Portrait of Mohammad Pedramfar is unavailable

Mohammad Pedramfar

Postdoctorate - McGill University
Supervisor
Siamak Ravanbakhsh
Research Topics
Deep Learning
Optimization
Reinforcement Learning

Publications

The Role of Symmetry in Optimizing Overparameterized Networks
Kusha Sareen
Mohammad Pedramfar
Sékou-Oumar Kaba
Mehran Shakerinava
Siamak Ravanbakhsh
Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely un… (see more)derstood. We analyze weight-space symmetries in neural networks and show that overparameterization introduces additional symmetries that benefit optimization in two distinct ways. First, we prove that these symmetries act as a form of diagonal preconditioning on the Hessian, enabling the existence of better-conditioned minima within each equivalence class of functionally identical solutions. Second, we show that overparameterization increases the probability mass of global minima near typical initializations, making these favorable solutions more reachable. Teacher-student network experiments validate our theoretical predictions: as width increases, the Hessian trace decreases, condition numbers improve, and convergence accelerates. Our analysis provides a unified framework for understanding overparameterization and width growth as a geometric transformation of the loss landscape.
2026-04-27
arXiv (preprint)
doi.org
arxiv.org
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Vineet Jain
Kusha Sareen
Mohammad Pedramfar
Siamak Ravanbakhsh
Adapting a pretrained diffusion model to new objectives at inference time remains an open problem in generative modeling. Existing steering … (see more)methods suffer from inaccurate value estimation, especially at high noise levels, which biases guidance. Moreover, information from past runs is not reused to improve sample quality, resulting in inefficient use of compute. Inspired by the success of Monte Carlo Tree Search, we address these limitations by casting inference-time alignment as a search problem that reuses past computations. We introduce a tree-based approach that samples from the reward-aligned target density by propagating terminal rewards back through the diffusion chain and iteratively refining value estimates with each additional generation. Our proposed method, Diffusion Tree Sampling (DTS), produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant, Diffusion Tree Search (DTS
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Multi-Armed Sampling Problem and the End of Exploration
Mohammad Pedramfar
Siamak Ravanbakhsh
This paper introduces the framework of multi-armed sampling, as the sampling counterpart to the optimization problem of multi-arm bandits. O… (see more)ur primary motivation is to rigorously examine the exploration-exploitation trade-off in the context of sampling. We systematically define plausible notions of regret for this framework and establish corresponding lower bounds. We then propose a simple algorithm that achieves these optimal regret bounds. Our theoretical results demonstrate that in contrast to optimization, sampling does not require exploration. To further connect our findings with those of multi-armed bandits, we define a continuous family of problems and associated regret measures that smoothly interpolates and unifies multi-armed sampling and multi-armed bandit problems using a temperature parameter. We believe the multi-armed sampling framework, and our findings in this setting can have a foundational role in the study of sampling including recent neural samplers, akin to the role of multi-armed bandits in reinforcement learning. In particular, our work sheds light on the need for exploration and the convergence properties of algorithm for entropy-regularized reinforcement learning, fine-tuning of pretrained models and reinforcement learning with human feedback (RLHF).
2025-07-13
ArXiv (preprint)
doi.org
arxiv.org