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.

Constrained Markov Decision Processes via Backward Value Functions

Although Reinforcement Learning (RL) algorithms have found tremendous su...
08/26/2020 ∙ by Harsh Satija, et al. ∙ 6 ∙ share

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How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics

Though generative dialogue modeling is widely seen as a language modelin...
08/24/2020 ∙ by Prasanna Parthasarathi, et al. ∙ 11 ∙ share

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Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information f...
07/14/2020 ∙ by Amy Zhang, et al. ∙ 16 ∙ share

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TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootst...
07/06/2020 ∙ by Joshua Romoff, et al. ∙ 0 ∙ share

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Deep interpretability for GWAS

Genome-Wide Association Studies are typically conducted using linear mod...
07/03/2020 ∙ by Deepak Sharma, et al. ∙ 1 ∙ share

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Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Adversarial imitation learning alternates between learning a discriminat...
06/23/2020 ∙ by Paul Barde, et al. ∙ 15 ∙ share

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Plan2Vec: Unsupervised Representation Learning by Latent Plans

In this paper we introduce plan2vec, an unsupervised representation lear...
05/07/2020 ∙ by Ge Yang, et al. ∙ 8 ∙ share

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A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...
05/06/2020 ∙ by Iulian Vlad Serban, et al. ∙ 1 ∙ share

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Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

We investigate how automated, data-driven, personalized feedback in a la...
05/05/2020 ∙ by Ekaterina Kochmar, et al. ∙ 8 ∙ share

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Learning an Unreferenced Metric for Online Dialogue Evaluation

Evaluating the quality of a dialogue interaction between two agents is a...
05/01/2020 ∙ by Koustuv Sinha, et al. ∙ 0 ∙ share

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Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

One of the challenges in machine learning research is to ensure that pre...
03/27/2020 ∙ by Joelle Pineau, et al. ∙ 0 ∙ 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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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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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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The importance of transparency and reproducibility in artificial intelligence research

In their study, McKinney et al. showed the high potential of artificial ...
02/28/2020 ∙ by Benjamin Haibe-Kains, et al. ∙ 0 ∙ share

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Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic

Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a re...
02/24/2020 ∙ by Wonseok Jeon, et al. ∙ 0 ∙ 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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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Accurate reporting of energy and carbon usage is essential for understan...
01/31/2020 ∙ by Peter Henderson, et al. ∙ 0 ∙ 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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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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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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Benchmarking Batch Deep Reinforcement Learning Algorithms

Widely-used deep reinforcement learning algorithms have been shown to fa...
10/03/2019 ∙ by Scott Fujimoto, et al. ∙ 0 ∙ 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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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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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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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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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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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

Multi-simulator training has contributed to the recent success of Deep R...
06/09/2019 ∙ by Mahmoud Assran, et al. ∙ 0 ∙ share

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Recurrent Value Functions

Despite recent successes in Reinforcement Learning, value-based methods ...
05/23/2019 ∙ by Pierre Thodoroff, et al. ∙ 0 ∙ share

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Leveraging exploration in off-policy algorithms via normalizing flows

Exploration is a crucial component for discovering approximately optimal...
05/16/2019 ∙ by Bogdan Mazoure, et al. ∙ 0 ∙ 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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Separating value functions across time-scales

In many finite horizon episodic reinforcement learning (RL) settings, it...
02/05/2019 ∙ by Joshua Romoff, et al. ∙ 0 ∙ 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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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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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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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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Compositional Language Understanding with Text-based Relational Reasoning

Neural networks for natural language reasoning have largely focused on e...
11/07/2018 ∙ by Koustuv Sinha, et al. ∙ 0 ∙ share

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The RLLChatbot: a solution to the ConvAI Challenge

Current conversational systems can follow simple commands and answer bas...
11/07/2018 ∙ by Nicolas A. Gontier, et al. ∙ 0 ∙ 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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Adversarial Gain

Adversarial examples can be defined as inputs to a model which induce a ...
11/04/2018 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Temporal Regularization in Markov Decision Process

Several applications of Reinforcement Learning suffer from instability d...
11/01/2018 ∙ by Pierre Thodoroff, et al. ∙ 0 ∙ 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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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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Extending Neural Generative Conversational Model using External Knowledge Sources

The use of connectionist approaches in conversational agents has been pr...
09/14/2018 ∙ by Prasanna Parthasarathi, et al. ∙ 0 ∙ share

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Sequential Coordination of Deep Models for Learning Visual Arithmetic

Achieving machine intelligence requires a smooth integration of percepti...
09/13/2018 ∙ by Eric Crawford, et al. ∙ 0 ∙ share

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Combined Reinforcement Learning via Abstract Representations

In the quest for efficient and robust reinforcement learning methods, bo...
09/12/2018 ∙ by Vincent Francois-Lavet, et al. ∙ 0 ∙ 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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The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach

Deep reinforcement learning has recently shown many impressive successes...
07/12/2018 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

The risks and perils of overfitting in machine learning are well known. ...
06/20/2018 ∙ by Amy Zhang, et al. ∙ 0 ∙ share

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Joelle Pineau

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