Joelle Pineau

Policy-contingent abstraction for robust robot control

基于策略抽象的鲁棒机器人控制

Joelle Pineau, Geoffrey Gordon, Sebastian Thrun

AI总结 提出一种可扩展的控制算法，使移动机器人系统在充分考虑概率信念的情况下做出高层决策，并成功部署于护理机构。

Comments Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)

Tiny Aya: Bridging Scale and Multilingual Depth

Tiny Aya：弥合规模与多语言深度

Alejandro R. Salamanca, Diana Abagyan, Daniel D'souza, Ammar Khairi, David Mora, Saurabh Dash, Viraat Aryabumi, Sara Rajaee, Mehrnaz Mofakhami, Ananya Sahu, Thomas Euyang, Brittawnya Prince, Madeline Smith, Hangyu Lin, Acyr Locatelli, Sara Hooker, Tom Kocmi, Aidan Gomez, Ivan Zhang, Phil Blunsom, Nick Frosst, Joelle Pineau, Beyza Ermis, Ahmet Üstün, Julia Kreutzer, Marzieh Fadaee

AI总结 Tiny Aya通过区域感知后训练，在仅3.35B参数下实现多语言翻译、理解和生成的高性能，提供高效且平衡的多语言AI解决方案。

Advancing Science- and Evidence-based AI Policy

Rishi Bommasani, Sanjeev Arora, Jennifer Chayes, Yejin Choi, Mariano-Florentino Cuéllar, Li Fei-Fei, Daniel E. Ho, Dan Jurafsky, Sanmi Koyejo, Hima Lakkaraju, Arvind Narayanan, Alondra Nelson, Emma Pierson, Joelle Pineau, Scott Singer, Gaël Varoquaux, Suresh Venkatasubramanian, Ion Stoica, Percy Liang, Dawn Song

Comments This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on July 31, 2025

Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation

Mohamad H. Danesh, Maxime Wabartha, Stanley Wu, Joelle Pineau, Hsiu-Chin Lin

On the Societal Impact of Open Foundation Models

Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, Arvind Narayanan

机构 * Princeton University（普林斯顿大学） ； Stanford University（斯坦福大学） ； Massachusetts Institute of Technology（麻省理工学院） ； Georgetown University（乔治城大学） ； Center for Democracy and Technology（民主与技术中心） ； Eleuther AI ； Humane Intelligence ； Brookings Institution（布鲁金斯学会） ； Hugging Face ； Australian National University（澳大利亚国立大学） ； Open Source Initiative（开源倡议组织） ； Institute for Advanced Study（高等研究院） ； Meta ； University of California, Berkeley（加州大学伯克利分校） ； Mozilla AI 纠错

Questions Are All You Need to Train a Dense Passage Retriever

Devendra Singh Sachan, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, Manzil Zaheer

机构 * McGill University（麦吉尔大学） ； Mila - Quebec AI Institute（米拉-魁北克人工智能研究所） ； Meta AI ； DeepMind（深度思维） ； University of Washington（华盛顿大学） 纠错

Comments Accepted to TACL, pre MIT Press publication version

Improving Passage Retrieval with Zero-Shot Question Generation

Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer

机构 * McGill University（麦吉尔大学） ； Mila - Quebec AI Institute（米拉-魁北克人工智能研究所） ； Meta AI Research（Meta人工智能研究院） ； Google Research（谷歌研究院） ； University of Washington（华盛顿大学） 纠错

Comments EMNLP 2022 camera-ready version. Code is available at: https://github.com/DevSinghSachan/unsupervised-passage-reranking

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

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

Comments Published in JMLR: https://jmlr.org/papers/v21/20-312.html

The Curious Case of Absolute Position Embeddings

Koustuv Sinha, Amirhossein Kazemnejad, Siva Reddy, Joelle Pineau, Dieuwke Hupkes, Adina Williams

Comments Accepted at EMNLP 2022 Findings; 5 pages and 15 pages Appendix

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky

Comments Accepted at ICLR 2022. Code available at www.github.com/pclucas14/AML

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky

Comments This has been withdrawn as it is a new version of arXiv:2104.05025

Novelty Search in Representational Space for Sample Efficient Exploration

Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau

Comments 10 pages + references + appendix. Oral presentation at NeurIPS 2020

Robust Policy Learning over Multiple Uncertainty Sets

Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang

Comments Project webpage at https://sites.google.com/view/sirsa-public/home

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

Martin Cousineau, Vedat Verter, Susan A. Murphy, Joelle Pineau

Comments In Press, Corrected Proof

Journal ref European Journal of Operational Research, 2022, 14 pages

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

Anthony GX-Chen, Veronica Chelu, Blake A. Richards, Joelle Pineau

Comments 18 pages, 6 figures, 2 tables. Preprint. Accepted by AAAI-22

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

Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche

Block Contextual MDPs for Continual Learning

Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang

Comments 26pages, Under Review

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

Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, Douwe Kiela

Comments To appear at EMNLP 2021; 26 pages total (9 main, 6 reference and 11 Appendix)

Quasi-Equivalence Discovery for Zero-Shot Emergent Communication

Kalesha Bullard, Douwe Kiela, Franziska Meier, Joelle Pineau, Jakob Foerster

Comments 14 pages

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

Jongmin Lee, Wonseok Jeon, Byung-Jun Lee, Joelle Pineau, Kee-Eung Kim

Comments 26 pages, 11 figures, Accepted at ICML 2021

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

Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar

Comments Accepted at SIGDial 2021. arXiv admin note: substantial text overlap with arXiv:2008.10427

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

Prasanna Parthasarathi, Mohamed Abdelsalam, Joelle Pineau, Sarath Chandar

Comments Accepted at SIGDial 2021

SPeCiaL: Self-Supervised Pretraining for Continual Learning

Lucas Caccia, Joelle Pineau

Multi-Task Reinforcement Learning with Context-based Representations

Shagun Sodhani, Amy Zhang, Joelle Pineau

Comments Accepted at the 38th International Conference on Machine Learning (ICML 2021). 17 pages, 4 figures, 20 tables

UnNatural Language Inference

Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, Adina Williams

Comments Accepted at ACL 2021 (Long Paper), 9 pages + Appendix

Correcting Momentum in Temporal Difference Learning

Emmanuel Bengio, Joelle Pineau, Doina Precup

Comments NeurIPS Deep RL Workshop 2020

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau

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

Viswanath Sivakumar, Olivier Delalleau, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel

Comments Workshop on ML for Systems at NeurIPS 2019

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Christopher Pal, Derek Nowrouzezahrai

Journal ref Advances in Neural Information Processing Systems 33 (2020)

Sometimes We Want Translationese

Prasanna Parthasarathi, Koustuv Sinha, Joelle Pineau, Adina Williams

Comments 16 pages, 11 figures and 3 tables