research
∙
05/18/2023
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
While policy optimization algorithms have played an important role in re...
share
research
∙
05/07/2023
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
The emergence of large language models has led to the development of pow...
research
∙
02/10/2023
A Second-Order Method for Stochastic Bandit Convex Optimisation
We introduce a simple and efficient algorithm for unconstrained zeroth-o...
research
∙
12/23/2022
Generalization Bounds for Transfer Learning with Pretrained Classifiers
We study the ability of foundation models to learn representations for c...
research
∙
12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
research
∙
10/27/2022
Confident Approximate Policy Iteration for Efficient Local Planning in q^π-realizable MDPs
We consider approximate dynamic programming in γ-discounted Markov decis...
research
∙
05/26/2022
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
We study distributed contextual linear bandits with stochastic contexts,...
research
∙
02/25/2022
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
We study a sequential decision problem where the learner faces a sequenc...
research
∙
01/17/2022
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits
We study the non-stationary stochastic multi-armed bandit problem, where...
research
∙
12/30/2021
On the Role of Neural Collapse in Transfer Learning
We study the ability of foundation models to learn representations for c...
research
∙
10/05/2021
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions
We consider the minimax query complexity of online planning with a gener...
research
∙
06/30/2021
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting
The time average expected age of information (AoI) is studied for status...
research
∙
06/15/2021
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
Many advances that have improved the robustness and efficiency of deep r...
research
∙
04/02/2021
Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vu...
research
∙
02/19/2021
A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ
Scheduling the transmission of time-sensitive information from a source ...
research
∙
02/14/2021
Perceptually Constrained Adversarial Attacks
Motivated by previous observations that the usually applied L_p norms (p...
research
∙
12/01/2020
Mutual Information Constraints for Monte-Carlo Objectives
A common failure mode of density models trained as variational autoencod...
research
∙
10/12/2020
Adapting to Delays and Data in Adversarial Multi-Armed Bandits
We consider the adversarial multi-armed bandit problem under delayed fee...
research
∙
09/25/2020
Mirror Descent and the Information Ratio
We establish a connection between the stability of mirror descent and th...
research
∙
06/18/2020
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
We consider off-policy evaluation in the contextual bandit setting for t...
research
∙
06/03/2020
Non-Stationary Bandits with Intermediate Observations
Online recommender systems often face long delays in receiving feedback,...
research
∙
10/12/2019
Minimal Assumptions Refinement for GR(1) Specifications
Reactive synthesis is concerned with finding a correct-by-construction c...
research
∙
05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
research
∙
03/06/2019
Detecting Overfitting via Adversarial Examples
The repeated reuse of test sets in popular benchmark problems raises dou...
research
∙
02/27/2019
Communication without Interception: Defense against Deep-Learning-based Modulation Detection
We consider a communication scenario, in which an intruder, employing a ...
research
∙
01/24/2019
Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost
The time average expected age of information (AoI) is studied for status...
research
∙
07/24/2018
Learning from Delayed Outcomes with Intermediate Observations
Optimizing for long term value is desirable in many practical applicatio...
research
∙
07/02/2018
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
We consider the problem of configuring general-purpose solvers to run ef...
research
∙
06/11/2018
Adaptive MCMC via Combining Local Samplers
Markov chain Monte Carlo (MCMC) methods are widely used in machine learn...
research
∙
06/01/2018
A Reinforcement Learning Approach to Age of Information in Multi-User Networks
Scheduling the transmission of time-sensitive data to multiple users ove...
research
∙
05/08/2018
A Weakness Measure for GR(1) Formulae
In spite of the theoretical and algorithmic developments for system synt...
research
∙
02/08/2018
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
Machine learning has become an important component for many systems and ...
research
∙
12/19/2017
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks
We consider a mobile user accessing contents in a dynamic environment, w...
research
∙
09/08/2017
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds
Recently, much work has been done on extending the scope of online learn...
research
∙
09/22/2016
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Algorithms for bandit convex optimization and online learning often rely...
research
∙
09/07/2016
Chaining Bounds for Empirical Risk Minimization
This paper extends the standard chaining technique to prove excess risk ...
research
∙
10/27/2015
Online Learning with Gaussian Payoffs and Side Observations
We consider a sequential learning problem with Gaussian payoffs and side...
research
∙
06/30/2015
Fast Cross-Validation for Incremental Learning
Cross-validation (CV) is one of the main tools for performance estimatio...
research
∙
05/13/2014
Adaptive Monte Carlo via Bandit Allocation
We consider the problem of sequentially choosing between a set of unbias...
research
∙
01/16/2014
Efficient Multi-Start Strategies for Local Search Algorithms
Local search algorithms applied to optimization problems often suffer fr...
research
∙
06/04/2013
Online Learning under Delayed Feedback
Online learning with delayed feedback has received increasing attention ...
research
∙
11/03/2012
Partition Tree Weighting
This paper introduces the Partition Tree Weighting technique, an efficie...
research
∙
05/01/2012