Portrait of Danyal REHMAN is unavailable

Danyal REHMAN

Postdoctorate - Université de Montréal
Supervisor
Yoshua Bengio
Research Topics
Deep Learning
Generative Models
Molecular Modeling
Reinforcement Learning
Representation Learning
GitHub

Publications

Autoregressive Boltzmann Generators
Danyal REHMAN
Charlie B. Tan
Yoshua Bengio
Joey Bose
Alexander Tong
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driv… (see more)en the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction. However, modern BGs predominantly rely on normalizing flows (NFs), which either suffer from limited expressivity due to strict invertibility constraints (discrete time) or computationally expensive likelihoods (continuous time). In this paper, we propose Autoregressive Boltzmann Generators (ArBG), a novel autoregressive modelling framework that overcomes these limitations by departing from the flow-based BG paradigm. ArBG circumvents the topological constraints of flows and enables sequential inference-time interventions, while offering enhanced scalability by leveraging architectures effective in Large Language Models. We empirically demonstrate that ArBG leads to significant improvements over flow-based models across all benchmarks, but particularly in larger peptide systems such as the 10-residue Chignolin. Furthermore, we introduce Robin, a 132 million parameter transferable model trained with the ArBG framework which improves over the previous state-of-the-art, reducing the zero-shot energy error,
2026-03-01
GEM @ International Conference on Learning Representations (published)
openreview.net
openreview.net
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal REHMAN
Tara Akhound-Sadegh
Artem Gazizov
Yoshua Bengio
Alexander Tong
2025-11-30
arXiv (published)
doi.org
arxiv.org
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Danyal REHMAN
Oscar Davis
Jiarui Lu
Jian Tang
Michael Bronstein
Yoshua Bengio
Alexander Tong
Avishek Joey Bose
Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to large… (see more)-scale diffusion and flow matching models. However, such modern generative models suffer from expensive inference, inhibiting their use in numerous scientific applications like Boltzmann Generators (BGs) for molecular conformations that require fast likelihood evaluation. In this paper, we revisit classical normalizing flows in the context of BGs that offer efficient sampling and likelihoods, but whose training via maximum likelihood is often unstable and computationally challenging. We propose Regression Training of Normalizing Flows (RegFlow), a novel and scalable regression-based training objective that bypasses the numerical instability and computational challenge of conventional maximum likelihood training in favour of a simple
2025-06-10
ICML.cc/2025/Workshop/GenBio (spotlight)
doi.org
openreview.net