorithms for episodic stochastic shortest path problems...

0 Daniel Vial, et al. ∙

research

∙ 04/24/2021

Achieving Small Test Error in Mildly Overparameterized Neural Networks

Recent theoretical works on over-parameterized neural nets have focused ...

0 Shiyu Liang, et al. ∙

research

∙ 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...

0 Semih Cayci, et al. ∙

research

∙ 01/29/2021

Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure

We consider Markov Decision Processes (MDPs) in which every stationary p...

0 Joseph Lubars, et al. ∙

research

∙ 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...

0 Daniel Vial, et al. ∙

research

∙ 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...

0 Joseph Lubars, et al. ∙

research

∙ 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 ...

0 Xiaotian Xie, et al. ∙

research

∙ 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...

0 Arghyadip Roy, et al. ∙

research

∙ 08/20/2020

Optimal Load Balancing in Bipartite Graphs

Applications in cloud platforms motivate the study of efficient load bal...

0 Wentao Weng, et al. ∙

research

∙ 07/09/2020

Provably-Efficient Double Q-Learning

In this paper, we establish a theoretical comparison between the asympto...

0 Wentao Weng, et al. ∙

research

∙ 07/07/2020

Robust Multi-Agent Multi-Armed Bandits

There has been recent interest in collaborative multi-agent bandits, whe...

0 Daniel Vial, et al. ∙

research

∙ 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...

14 Ruoyu Sun, et al. ∙

research

∙ 06/30/2020

Continuous-Time Multi-Armed Bandits with Controlled Restarts

Time-constrained decision processes have been ubiquitous in many fundame...

0 Semih Cayci, et al. ∙

research

∙ 02/29/2020

Budget-Constrained Bandits over General Cost and Reward Distributions

We consider a budget-constrained bandit problem where each arm pull incu...

0 Semih Cayci, et al. ∙

research

∙ 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...

0 Shiyu Liang, et al. ∙

research

∙ 08/20/2019

Optimal Search Segmentation Mechanisms for Online Platform Markets

Online platforms, such as Airbnb, hotels.com, Amazon, Uber and Lyft, can...

0 Zhenzhe Zheng, et al. ∙

research

∙ 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...

0 Harsh Gupta, et al. ∙

research

∙ 02/03/2019

Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning

We consider the dynamics of a linear stochastic approximation algorithm ...

0 R. Srikant, et al. ∙

research

∙ 01/25/2019

Almost Boltzmann Exploration

Boltzmann exploration is widely used in reinforcement learning to provid...

0 Harsh Gupta, et al. ∙

research

∙ 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...

0 Weina Wang, et al. ∙

research

∙ 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...

0 Shiyu Liang, et al. ∙

research

∙ 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...

0 Siddhartha Satpathi, et al. ∙

research

∙ 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...

0 Shiyu Liang, et al. ∙

research

∙ 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...

0 Weina Wang, et al. ∙

research

∙ 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...

0 Weina Wang, et al. ∙

research

∙ 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...

0 Shiyu Liang, et al. ∙

research

∙ 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...

0 Siva Theja Maguluri, et al. ∙

research

∙ 10/13/2016

Why Deep Neural Networks for Function Approximation?

Recently there has been much interest in understanding why deep neural n...

0 Shiyu Liang, et al. ∙

research

∙ 04/27/2015

Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits

We study contextual bandits with budget and time constraints, referred t...

0 Huasen Wu, et al. ∙

research

∙ 02/16/2015

Clustering and Inference From Pairwise Comparisons

Given a set of pairwise comparisons, the classical ranking problem compu...

0 Rui Wu, et al. ∙

research

∙ 03/06/2014

Collaborative Filtering with Information-Rich and Information-Sparse Entities

In this paper, we consider a popular model for collaborative filtering i...

0 Kai Zhu, et al. ∙

research

∙ 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,...

0 Jiaming Xu, et al. ∙

research

∙ 04/25/2012

Learning Loosely Connected Markov Random Fields

We consider the structure learning problem for graphical models that we ...

0 Rui Wu, et al. ∙

R. Srikant

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