Sanjay Shakkottai

research

∙ 07/02/2023

Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models

We present the first framework to solve linear inverse problems leveragi...

0 Litu Rout, 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

∙ 02/15/2023

InfoNCE Loss Provably Learns Cluster-Preserving Representations

The goal of contrasting learning is to learn a representation that prese...

0 Advait Parulekar, et al. ∙

research

∙ 02/13/2023

Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD

This work considers the problem of finding a first-order stationary poin...

0 Matthew Faw, et al. ∙

research

∙ 02/02/2023

A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

We provide a theoretical justification for sample recovery using diffusi...

0 Litu Rout, et al. ∙

research

∙ 09/22/2022

Learning Certifiably Robust Controllers Using Fragile Perception

Advances in computer vision and machine learning enable robots to percei...

0 Dawei Sun, et al. ∙

research

∙ 05/30/2022

PAC Generalization via Invariant Representations

One method for obtaining generalizable solutions to machine learning tas...

0 Advait Parulekar, et al. ∙

research

∙ 05/29/2022

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

The stochastic multi-armed bandit setting has been recently studied in t...

0 Orestis Papadigenopoulos, 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

∙ 02/11/2022

The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance

We study convergence rates of AdaGrad-Norm as an exemplar of adaptive st...

14 Matthew Faw, et al. ∙

research

∙ 02/07/2022

MAML and ANIL Provably Learn Representations

Recent empirical evidence has driven conventional wisdom to believe that...

0 Liam Collins, 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

∙ 07/07/2021

Episodic Bandits with Stochastic Experts

We study a version of the contextual bandit problem where an agent is gi...

0 Nihal Sharma, et al. ∙

research

∙ 06/24/2021

Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators

In temporal difference (TD) learning, off-policy sampling is known to be...

0 Zaiwei Chen, et al. ∙

research

∙ 06/21/2021

Does Optimal Source Task Performance Imply Optimal Pre-training for a Target Task?

Pre-trained deep nets are commonly used to improve accuracies and traini...

0 Steven Gutstein, et al. ∙

research

∙ 06/08/2021

Job Dispatching Policies for Queueing Systems with Unknown Service Rates

In multi-server queueing systems where there is no central queue holding...

0 Tuhinangshu Choudhury, et al. ∙

research

∙ 05/22/2021

Combinatorial Blocking Bandits with Stochastic Delays

Recent work has considered natural variations of the multi-armed bandit ...

0 Alexia Atsidakou, 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

∙ 03/03/2021

Linear Bandit Algorithms with Sublinear Time Complexity

We propose to accelerate existing linear bandit algorithms to achieve pe...

18 Shuo Yang, et al. ∙

research

∙ 02/02/2021

A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants

This paper develops an unified framework to study finite-sample converge...

0 Zaiwei Chen, 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

∙ 11/02/2020

Stochastic Linear Bandits with Protected Subspace

We study a variant of the stochastic linear bandit problem wherein we op...

0 Advait Parulekar, et al. ∙

research

∙ 10/27/2020

Why Does MAML Outperform ERM? An Optimization Perspective

Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...

0 Liam Collins, 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

∙ 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

Multi-Agent Low-Dimensional Linear Bandits

We study a multi-agent stochastic linear bandit with side information, p...

0 Ronshee Chawla, et al. ∙

research

∙ 03/06/2020

Contextual Blocking Bandits

We study a novel variant of the multi-armed bandit problem, where at eac...

6 Soumya Basu, et al. ∙

research

∙ 02/19/2020

Warm Starting Bandits with Side Information from Confounded Data

We study a variant of the multi-armed bandit problem where side informat...

0 Nihal Sharma, et al. ∙

research

∙ 02/12/2020

Distribution-Agnostic Model-Agnostic Meta-Learning

The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...

21 Liam Collins, et al. ∙

research

∙ 02/03/2020

Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes

Stochastic Approximation (SA) is a popular approach for solving fixed po...

0 Zaiwei Chen, et al. ∙

research

∙ 01/15/2020

The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits

We consider a decentralized multi-agent Multi Armed Bandit (MAB) setup c...

0 Ronshee Chawla, et al. ∙

research

∙ 11/04/2019

Optimistic Optimization for Statistical Model Checking with Regret Bounds

We explore application of multi-armed bandit algorithms to statistical m...

0 Negin Musavi, et al. ∙

research

∙ 10/04/2019

Social Learning in Multi Agent Multi Armed Bandits

In this paper, we introduce a distributed version of the classical stoch...

0 Abishek Sankararaman, et al. ∙

research

∙ 07/27/2019

Blocking Bandits

We consider a novel stochastic multi-armed bandit setting, where playing...

0 Soumya Basu, et al. ∙

research

∙ 07/23/2019

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

We consider a co-variate shift problem where one has access to several m...

5 Matthew Faw, et al. ∙

research

∙ 10/24/2018

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach

We study the problem of black-box optimization of a noisy function in th...

0 Rajat Sen, et al. ∙

research

∙ 07/26/2018

Entropic Latent Variable Discovery

We consider the problem of discovering the simplest latent variable that...

2 Murat Kocaoglu, et al. ∙

research

∙ 06/07/2018

Importance weighted generative networks

Deep generative networks can simulate from a complex target distribution...

2 Maurice Diesendruck, et al. ∙

research

∙ 03/01/2018

The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations

We introduce a new malware detector - Shape-GD - that aggregates per-mac...

0 Mikhail Kazdagli, et al. ∙

research

∙ 02/23/2018

Contextual Bandits with Stochastic Experts

We consider the problem of contextual bandits with stochastic experts, w...

0 Rajat Sen, et al. ∙

research

∙ 02/02/2018

On Learning the cμ Rule in Single and Parallel Server Networks

We consider learning-based variants of the c μ rule for scheduling in si...

0 Subhashini Krishnasamy, et al. ∙

research

∙ 02/02/2018

On Learning the cμ Rule: Single and Multiserver Settings

We consider learning-based variants of the c μ rule -- a classic and wel...

0 Subhashini Krishnasamy, et al. ∙

research

∙ 12/14/2017

Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

Emerging 5G systems will need to efficiently support both broadband traf...

0 Arjun Anand, et al. ∙

research

∙ 09/18/2017

Model-Powered Conditional Independence Test

We consider the problem of non-parametric Conditional Independence testi...

0 Rajat Sen, et al. ∙

research

∙ 01/10/2017

Identifying Best Interventions through Online Importance Sampling

Motivated by applications in computational advertising and systems biolo...

0 Rajat Sen, et al. ∙

research

∙ 10/04/2016

The Search Problem in Mixture Models

We consider the task of learning the parameters of a single component o...

0 Avik Ray, et al. ∙

research

∙ 06/01/2016

Contextual Bandits with Latent Confounders: An NMF Approach

Motivated by online recommendation and advertising systems, we consider ...

0 Rajat Sen, et al. ∙

research

∙ 02/08/2012

Greedy Learning of Markov Network Structure

We propose a new yet natural algorithm for learning the graph structure ...

0 Praneeth Netrapalli, et al. ∙

Sanjay Shakkottai

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