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2020 – today
- 2024
[j2]Christopher Beckham, Alexandre Piché, David Vázquez, Christopher Pal:
Exploring validation metrics for offline model-based optimisation with diffusion models. Trans. Mach. Learn. Res. 2024 (2024)- [i11]Alexandre Piché, Aristides Milios, Dzmitry Bahdanau, Chris Pal:
LLMs can learn self-restraint through iterative self-reflection. CoRR abs/2405.13022 (2024) - [i10]Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang, Ehsan Kamalloo, Rafael Pardinas, Alexandre Piché, Torsten Scholak, Oleh Shliazhko, Jordan Prince Tremblay, Karam Ghanem, Soham Parikh, Mitul Tiwari, Quaizar Vohra:
TapeAgents: a Holistic Framework for Agent Development and Optimization. CoRR abs/2412.08445 (2024) - 2023
- [j1]Alexandre Piché, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan:
Bridging the Gap Between Target Networks and Functional Regularization. Trans. Mach. Learn. Res. 2023 (2023) - [c6]Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste:
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. ICML 2023: 28598-28617 - [i9]Stephanie Long, Tibor Schuster, Alexandre Piché:
Can large language models build causal graphs? CoRR abs/2303.05279 (2023) - [i8]Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin:
Causal Discovery with Language Models as Imperfect Experts. CoRR abs/2307.02390 (2023) - 2022
- [i7]Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste:
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels. CoRR abs/2209.12016 (2022) - [i6]Alexandre Piché, Rafael Pardinas, David Vázquez, Igor Mordatch, Chris Pal:
Implicit Offline Reinforcement Learning via Supervised Learning. CoRR abs/2210.12272 (2022) - [i5]Christopher Beckham, Alexandre Piché, David Vázquez, Christopher Pal:
Towards good validation metrics for generative models in offline model-based optimisation. CoRR abs/2211.10747 (2022) - 2021
- [c5]Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue:
Iterative Amortized Policy Optimization. NeurIPS 2021: 15667-15681 - [i4]Alexandre Piché, Joseph Marino, Gian Maria Marconi, Christopher Joseph Pal, Mohammad Emtiyaz Khan:
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization. CoRR abs/2106.02613 (2021) - 2020
- [i3]Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue:
Iterative Amortized Policy Optimization. CoRR abs/2010.10670 (2020)
2010 – 2019
- 2019
- [c4]Alexandre Piché, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal:
Probabilistic Planning with Sequential Monte Carlo methods. ICLR (Poster) 2019 - 2018
- [c3]Joshua Romoff, Peter Henderson, Alexandre Piché, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. CoRL 2018: 674-699 - [c2]Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. ICLR (Workshop) 2018 - [c1]Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien:
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. UAI 2018: 815-824 - [i2]Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. CoRR abs/1805.03359 (2018) - 2017
- [i1]Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien:
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. CoRR abs/1712.08577 (2017)
Coauthor Index
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