Reward Estimation for Variance Reduction in Deep Reinforcement Learning. - Joshua Romoff; Alexandre Piché; Peter Henderson 002; Vincent François-Lavet; Joelle Pineau (ICLR (Workshop), 2018)

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. - Rémi Le Priol; Alexandre Piché; Simon Lacoste-Julien (UAI, 2018)

Probabilistic Planning with Sequential Monte Carlo methods. - Alexandre Piché; Valentin Thomas; Cyril Ibrahim; Yoshua Bengio; Chris Pal (ICLR (Poster), 2019)

Iterative Amortized Policy Optimization. - Joseph Marino; Alexandre Piché; Alessandro Davide Ialongo; Yisong Yue (NeurIPS, 2021)

Beyond Target Networks: Improving Deep Q-learning with Functional Regularization. - Alexandre Piché; Joseph Marino; Gian Maria Marconi; Christopher Joseph Pal; Mohammad Emtiyaz Khan (CoRR, 2021)

Unsupervised Model-based Pre-training for Data-efficient Control from Pixels. - Sai Rajeswar; Pietro Mazzaglia; Tim Verbelen; Alexandre Piché; Bart Dhoedt; Aaron C. Courville; Alexandre Lacoste (CoRR, 2022)

Implicit Offline Reinforcement Learning via Supervised Learning. - Alexandre Piché; Rafael Pardinas; David Vázquez 001; Igor Mordatch; Chris Pal (CoRR, 2022)

Towards good validation metrics for generative models in offline model-based optimisation. - Christopher Beckham; Alexandre Piché; David Vázquez 001; Christopher Pal (CoRR, 2022)

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. - Sai Rajeswar; Pietro Mazzaglia; Tim Verbelen; Alexandre Piché; Bart Dhoedt; Aaron C. Courville; Alexandre Lacoste (ICML, 2023)

Can large language models build causal graphs? - Stephanie Long; Tibor Schuster; Alexandre Piché (CoRR, 2023)

Causal Discovery with Language Models as Imperfect Experts. - Stephanie Long; Alexandre Piché; Valentina Zantedeschi; Tibor Schuster; Alexandre Drouin (CoRR, 2023)

Bridging the Gap Between Target Networks and Functional Regularization. - Alexandre Piché; Valentin Thomas; Joseph Marino; Rafael Pardinas; Gian Maria Marconi; Christopher Pal; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

LLMs can learn self-restraint through iterative self-reflection. - Alexandre Piché; Aristides Milios; Dzmitry Bahdanau; Chris Pal (CoRR, 2024)

TapeAgents: a Holistic Framework for Agent Development and Optimization. - 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; et al. (CoRR, 2024)

Exploring validation metrics for offline model-based optimisation with diffusion models. - Christopher Beckham; Alexandre Piché; David Vázquez 001; Christopher Pal (Trans. Mach. Learn. Res., 2024)

Self-Evolving Curriculum for LLM Reasoning. - Xiaoyin Chen; Jiarui Lu; Minsu Kim 004; Dinghuai Zhang; Jian Tang 005; Alexandre Piché; Nicolas Gontier; Yoshua Bengio; Ehsan Kamalloo (CoRR, 2025)

How to Train Your LLM Web Agent: A Statistical Diagnosis. - Dheeraj Vattikonda; Santhoshi Ravichandran; Emiliano Penaloza; Hadi Nekoei; Megh Thakkar; Thibault Le Sellier de Chezelles; Nicolas Gontier; Miguel Muñoz-Mármol; Sahar Omidi Shayegan; Stefania Raimondo; Xue Liu; Alexandre Drouin; et al. (CoRR, 2025)

BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning. - Ahmed Masry; Abhay Puri; Masoud Hashemi; Juan A. Rodríguez; Megh Thakkar; Khyati Mahajan; Vikas Yadav; Sathwik Tejaswi Madhusudhan; Alexandre Piché; Dzmitry Bahdanau; Christopher Pal; David Vázquez 001; et al. (CoRR, 2025)