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...
share
research
∙
05/30/2023
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
The study of collaborative multi-agent bandits has attracted significant...
research
∙
02/15/2023
InfoNCE Loss Provably Learns Cluster-Preserving Representations
The goal of contrasting learning is to learn a representation that prese...
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...
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...
research
∙
09/22/2022
Learning Certifiably Robust Controllers Using Fragile Perception
Advances in computer vision and machine learning enable robots to percei...
research
∙
05/30/2022
PAC Generalization via Invariant Representations
One method for obtaining generalizable solutions to machine learning tas...
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...
research
∙
03/23/2022
Minimax Regret for Cascading Bandits
Cascading bandits model the task of learning to rank K out of L items ov...
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...
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...
research
∙
02/07/2022
MAML and ANIL Provably Learn Representations
Recent empirical evidence has driven conventional wisdom to believe that...
research
∙
09/12/2021
Improved Algorithms for Misspecified Linear Markov Decision Processes
For the misspecified linear Markov decision process (MLMDP) model of Jin...
research
∙
07/07/2021
Episodic Bandits with Stochastic Experts
We study a version of the contextual bandit problem where an agent is gi...
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...
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...
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...
research
∙
05/22/2021
Combinatorial Blocking Bandits with Stochastic Delays
Recent work has considered natural variations of the multi-armed bandit ...
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...
research
∙
03/03/2021
Linear Bandit Algorithms with Sublinear Time Complexity
We propose to accelerate existing linear bandit algorithms to achieve pe...
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...
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...
research
∙
11/02/2020
Stochastic Linear Bandits with Protected Subspace
We study a variant of the stochastic linear bandit problem wherein we op...
research
∙
10/27/2020
Why Does MAML Outperform ERM? An Optimization Perspective
Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...
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...
research
∙
07/07/2020
Robust Multi-Agent Multi-Armed Bandits
There has been recent interest in collaborative multi-agent bandits, whe...
research
∙
07/02/2020
Multi-Agent Low-Dimensional Linear Bandits
We study a multi-agent stochastic linear bandit with side information, p...
research
∙
03/06/2020
Contextual Blocking Bandits
We study a novel variant of the multi-armed bandit problem, where at eac...
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...
research
∙
02/12/2020
Distribution-Agnostic Model-Agnostic Meta-Learning
The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...
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...
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...
research
∙
11/04/2019
Optimistic Optimization for Statistical Model Checking with Regret Bounds
We explore application of multi-armed bandit algorithms to statistical m...
research
∙
10/04/2019
Social Learning in Multi Agent Multi Armed Bandits
In this paper, we introduce a distributed version of the classical stoch...
research
∙
07/27/2019
Blocking Bandits
We consider a novel stochastic multi-armed bandit setting, where playing...
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...
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...
research
∙
07/26/2018
Entropic Latent Variable Discovery
We consider the problem of discovering the simplest latent variable that...
research
∙
06/07/2018
Importance weighted generative networks
Deep generative networks can simulate from a complex target distribution...
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...
research
∙
02/23/2018
Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, w...
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...
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...
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...
research
∙
09/18/2017
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testi...
research
∙
01/10/2017
Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
research
∙
10/04/2016
The Search Problem in Mixture Models
We consider the task of learning the parameters of a single component o...
research
∙
06/01/2016
Contextual Bandits with Latent Confounders: An NMF Approach
Motivated by online recommendation and advertising systems, we consider ...
research
∙
02/08/2012
