Joelle Pineau

2020

Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Board, Levi Waldron, Bo Wang, Chris McIntosh, Anshul Kundaje, Casey S Greene, Michael M Hoffman, Jeffrey T Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John Ioannidis, John Quackenbush, Hugo JWL Aerts. The importance of transparency and reproducibility in artificial intelligence research. Nature. 2020.

Nathan Peifer-Smadja, Redwan Maatoug, François-Xavier Lescure, Eric D’Ortenzio, Joelle Pineau and Jean-Rémi King. Machine Learning for COVID-19 needs global collaboration and data-sharing. Nature Machine Intelligence. 2020.

Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John P Kemp, Maria Nethander, Daniel Evans, John A Morris, Douglas P Kiel, Fernando Rivadeneira, Helena Johansson, Nicholas C Harvey, Dan Mellström, Magnus Karlsson, Cyrus Cooper, David M Evans, Robert Clarke, John A Kanis, Eric Orwoll, Eugene V McCloskey, Claes Ohlsson, Joelle Pineau, William D Leslie, Celia MT Greenwood, J Brent Richards. Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study. PLoS medicine 17 (7). 2020.

Ximeng Mao, Joelle Pineau, Roy Keyes, Shirin A Enger. RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy via Deep Learning. International Journal of Radiation Oncology Biology Physics. 2020

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. JMLR. 21(248), pp.1−43.

Koustuv Sinha, Joelle Pineau, Jessica Forde, Rosemary Nan Ke, Hugo Laorchelle. Neurips 2019 Reproducibility Challenge. A special issue of the journal ReScience C 6(2). 2020.

Clare Lyle, Amy Zhang, Angelos Filos, Shagun Sodhani, Marta Kwiatkowska, Yarin Gal, Doina Precup, Joelle Pineau. Invariant Causal Prediction for Block MDPs. ICML 2020.

Harsh Satija, Philip Amortila, Joelle Pineau. Constrained Markov Decision Processes via Backward Value Functions. ICML 2020.

Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau. Online Learned Continual Compression with Adaptive Quantization Module. ICML 2020.

Emmanuel Bengio, Joelle Pineau, Doina Precup. Interference and Generalization in Temporal Difference Learning. Submitted and accepted to ICML 2020.

Maxime Wabartha, Audrey Durand, Vincent François-Lavet, Joelle Pineau. Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks. IJCAI 2020.

Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent. Stable Policy Optimization via Off-Policy Divergence Regularization. UAI 2020.

Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L Hamilton, Joelle Pineau. Learning an Unreferenced Metric for Online Dialogue Evaluation. ACL 2020.

Ge Yang, Amy Zhang, Ari Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra. Plan2Vec: Unsupervised Representation Learning by Latent Plans. Learning for Dynamics and Control (L4DC) 2020.

Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung D Vu, Robert Belfer, Joelle Pineau, Aaron Courville, Laurent Charlin, Yoshua Bengio. A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. AIED 2020.

Ekaterina Kochmar, Dung D Vu, Robert Belfer, Varun Gupta, Iulian V Serban, Joelle Pineau. Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. AIED 2020.

R.Y. (David) Tao, Vincent Francois-Lavet, Joelle Pineau. Novelty Search in Representational Space for Sample Efficient Exploration. NeurIPS 2020. Oral presentation (1% of submissions).

Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai. Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization. NeurIPS 2020. Spotlight presentation (4% of submissions).

2019

I.V. Serban, C. Sankar, M. Pieped, J. Pineau, Y. Bengio. “The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach”. Journal of Machine Learning Research (JMLR). Accepted.

V. François-Lavet, G. Rabusseau, J. Pineau, D. Ernst, R. Fontaineau. “On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability”. Journal of AI Research (JAIR). Vol.65. pp.1-30. 2019.

A.M.Froomkin, I. Kerr, J. Pineau. “When AIs outperform doctors: Confronting the challenges of a tort-induced over-reliance on machine learning”. Arizona Law Review, vol.61:33. 2019.

P. Paquette, Y. Lu, S. Bocco, M.O. Smith, S. Ortiz-Gagne, J. K. Kummerfeld, S. Singh, J. Pineau, A. Courville. “No Press Diplomacy: Modeling Multi-Agent Gameplay”. NeurIPS 2019.

M. Assran, J. Romoff, N. Ballas, J. Pineau, M. Rabbat. “Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning”. NeurIPS 2019.

J. Romoff, P. Henderson, A. Touati, E. Brunskill, J. Pineau, Y. Ollivier, “Separable value functions across time-scales”. ICML 2019.

A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, J. Pineau, “TarMAC: Targeted Multi-Agent Communication”. ICML 2019.

K. Sinha, S. Sodhani, J. Dong, J. Pineau, W. L. Hamilton. “CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text”. EMNLP 2019.

B. Mazoure, T. Doan, A. Durand, R.D. Helm, J. Pineau. “Leveraging exploration in off-policy algorithms via normalizing flows”. CoRL 2019

L. Caccia, H. van Hoof, A. Courville, J. Pineau. “Deep Generative Modeling of LiDAR Data”. IROS 2019.

R. Lowe, J. Foerster, Y-L. Boureau, J. Pineau, Y. Dauphin. “On the Pitfalls of Measuring Emergent Communication”. AAMAS 2019.

J. Pineau, K. Sinha, G. Fried, R.N. Ke, H. Larochelle (guest editors). ReScience Journal, vol.5(2). Special Issue on the ICLR Reproducibility Challenge 2019.

2018

V. Francois-Lavet, P. Henderson, R. Islam, M. Bellemare, J. Pineau. "An Introduction to Deep Reinforcement Learning”. Foundations and Trends in Machine Learning. 11 (3-4). pp.219-354. 2018.

I. V. Serban, R. Lowe, P. Henderson, L. Charlin, J. Pineau. "A Survey of Available Corpora for Building Data-Driven Dialogue Systems: The Journal Version”. Dialogue & Discourse. 9 (1). pp.1-49. 2018.

A. Durand, O. Maillard, J. Pineau. "Streaming kernel regression with provably adaptive mean, variance, and regularization”. Journal of Machine Learning Research. 19. pp.1-34. 2018.

P. Henderson,R. Islam, P. Bachman, J. Pineau, D. Precup, D. Meger."Deep Reinforcement Learning that Matters”. AAAI. 7 pages. 2018.

P. Henderson, W-D. Chang, P.L. Bacon, D. Meger, J. Pineau, D. Precup. "OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning”. AAAI. 7 pages. 2018.

P. Henderson, K. Sinha, N. Angelard-Gontier, N.R. Ke, G. Fried, R. Lowe, J. Pineau. "Ethical Challenges in Data-Driven Dialogue Systems”. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society. 7 pages. 2018.

M. Smith, H. van Hoof, J. Pineau. "An Inference-Based Policy Gradient Method for Learning Options”. ICML. 8 pages. 2018.

A. Durand, C. Achilleos, D. Iacovides, K. Strati, T. Mitsis, J. Pineau. "Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis”. Machine Learning for Healthcare. pp.67-82 2018.

P. Thodoro, A. Durand, J. Pineau, D. Precup. "Temporal Regularization for Markov Decision Processes”. NeurIPS (formerly NIPS). 8 pages. 2018.

P. Parthasarathi, J. Pineau. "Extending Neural Generative Conversational Model using External Knowledge Sources”. EMNLP. 6 pages. 2018.

J. Romo, P. Henderson, A. Piche, V. Francois-Lavet, J. Pineau. "Reward Estimation forVariance Reduction in Deep Reinforcement Learning”. International Conference on Robot Learning (CoRL). 11 pages. 2018.

P. Henderson, J. Romo, J. Pineau. "Where Did My OptimumGo?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods”. EWRL. 2018.

A. Touati, H. Satija, J. Romo, J. Pineau, P. Vincent. "Randomized Value Functions via Multiplicative Normalizing Flows”. 8 pages. EWRL. 2018.

2017

M. Ghorbel, J. Pineau, R. Gourdeau, S. Javdani, S. Srinivasa. “A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair”. In. Journal of Social Robotics. pp. 1-15. 2017.

R. Lowe, N. Pow, I.V. Serban, L. Charlin, C-W. Liu J. Pineau. “Training end-to-end dialogue systems with the ubuntu dialogue corpus”. In. Dialogue & Discourse. pp. 31-65. 2017.

A. Emami, J El Youssef, R Rabasa-Lhoret, J Pineau, JR Castle, A Haidar. “Modeling Glucagon Action in Patients with Type 1 Diabetes”. IEEE journal of biomedical and health informatics 21 (4), 1163-1171. 2017.

W. Choi, O. Cyens, T. Chan, M. Schijven, S. Lajoie, M.E. Mancini, P. Dev, L. Fellander-Tsai, M. Ferland, P. Kato, J. Lau, M. Montonaro, J. Pineau, R. Aggarwal. “Engagement and Learning in Simulation: Recommendations of the Simnovate Engaged Learning Domain Group”. BMJ Simulation & Technology Enhanced Learning. 2017

R. Lowe, M. Noseworthy, I.V. Serban, N. Angelard-Gontier, E. Bengio, J. Pineau. “Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses”. Association for Computational Linguistics (ACL). 2017. Outstanding paper track (1.5% of submissions).

G. Rabusseau, B. Balle, J. Pineau. “Multitask Spectral Learning of Weighted Automata”. Neural Information Processing Systems (NIPS). 2017.

D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, Y. Bengio. “An Actor-Critic Algorithm for Sequence Prediction”. International Conference on Learning Representations (ICLR). 2017.

I.V. Serban, A. Sordoni, R. Lowe, L. Charlin, J. Pineau, A. Courville, Y. Bengio.“A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues”. Association for the Advancement of Artificial Intelligence (AAAI). 2017.

I.V. Serban, R. Lowe, L. Charlin, J. Pineau. “Generative Deep Neural Networks for Dialogue: A Short Review”. Empirical Methods in Natural Language Processing (EMNLP). 2017.

I.V. Serban, A.G. Ororbia II, J. Pineau, A. Courville. “Piecewise Latent Variables for Neural Variational Text Processing”. Empirical Methods in Natural Language Processing (EMNLP). 2017.

M. Noseworthy, J.C.K. Cheung, J. Pineau. “Predicting Success in Goal-Driven Human-Human Dialogues”. SIGdial Meeting on Discourse and Dialogue (SIGdial). 2017.

H.P. Truong, P. Parthasarathi, J. Pineau. “MACA: A Modular Architecture for Conversational Agents”. SIGdial Meeting on Discourse and Dialogue (SIGDIAL). 2017.

M. Smith, L. Charlin, J. Pineau. “A Sparse Probabilistic Model of User Preference Data”. Canadian Conference on Artificial Intelligence (CAIAC). 2017.

E. Bengio, V. Thomas, J. Pineau, D. Precup, Y. Bengio. “Independently Controllable Features” Reinforcement Learning and Decision Making (RLDM). arXiv: 1708.01289. 2017.

I.V. Serban, C. Sankar, M. Germain, S. Zhang, Z. Lin, S. Subramanian, T. Kim, M. Pieper, S. Chandar, N. Ke, S. Rajeswar, A. Brebisson, J.M.R. Sotelo, D. Suhubdy, V. Michalski, A. Nguyen, J. Pineau, Y. Bengio. “A Deep Reinforcement Learning Chatbot (Short Version)”. Neural Information Processing Systems (NIPS) Workshop on Conversational AI. 2017.

X. Cao, G. Rabusseau, J. Pineau. “Tensor Regression Networks with various Low-Rank Tensor Approximations. arXiv: 1712.09520. 2017.

A. Goyal, N.R. Ke, A. Lamb, C. Pal, J. Pineau, Y. Bengio. “ACtuAL: Actor-Critic Under Adversarial Learning” arXiv: 1711.04755. 2017.

A. Durand, O-A. Maillard, J. Pineau. “Streaming kernel regression with provably adaptive mean, variance, and regularization” arXiv: 1708.00768. 2017.

P. Henderson,R. Islam, P. Bachman, J. Pineau, D. Precup, D. Meger.“Deep Reinforcement Learning that Matters”. arXiv: 1709.06560. (Accepted at AAAI 2018.)

P. Henderson, W-D. Chang, P.L. Bacon, D. Meger, J. Pineau, D. Precup. “OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning”. arXiv: 1709.06683. (Accepted at AAAI 2018.)

P. Henderson, K. Sinha, N. Angelard-Gontier, N.R. Ke, G. Fried, R. Lowe, J. Pineau. “Ethical Challenges in Data-Driven Dialogue Systems”. arXiv: 1711.09050. (Accepted at AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society. 2018.)