50 citations · 275 across the 56 of their papers we have counts for
7 papers · 1 filter
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…
Anytime Model Selection in Linear Bandits
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause +1
Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action selection, but also for mo…
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Jonathan N. Lee, Annie Xie, Aldo Pacchiano +4
Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trai…
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…
Data-Driven Online Model Selection With Regret Guarantees
Aldo Pacchiano, Christoph Dann, Claudio Gentile
We consider model selection for sequential decision making in stochastic environments with bandit feedback, where a meta-learner has at its disposal a pool of base learners, and de…
Improving Offline RL by Blending Heuristics
Sinong Geng, Aldo Pacchiano, Andrey Kolobov +1
We propose Heuristic Blending (HUBL), a simple performance-improving technique for a broad class of offline RL algorithms based on value bootstrapping. HUBL modifies the Bellman op…
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…