Search Results for author:
Found 7 papers, 2 papers with code
Interactive Learning from Activity Description
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
We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model.
Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward.
Adversarial Bandits with Knapsacks
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.
Learning Deep ResNet Blocks Sequentially using Boosting Theory
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
Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance.
Convex Risk Minimization and Conditional Probability Estimation
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.
