107 citations · 287 across the 15 of their papers we have counts for
7 papers · 1 filter
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko +8
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two recent continual-learning scenarios have opened…
Contextual Bandit with Adaptive Feature Extraction
Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi +1
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature e…
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard T…
Context Attentive Bandits: Contextual Bandit with Restricted Context
Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi +1
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accesse…
A Scheme for Approximating Probabilistic Inference
Rina Dechter, Irina Rish
This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approxim…
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
Irina Rish, Kalev Kask, Rina Dechter
It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [McEliece].…