b'Joelle Pineau'

research

∙ 06/21/2022

Questions Are All You Need to Train a Dense Passage Retriever

We introduce ART, a new corpus-level autoencoding approach for training ...

Devendra Singh Sachan, et al. ∙

research

∙ 04/15/2022

Improving Passage Retrieval with Zero-Shot Question Generation

We propose a simple and effective re-ranking method for improving passag...

Devendra Singh Sachan, et al. ∙

research

∙ 03/08/2022

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

In the online continual learning paradigm, agents must learn from a chan...

Lucas Caccia, et al. ∙

research

∙ 02/28/2022

Estimating causal effects with optimization-based methods: A review and empirical comparison

In the absence of randomized controlled and natural experiments, it is n...

Martin Cousineau, et al. ∙

research

∙ 02/20/2022

Efficient Continual Learning Ensembles in Neural Network Subspaces

A growing body of research in continual learning focuses on the catastro...

Thang Doan, et al. ∙

research

∙ 02/14/2022

Robust Policy Learning over Multiple Uncertainty Sets

Reinforcement learning (RL) agents need to be robust to variations in sa...

Annie Xie, et al. ∙

research

∙ 01/05/2022

A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions

Estimating value functions is a core component of reinforcement learning...

Anthony GX-Chen, et al. ∙

research

∙ 10/13/2021

Block Contextual MDPs for Continual Learning

In reinforcement learning (RL), when defining a Markov Decision Process ...

Shagun Sodhani, et al. ∙

research

∙ 06/21/2021

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

We consider the offline reinforcement learning (RL) setting where the ag...

Jongmin Lee, et al. ∙

research

∙ 06/20/2021

Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?

Predicting the next utterance in dialogue is contingent on encoding of u...

Prasanna Parthasarathi, et al. ∙

research

∙ 06/20/2021

A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss

Neural models trained for next utterance generation in dialogue task lea...

Prasanna Parthasarathi, et al. ∙

research

∙ 06/16/2021

SPeCiaL: Self-Supervised Pretraining for Continual Learning

This paper presents SPeCiaL: a method for unsupervised pretraining of re...

Lucas Caccia, et al. ∙

research

∙ 06/07/2021

Correcting Momentum in Temporal Difference Learning

A common optimization tool used in deep reinforcement learning is moment...

Emmanuel Bengio, et al. ∙

research

∙ 05/31/2021

Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs

We study the problem of Safe Policy Improvement (SPI) under constraints ...

Harsh Satija, et al. ∙

research

∙ 04/15/2021

Sometimes We Want Translationese

Rapid progress in Neural Machine Translation (NMT) systems over the last...

Prasanna Parthasarathi, et al. ∙

research

∙ 04/14/2021

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

A possible explanation for the impressive performance of masked language...

Koustuv Sinha, et al. ∙

research

∙ 04/11/2021

Reducing Representation Drift in Online Continual Learning

We study the online continual learning paradigm, where agents must learn...

Lucas Caccia, et al. ∙

research

∙ 03/14/2021

Quasi-Equivalence Discovery for Zero-Shot Emergent Communication

Effective communication is an important skill for enabling information e...

Kalesha Bullard, et al. ∙

research

∙ 02/19/2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of ...

Manan Tomar, et al. ∙

research

∙ 02/14/2021

Domain Adversarial Reinforcement Learning

We consider the problem of generalization in reinforcement learning wher...

Bonnie Li, et al. ∙

research

∙ 02/11/2021

Multi-Task Reinforcement Learning with Context-based Representations

The benefit of multi-task learning over single-task learning relies on t...

Shagun Sodhani, et al. ∙

research

∙ 02/05/2021

Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs

Real-world knowledge graphs are often characterized by low-frequency rel...

Dora Jambor, et al. ∙

research

∙ 01/13/2021

COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction

The rapid spread of COVID-19 cases in recent months has strained hospita...

Anuroop Sriram, et al. ∙

research

∙ 12/30/2020

Unnatural Language Inference

Natural Language Understanding has witnessed a watershed moment with the...

Koustuv Sinha, et al. ∙

research

∙ 12/03/2020

Intervention Design for Effective Sim2Real Transfer

The goal of this work is to address the recent success of domain randomi...

Melissa Mozifian, et al. ∙

research

∙ 10/29/2020

Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Effective communication is an important skill for enabling information e...

Kalesha Bullard, et al. ∙

research

∙ 10/07/2020

Regularized Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) aims to facilitate a learner's abil...

Wonseok Jeon, et al. ∙

research

∙ 09/28/2020

Novelty Search in representational space for sample efficient exploration

We present a new approach for efficient exploration which leverages a lo...

Ruo Yu Tao, et al. ∙

research

∙ 08/26/2020

Constrained Markov Decision Processes via Backward Value Functions

Although Reinforcement Learning (RL) algorithms have found tremendous su...

Harsh Satija, et al. ∙

research

∙ 08/24/2020

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...

Prasanna Parthasarathi, et al. ∙

research

∙ 07/14/2020

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

Multi-task reinforcement learning is a rich paradigm where information f...

Amy Zhang, et al. ∙

research

∙ 07/06/2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootst...

Joshua Romoff, et al. ∙

research

∙ 07/03/2020

Deep interpretability for GWAS

Genome-Wide Association Studies are typically conducted using linear mod...

Deepak Sharma, et al. ∙

research

∙ 06/23/2020

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Adversarial imitation learning alternates between learning a discriminat...

Paul Barde, et al. ∙

research

∙ 05/07/2020

Plan2Vec: Unsupervised Representation Learning by Latent Plans

In this paper we introduce plan2vec, an unsupervised representation lear...

Ge Yang, et al. ∙

research

∙ 05/06/2020

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

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...

Iulian Vlad Serban, et al. ∙

research

∙ 05/05/2020

Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

We investigate how automated, data-driven, personalized feedback in a la...

Ekaterina Kochmar, et al. ∙

research

∙ 05/01/2020

Learning an Unreferenced Metric for Online Dialogue Evaluation

Evaluating the quality of a dialogue interaction between two agents is a...

Koustuv Sinha, et al. ∙

research

∙ 03/27/2020

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...

Joelle Pineau, et al. ∙

research

∙ 03/14/2020

Evaluating Logical Generalization in Graph Neural Networks

Recent research has highlighted the role of relational inductive biases ...

Koustuv Sinha, et al. ∙

research

∙ 03/13/2020

Interference and Generalization in Temporal Difference Learning

We study the link between generalization and interference in temporal-di...

Emmanuel Bengio, et al. ∙

research

∙ 03/12/2020

Invariant Causal Prediction for Block MDPs

Generalization across environments is critical to the successful applica...

Amy Zhang, et al. ∙

research

∙ 03/09/2020

Stable Policy Optimization via Off-Policy Divergence Regularization

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...

Ahmed Touati, et al. ∙

research

∙ 02/28/2020

The importance of transparency and reproducibility in artificial intelligence research

In their study, McKinney et al. showed the high potential of artificial ...

Benjamin Haibe-Kains, et al. ∙

research

∙ 02/24/2020

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

Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a re...

Wonseok Jeon, et al. ∙

research

∙ 02/07/2020

Provably efficient reconstruction of policy networks

Recent research has shown that learning poli-cies parametrized by large ...

Bogdan Mazoure, et al. ∙

research

∙ 01/31/2020

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Accurate reporting of energy and carbon usage is essential for understan...

Peter Henderson, et al. ∙

research

∙ 11/20/2019

Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

The ability to detect and track objects in the visual world is a crucial...

Eric Crawford, et al. ∙

research

∙ 11/19/2019

Online Learned Continual Compression with Stacked Quantization Module

We introduce and study the problem of Online Continual Compression, wher...

Lucas Caccia, et al. ∙

research

∙ 10/09/2019

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

Effective network congestion control strategies are key to keeping the I...

Viswanath Sivakumar, et al. ∙

Joelle Pineau

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