Portrait of Mohsin Hasan

Mohsin Hasan

PhD - Université de Montréal
Supervisor
Yoshua Bengio
Research Topics
Dynamical Systems
Generative Models
Probabilistic Models
Website
Google Scholar
GitHub

Publications

Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
openreview.net
Discrete Feynman-Kac Correctors
Mohsin Hasan
Marta Skreta
Alan Aspuru-Guzik
Yoshua Bengio
Kirill Neklyudov
The performance of Large Language Models (LLMs) directly depends on the size of the context that the model was trained on. Despite significa… (see more)nt progress in increasing the context size of the current models, some applications remain bottlenecked by the number of processed tokens at inference time. A particular mathematical problem LLMs can be used for is inferring parameters in a statistical model, given data-points as input. Here we make a case demonstrating that discrete diffusion models offer a promising avenue for scaling such parameter prediction tasks, by combining the outputs of the same model evaluated on different parts of the training data. We propose Discrete Fenyman-Kac Correctors --- a framework that allows for controlling the generated distribution of discrete masked diffusion models at inference time. We derive Sequential Monte Carlo (SMC) algorithms that, given a trained discrete diffusion model, sample from its annealed distribution or the product of distributions with different conditions. Notably, our framework does not require any training, finetuning and external reward functions. Finally, we apply our framework to amortized linear regression using LLaDA and demonstrate that it drastically outperforms the standard inference procedure in terms of accuracy and adherence to prompt format.
2025-07-09
ICML.cc/2025/Workshop/AI4MATH (poster)
openreview.net
openreview.net
Discrete Feynman-Kac Correctors
Mohsin Hasan
Viktor Ohanesian
Artem Gazizov
Yoshua Bengio
Alan Aspuru-Guzik
Roberto Bondesan
Marta Skreta
Kirill Neklyudov
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences.… (see more) Sample generation via gradual denoising or demasking processes allows them to capture hierarchical non-sequential interdependencies in the data. These custom processes, however, do not assume a flexible control over the distribution of generated samples. We propose Discrete Feynman-Kac Correctors, a framework that allows for controlling the generated distribution of discrete masked diffusion models at inference time. We derive Sequential Monte Carlo (SMC) algorithms that, given a trained discrete diffusion model, control the temperature of the sampled distribution (i.e. perform annealing), sample from the product of marginals of several diffusion processes (e.g. differently conditioned processes), and sample from the product of the marginal with an external reward function, producing likely samples from the target distribution that also have high reward. Notably, our framework does not require any training of additional models or fine-tuning of the original model. We illustrate the utility of our framework in several applications including: efficient sampling from the annealed Boltzmann distribution of the Ising model, improving the performance of language models for code generation and amortized learning, as well as reward-tilted protein sequence generation.
2025-07-08
ICML.cc/2025/Workshop/AI4MATH (poster)
doi.org
openreview.net
Solving Bayesian inverse problems with diffusion priors and off-policy RL
Luca Scimeca
Siddarth Venkatraman
Moksh J. Jain
Minsu Kim
Marcin Sendera
Mohsin Hasan
Luke Rowe
Sarthak Mittal
Pablo Lemos
Emmanuel Bengio
Alexandre Adam
Jarrid Rector-Brooks
Yashar Hezaveh
Laurence Perreault-Levasseur
Yoshua Bengio
Glen Berseth
Nikolay Malkin
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (see more)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.
2025-03-06
ICLR.cc/2025/Workshop/DeLTa (poster)
doi.org
openreview.net
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-02-10
ArXiv (preprint)
doi.org
arxiv.org
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-02-10
ArXiv (preprint)
arxiv.org
arxiv.org
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-02-10
ArXiv (preprint)
doi.org
arxiv.org
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks
Mohsin Hasan
Zhangzhi Peng
Zachary Quinn
Cheng-Hao Liu
Sarthak Mittal
nouha Dziri
Michael M. Bronstein
Yoshua Bengio
Pranam Chatterjee
Alexander Tong
Joey Bose
2025-01-01
ICLR (published)
doi.org
arxiv.org
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks
Mohsin Hasan
Zhangzhi Peng
Zachary Quinn
Cheng-Hao Liu
Sarthak Mittal
nouha Dziri
Michael M. Bronstein
Yoshua Bengio
Pranam Chatterjee
Alexander Tong
Joey Bose
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (see more)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
2024-10-10
ArXiv (preprint)
doi.org
arxiv.org
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks
Mohsin Hasan
Zhangzhi Peng
Zachary Quinn
Cheng-Hao Liu
Sarthak Mittal
nouha Dziri
Michael M. Bronstein
Yoshua Bengio
Pranam Chatterjee
Alexander Tong
Joey Bose
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (see more)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
2024-10-10
ArXiv (preprint)
doi.org
arxiv.org
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks
Mohsin Hasan
Zhangzhi Peng
Zachary Quinn
Cheng-Hao Liu
Sarthak Mittal
nouha Dziri
Michael M. Bronstein
Yoshua Bengio
Pranam Chatterjee
Alexander Tong
Joey Bose
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (see more)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
2024-10-10
ArXiv (preprint)
doi.org
arxiv.org