1 citations · 2 across the 8 of their papers we have counts for
8 papers
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
Aida Afshar, Aldo Pacchiano
The performance of reinforcement learning (RL) algorithms is sensitive to the choice of hyperparameters, with the learning rate being particularly influential. RL algorithms fail t…
Provable Interactive Learning with Hindsight Instruction Feedback
Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical ap…
Experiment Planning with Function Approximation
Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
We study the problem of experiment planning with function approximation in contextual bandit problems. In settings where there is a significant overhead to deploying adaptive algor…
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
Elena Gal, Shaun Singh, Aldo Pacchiano +3
In many real world settings binary classification decisions are made based on limited data in near real-time, e.g. when assessing a loan application. We focus on a class of these p…
A Unified Model and Dimension for Interactive Estimation
Nataly Brukhim, Miroslav Dudik, Aldo Pacchiano +1
We study an abstract framework for interactive learning called interactive estimation in which the goal is to estimate a target from its "similarity'' to points queried by the lear…
Estimating Optimal Policy Value in General Linear Contextual Bandits
Jonathan N. Lee, Weihao Kong, Aldo Pacchiano +2
In many bandit problems, the maximal reward achievable by a policy is often unknown in advance. We consider the problem of estimating the optimal policy value in the sublinear data…