Portrait of Younesse Kaddar

Younesse Kaddar

Collaborating Alumni - Université de Montréal
Supervisor
Yoshua Bengio
Research Topics
Deep Learning
Generative Models
Learning to Program
Probabilistic Models
Reasoning
Website
GitHub

Publications

Can a Bayesian Oracle Prevent Harm from an Agent?
Yoshua Bengio
Michael K. Cohen
Nikolay Malkin
Matt MacDermott
Damiano Fornasiere
Pietro Greiner
Younesse Kaddar
Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the … (see more)long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the i.i.d. case and in the non-i.i.d. case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.
2025-07-11
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence (published)
proceedings.mlr.press
Can a Bayesian Oracle Prevent Harm from an Agent?
Yoshua Bengio
Michael K. Cohen
Nikolay Malkin
Matt MacDermott
Damiano Fornasiere
Pietro Greiner
Younesse Kaddar
Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the … (see more)long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the iid case and in the non-iid case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.
2025-05-07
auai.org/UAI/2025/Conference (poster)
doi.org
openreview.net
Amortizing intractable inference in large language models
Edward J Hu
Moksh J. Jain
Eric Elmoznino
Younesse Kaddar
Guillaume Lajoie
Yoshua Bengio
Nikolay Malkin
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (see more)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.
2024-01-16
ICLR.cc/2024/Conference (oral)
doi.org
openreview.net
Amortizing intractable inference in large language models
Edward J Hu
Moksh J. Jain
Eric Elmoznino
Younesse Kaddar
Guillaume Lajoie
Yoshua Bengio
Nikolay Malkin
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (see more)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.
2023-10-06
ArXiv (preprint)
doi.org
arxiv.org