Joelle Pineau

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Facebook ∙ McGill University

Associate Professor at McGill University and Research Manager at Facebook AI Research Lab in Montreal.

On the editorial board of Journal of Artificial Intelligence Research and the Journal of Machine Learning Research.

President-Elect of the International Machine Learning Society.

Learning Causal State Representations of Partially Observable Environments

Intelligent agents can cope with sensory-rich environments by learning t...
06/25/2019 ∙ by Amy Zhang, et al. ∙ 68 ∙ share

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An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning...
11/30/2018 ∙ by Vincent Francois-Lavet, et al. ∙ 28 ∙ share

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TarMAC: Targeted Multi-Agent Communication

We explore a collaborative multi-agent reinforcement learning setting wh...
10/26/2018 ∙ by Abhishek Das, et al. ∙ 22 ∙ share

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Online Adaptative Curriculum Learning for GANs

Generative Adversarial Networks (GANs) can successfully learn a probabil...
07/31/2018 ∙ by Thang Doan, et al. ∙ 20 ∙ share

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Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Training an agent to solve control tasks directly from high-dimensional ...
10/02/2019 ∙ by Denis Yarats, et al. ∙ 18 ∙ share

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Provably efficient reconstruction of policy networks

Recent research has shown that learning poli-cies parametrized by large ...
02/07/2020 ∙ by Bogdan Mazoure, et al. ∙ 18 ∙ share

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MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

Effective network congestion control strategies are key to keeping the I...
10/09/2019 ∙ by Viswanath Sivakumar, et al. ∙ 16 ∙ share

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Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because...
09/17/2019 ∙ by Thang Doan, et al. ∙ 15 ∙ share

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On the Pitfalls of Measuring Emergent Communication

How do we know if communication is emerging in a multi-agent system? The...
03/12/2019 ∙ by Ryan Lowe, et al. ∙ 14 ∙ share

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Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

The ability to detect and track objects in the visual world is a crucial...
11/20/2019 ∙ by Eric Crawford, et al. ∙ 14 ∙ share

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Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods

Recent analyses of certain gradient descent optimization methods have sh...
10/05/2018 ∙ by Peter Henderson, et al. ∙ 12 ∙ share

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Online Learned Continual Compression with Stacked Quantization Module

We introduce and study the problem of Online Continual Compression, wher...
11/19/2019 ∙ by Lucas Caccia, et al. ∙ 12 ∙ share

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Evaluating Logical Generalization in Graph Neural Networks

Recent research has highlighted the role of relational inductive biases ...
03/14/2020 ∙ by Koustuv Sinha, et al. ∙ 9 ∙ share

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Interference and Generalization in Temporal Difference Learning

We study the link between generalization and interference in temporal-di...
03/13/2020 ∙ by Emmanuel Bengio, et al. ∙ 7 ∙ share

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Natural Environment Benchmarks for Reinforcement Learning

While current benchmark reinforcement learning (RL) tasks have been usef...
11/14/2018 ∙ by Amy Zhang, et al. ∙ 6 ∙ share

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Stable Policy Optimization via Off-Policy Divergence Regularization

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...
03/09/2020 ∙ by Ahmed Touati, et al. ∙ 6 ∙ share

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No Press Diplomacy: Modeling Multi-Agent Gameplay

Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
09/04/2019 ∙ by Philip Paquette, et al. ∙ 4 ∙ share

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The Second Conversational Intelligence Challenge (ConvAI2)

We describe the setting and results of the ConvAI2 NeurIPS competition t...
01/31/2019 ∙ by Emily Dinan, et al. ∙ 2 ∙ share

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Invariant Causal Prediction for Block MDPs

Generalization across environments is critical to the successful applica...
03/12/2020 ∙ by Amy Zhang, et al. ∙ 2 ∙ share

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Language GANs Falling Short

Generating high-quality text with sufficient diversity is essential for ...
11/06/2018 ∙ by Massimo Caccia, et al. ∙ 1 ∙ share

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Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challen...
12/04/2018 ∙ by Lucas Caccia, et al. ∙ 1 ∙ share

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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

The recent success of natural language understanding (NLU) systems has b...
08/16/2019 ∙ by Koustuv Sinha, et al. ∙ 1 ∙ share

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Independently Controllable Features

Finding features that disentangle the different causes of variation in r...
03/22/2017 ∙ by Emmanuel Bengio, et al. ∙ 0 ∙ share

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ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...
11/13/2017 ∙ by Anirudh Goyal, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...
09/19/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Independently Controllable Factors

It has been postulated that a good representation is one that disentangl...
08/03/2017 ∙ by Valentin Thomas, et al. ∙ 0 ∙ share

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Streaming kernel regression with provably adaptive mean, variance, and regularization

We consider the problem of streaming kernel regression, when the observa...
08/02/2017 ∙ by Audrey Durand, et al. ∙ 0 ∙ share

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MACA: A Modular Architecture for Conversational Agents

We propose a software architecture designed to ease the implementation o...
05/01/2017 ∙ by Hoai Phuoc Truong, et al. ∙ 0 ∙ share

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Bayesian Reinforcement Learning: A Survey

Bayesian methods for machine learning have been widely investigated, yie...
09/14/2016 ∙ by Mohammad Ghavamzadeh, et al. ∙ 0 ∙ share

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Non-Deterministic Policies in Markovian Decision Processes

Markovian processes have long been used to model stochastic environments...
01/16/2014 ∙ by Mahdi Milani Fard, et al. ∙ 0 ∙ share

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Online Planning Algorithms for POMDPs

Partially Observable Markov Decision Processes (POMDPs) provide a rich f...
01/15/2014 ∙ by Stéphane Ross, et al. ∙ 0 ∙ share

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Learning Robust Features using Deep Learning for Automatic Seizure Detection

We present and evaluate the capacity of a deep neural network to learn r...
07/31/2016 ∙ by Pierre Thodoroff, et al. ∙ 0 ∙ share

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Policy-contingent abstraction for robust robot control

This paper presents a scalable control algorithm that enables a deployed...
10/19/2012 ∙ by Joelle Pineau, et al. ∙ 0 ∙ share

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Practical Kernel-Based Reinforcement Learning

Kernel-based reinforcement learning (KBRL) stands out among reinforcemen...
07/21/2014 ∙ by André M. S. Barreto, et al. ∙ 0 ∙ share

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Model-Based Bayesian Reinforcement Learning in Large Structured Domains

Model-based Bayesian reinforcement learning has generated significant in...
06/13/2012 ∙ by Stéphane Ross, et al. ∙ 0 ∙ share

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Efficient Learning and Planning with Compressed Predictive States

Predictive state representations (PSRs) offer an expressive framework fo...
12/01/2013 ∙ by William L. Hamilton, et al. ∙ 0 ∙ share

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Bellman Error Based Feature Generation using Random Projections on Sparse Spaces

We address the problem of automatic generation of features for value fun...
07/23/2012 ∙ by Mahdi Milani Fard, et al. ∙ 0 ∙ share

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Proceedings of the 29th International Conference on Machine Learning (ICML-12)

This is an index to the papers that appear in the Proceedings of the 29t...
07/19/2012 ∙ by John Langford, et al. ∙ 0 ∙ share

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Active Learning for Developing Personalized Treatment

The personalization of treatment via bio-markers and other risk categori...
02/14/2012 ∙ by Kun Deng, et al. ∙ 0 ∙ share

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PAC-Bayesian Policy Evaluation for Reinforcement Learning

Bayesian priors offer a compact yet general means of incorporating domai...
02/14/2012 ∙ by Mahdi Milani Fard, et al. ∙ 0 ∙ share

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End-to-End Text Recognition with Hybrid HMM Maxout Models

The problem of detecting and recognizing text in natural scenes has prov...
10/07/2013 ∙ by Ouais Alsharif, et al. ∙ 0 ∙ share

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A Deep Reinforcement Learning Chatbot

We present MILABOT: a deep reinforcement learning chatbot developed by t...
09/07/2017 ∙ by Iulian V. Serban, et al. ∙ 0 ∙ share

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Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructu...
08/23/2017 ∙ by Ryan Lowe, et al. ∙ 0 ∙ share

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Piecewise Latent Variables for Neural Variational Text Processing

Advances in neural variational inference have facilitated the learning o...
12/01/2016 ∙ by Iulian V. Serban, et al. ∙ 0 ∙ share

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Generative Deep Neural Networks for Dialogue: A Short Review

Researchers have recently started investigating deep neural networks for...
11/18/2016 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex de...
05/19/2016 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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On the Evaluation of Dialogue Systems with Next Utterance Classification

An open challenge in constructing dialogue systems is developing methods...
05/18/2016 ∙ by Ryan Lowe, et al. ∙ 0 ∙ share

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How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

We investigate evaluation metrics for dialogue response generation syste...
03/25/2016 ∙ by Chia-Wei Liu, et al. ∙ 0 ∙ share

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A Survey of Available Corpora for Building Data-Driven Dialogue Systems

During the past decade, several areas of speech and language understandi...
12/17/2015 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue...
07/17/2015 ∙ by Iulian V. Serban, et al. ∙ 0 ∙ share

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Joelle Pineau

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