Portrait of Padideh Nouri

Padideh Nouri

PhD - Université de Montréal
Supervisor
Doina Precup
Co-supervisor
Yoshua Bengio
Research Topics
Computational Biology
Deep Learning
Probabilistic Models
Reinforcement Learning
Website
Google Scholar
GitHub

Publications

Relative Trajectory Balance is equivalent to Trust-PCL
Tristan Deleu
Padideh Nouri
Yoshua Bengio
Doina Precup
2025-09-01
ArXiv (preprint)
arxiv.org
arxiv.org
Revisiting Laplacian Representations for Value Function Approximation in Deep RL
Priyesh Vijayan
Padideh Nouri
Rishav
Sarath Chandar
Yash Chandak
Mathieu Reymond
Samira Ebrahimi Kahou
Doina Precup
Proto-value functions (PVFs) introduced Laplacian embeddings as an effective feature basis for value-function approximation; however, their … (see more)utility remained limited to small, fully known state spaces. Recent work has scaled Laplacian embeddings to high-dimensional inputs, using them for reward shaping and option discovery in goal-directed tasks, yet only as auxiliary signals, rather than directly using them as features for value functions. In this paper, we learn Laplacian eigenvectors online and employ them as features for Q-learning in 23 Atari games. We empirically demonstrate that these online–learned embeddings substantially improve model-free RL in large, high-dimensional domains. We demonstrate that enriching state representations with action embeddings yields additional gains under both behavior-policy and uniform-random policies. Additionally, we introduce the Fusion architecture, which augments the representation with useful inductive bias at the embedding level. To assess the usefulness of each embedding used in the Fusion architecture, we use Shapley values analysis.
2025-06-22
rl-conference.cc/RLC/2025/Workshop/IBRL (published)
openreview.net
openreview.net
Discrete Probabilistic Inference as Control in Multi-path Environments
Tristan Deleu
Padideh Nouri
Nikolay Malkin
Doina Precup
Yoshua Bengio
We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to … (see more)find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this problem for some distributions, it has been shown that in general, the distribution over states induced by the optimal policy may be biased in cases where there are multiple ways to generate the same object. To address this issue, Generative Flow Networks (GFlowNets) learn a stochastic policy that samples objects proportionally to their reward by approximately enforcing a conservation of flows across the whole Markov Decision Process (MDP). In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. We also prove that some flow-matching objectives found in the GFlowNet literature are in fact equivalent to well-established MaxEnt RL algorithms with a corrected reward. Finally, we study empirically the performance of multiple MaxEnt RL and GFlowNet algorithms on multiple problems involving sampling from discrete distributions.
2024-04-26
auai.org/UAI/2024/Conference (poster)
doi.org
openreview.net
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng
Padideh Nouri
Aneri Muni
Yoshua Bengio
Pierre-Luc Bacon
The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem… (see more) can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.
2022-09-13
ArXiv (preprint)
doi.org
arxiv.org