Alex Piché

Alex Piché

Senior research scientist

Publications

You can also find my articles on my Google Scholar profile.


  1. Piché, A., Milios, A., Bahdanau, D., & Pal, C. (2024). LLMs can learn self-restraint through iterative self-reflection. arXiv preprint arXiv:2405.13022.
  2. Long, S., Schuster, T., & Piché, A. (2023). Can large language models build causal graphs. arXiv preprint arXiv:2303.05279.
  3. Long, S., Piché, A., Zantedeschi, V., Schuster, T., & Drouin, A. (2023). Causal discovery with language models as imperfect experts. arXiv preprint arXiv:2307.02390.
  4. Rajeswar, S., Mazzaglia, P., Verbelen, T., Piché, A., Dhoedt, B., Courville, A., & Lacoste, A. (2023). Mastering the unsupervised reinforcement learning benchmark from pixels. International Conference on Machine Learning.
  5. Beckham, C., Piche, A., Vázquez, D., & Pal, C. (2022). Towards good validation metrics for generative models in offline model-based optimisation. arXiv preprint arXiv:2211.10747.
  6. Piche, A., Pardinas, R., Vazquez, D., Mordatch, I., & Pal, C. (2022). Implicit offline reinforcement learning via supervised learning. arXiv preprint arXiv:2210.12272.
  7. Piche, A., Thomas, V., Marino, J., Pardinas, R., Marconi, G., Pal, C., & Khan, M. (2022). Bridging the Gap Between Target Networks and Functional Regularization. TMLR.
  8. Rajeswar, S., Mazzaglia, P., Verbelen, T., Piché, A., Dhoedt, B., Courville, A., & Lacoste, A. (2022). Unsupervised model-based pre-training for data-efficient reinforcement learning from pixels. ICML2022: the 39th International Conference on Machine Learning.
  9. Marino, J., Piché, A., Ialongo, A., & Yue, Y. (2021). Iterative amortized policy optimization. Advances in Neural Information Processing Systems.
  10. Piché, A., Thomas, V., Ibrahim, C., Bengio, Y., & Pal, C. (2019). Probabilistic Planning with Sequential Monte Carlo methods. International Conference on Learning Representations.
  11. Le Priol, R., Piché, A., & Lacoste-Julien, S. (2018). Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields.. UAI.
  12. Romoff, J., Henderson, P., Piché, A., Francois-Lavet, V., & Pineau, J. (2018). Reward estimation for variance reduction in deep reinforcement learning. arXiv preprint arXiv:1805.03359.