Search Results for author: Robert Schapire

Found 7 papers, 2 papers with code

Interactive Learning from Activity Description

1 code implementation13 Feb 2021 Khanh Nguyen, Dipendra Misra, Robert Schapire, Miro Dudík, Patrick Shafto

We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities.

General Reinforcement Learning Grounded language learning +2

Contextual Search in the Presence of Adversarial Corruptions

no code implementations26 Feb 2020 Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert Schapire

We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model.

Learning Theory

Adversarial Bandits with Knapsacks

no code implementations28 Nov 2018 Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins

We suggest a new algorithm for the stochastic version, which builds on the framework of regret minimization in repeated games and admits a substantially simpler analysis compared to prior work.

Multi-Armed Bandits Scheduling

Learning Deep ResNet Blocks Sequentially using Boosting Theory

no code implementations ICML 2018 Furong Huang, Jordan Ash, John Langford, Robert Schapire

We prove that the training error decays exponentially with the depth $T$ if the \emph{weak module classifiers} that we train perform slightly better than some weak baseline.

Functional Frank-Wolfe Boosting for General Loss Functions

no code implementations9 Oct 2015 Chu Wang, Yingfei Wang, Weinan E, Robert Schapire

Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance.

Binary Classification General Classification +1

Convex Risk Minimization and Conditional Probability Estimation

no code implementations15 Jun 2015 Matus Telgarsky, Miroslav Dudík, Robert Schapire

This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem.

General Classification

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