research
∙
09/08/2023
Online Infinite-Dimensional Regression: Learning Linear Operators
We consider the problem of learning linear operators under squared loss ...
share
research
∙
08/08/2023
Multiclass Online Learnability under Bandit Feedback
We study online multiclass classification under bandit feedback. We exte...
research
∙
07/07/2023
A Combinatorial Characterization of Online Learning Games with Bounded Losses
We study the online learnability of hypothesis classes with respect to a...
research
∙
06/13/2023
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Offline constrained reinforcement learning (RL) aims to learn a policy t...
research
∙
06/09/2023
Online Learning with Set-Valued Feedback
We study a variant of online multiclass classification where the learner...
research
∙
04/06/2023
On the Learnability of Multilabel Ranking
Multilabel ranking is a central task in machine learning with widespread...
research
∙
03/30/2023
A Characterization of Online Multiclass Learnability
We consider the problem of online multiclass learning when the number of...
research
∙
02/15/2023
Quantum Learning Theory Beyond Batch Binary Classification
Arunachalam and de Wolf (2018) showed that the sample complexity of quan...
research
∙
02/03/2023
An Asymptotically Optimal Algorithm for the One-Dimensional Convex Hull Feasibility Problem
This work studies the pure-exploration setting for the convex hull feasi...
research
∙
01/16/2023
Tale of two c(omplex)ities
For decades, best subset selection (BSS) has eluded statisticians mainly...
research
∙
01/06/2023
A Characterization of Multilabel Learnability
We consider the problem of multilabel classification and investigate lea...
research
∙
11/17/2022
Learning Mixtures of Markov Chains and MDPs
We present an algorithm for use in learning mixtures of both Markov chai...
research
∙
11/11/2022
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
We consider the stochastic linear contextual bandit problem with high-di...
research
∙
11/10/2022
Probabilistically Robust PAC Learning
Recently, Robey et al. propose a notion of probabilistic robustness, whi...
research
∙
05/30/2022
Online Agnostic Multiclass Boosting
Boosting is a fundamental approach in machine learning that enjoys both ...
research
∙
05/30/2022
Adaptive Learning for Discovery
In this paper, we study a sequential decision-making problem, called Ada...
research
∙
05/29/2022
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge
We propose an algorithm for non-stationary kernel bandits that does not ...
research
∙
04/13/2022
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Sampling biases in training data are a major source of algorithmic biase...
research
∙
01/05/2022
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
We study the problem of exact support recovery for high-dimensional spar...
research
∙
12/21/2021
Joint Learning of Linear Time-Invariant Dynamical Systems
Learning the parameters of a linear time-invariant dynamical system (LTI...
research
∙
12/20/2021
Balancing Adaptability and Non-exploitability in Repeated Games
We study the problem of guaranteeing low regret in repeated games agains...
research
∙
11/13/2021
On the Statistical Benefits of Curriculum Learning
Curriculum learning (CL) is a commonly used machine learning training st...
research
∙
11/03/2021
Online Learning in Adversarial MDPs: Is the Communicating Case Harder than Ergodic?
We study online learning in adversarial communicating Markov Decision Pr...
research
∙
08/10/2021
Bandit Algorithms for Precision Medicine
The Oxford English Dictionary defines precision medicine as "medical car...
research
∙
07/06/2021
Weighted Gaussian Process Bandits for Non-stationary Environments
In this paper, we consider the Gaussian process (GP) bandit optimization...
research
∙
06/05/2021
Causal Bandits with Unknown Graph Structure
In causal bandit problems, the action set consists of interventions on v...
research
∙
05/31/2021
Representation Learning Beyond Linear Prediction Functions
Recent papers on the theory of representation learning has shown the imp...
research
∙
02/15/2021
Causal Markov Decision Processes: Learning Good Interventions Efficiently
We introduce causal Markov Decision Processes (C-MDPs), a new formalism ...
research
∙
10/15/2020
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns
This paper studies the decision making problem with Funnel Structure. Fu...
research
∙
08/11/2020
Federated Learning via Synthetic Data
Federated learning allows for the training of a model using data on mult...
research
∙
06/12/2020
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Molecular geometry prediction of flexible molecules, or conformer search...
research
∙
06/04/2020
Low-Rank Generalized Linear Bandit Problems
In a low-rank linear bandit problem, the reward of an action (represente...
research
∙
06/02/2020
On the Equivalence between Online and Private Learnability beyond Binary Classification
Alon et al. [2019] and Bun et al. [2020] recently showed that online lea...
research
∙
05/15/2020
On Learnability under General Stochastic Processes
Statistical learning theory under independent and identically distribute...
research
∙
02/06/2020
Near-optimal Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms for the Non-episodic Setting
We study reinforcement learning in factored Markov decision processes (F...
research
∙
12/11/2019
Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits
We investigate the design of two algorithms that enjoy not only computat...
research
∙
10/24/2019
Online Boosting for Multilabel Ranking with Top-k Feedback
We present online boosting algorithms for multilabel ranking with top-k ...
research
∙
10/23/2019
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Reinforcement learning (RL) methods have been shown to be capable of lea...
research
∙
10/12/2019
Thompson Sampling in Non-Episodic Restless Bandits
Restless bandit problems assume time-varying reward distributions of the...
research
∙
10/12/2019
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
Motivated by applications of bandit algorithms in education, we consider...
research
∙
10/11/2019
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
Active learning seeks to build the best possible model with a budget of ...
research
∙
10/11/2019
Regret Analysis of Causal Bandit Problems
We study how to learn optimal interventions sequentially given causal in...
research
∙
05/29/2019
Regret Bounds for Thompson Sampling in Restless Bandit Problems
Restless bandit problems are instances of non-stationary multi-armed ban...
research
∙
05/27/2019
Generalization Bounds in the Predict-then-Optimize Framework
The predict-then-optimize framework is fundamental in many practical set...
research
∙
05/16/2019
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
Data-driven control strategies for dynamical systems with unknown parame...
research
∙
03/14/2019
Contextual Markov Decision Processes using Generalized Linear Models
We consider the recently proposed reinforcement learning (RL) framework ...
research
∙
03/14/2019
On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, line...
research
∙
02/02/2019
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
We investigate the optimality of perturbation based algorithms in the st...
research
∙
11/10/2018
Input Perturbations for Adaptive Regulation and Learning
Design of adaptive algorithms for simultaneous regulation and estimation...
research
∙
10/11/2018
