Search Results for author: Carlos Riquelme

Found 15 papers, 7 papers with code

Which Model to Transfer? Finding the Needle in the Growing Haystack

no code implementations13 Oct 2020 Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic

Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline.

Transfer Learning

Google Research Football: A Novel Reinforcement Learning Environment

1 code implementation25 Jul 2019 Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.

Game of Football reinforcement-learning

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates

no code implementations NeurIPS 2019 Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy Mann, Andre Barreto, Sylvain Gelly, Gergely Neu

We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation.

Practical and Consistent Estimation of f-Divergences

1 code implementation NeurIPS 2019 Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin

The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.

Mutual Information Estimation Representation Learning

Active Learning for Accurate Estimation of Linear Models

no code implementations ICML 2017 Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric

We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sampled from a known distribution.

Active Learning Decision Making

Human Interaction with Recommendation Systems

1 code implementation1 Mar 2017 Sven Schmit, Carlos Riquelme

Based on this model, we prove that naive estimators, i. e. those which ignore this feedback loop, are not consistent.

Recommendation Systems

Online Active Linear Regression via Thresholding

no code implementations9 Feb 2016 Carlos Riquelme, Ramesh Johari, Baosen Zhang

We consider the problem of online active learning to collect data for regression modeling.

Active Learning

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