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Vineet Jain

Doctorat - McGill
Superviseur⋅e principal⋅e
Siamak Ravanbakhsh
Sujets de recherche
Apprentissage par renforcement
Modèles génératifs
Modèles probabilistes

Publications

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 … (voir plus)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, leading to 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*) performs a robust search for high reward samples. On MNIST and CIFAR-10 class-conditional generation, DTS matches the FID of the best-performing baseline with up to
2025-06-10
ICML.cc/2025/Workshop/PUT (poster)
openreview.net
openreview.net
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 … (voir plus)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
2025-06-10
ICML.cc/2025/Workshop/PUT (poster)
doi.org
openreview.net
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Vineet Jain
Kusha Sareen
Mohammad Pedramfar
Siamak Ravanbakhsh
2025-01-01
arXiv.org (prépublication)
doi.org
Sampling from Energy-based Policies using Diffusion
Vineet Jain
Tara Akhound-Sadegh
Siamak Ravanbakhsh
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
On Diffusion Modeling for Anomaly Detection
Victor Livernoche
Vineet Jain
Yashar Hezaveh
Siamak Ravanbakhsh
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.
2024-01-16
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net
Learning to Reach Goals via Diffusion
Vineet Jain
Siamak Ravanbakhsh
We present a novel perspective on goal-conditioned reinforcement learning by framing it within the context of denoising diffusion models. An… (voir plus)alogous to the diffusion process, where Gaussian noise is used to create random trajectories that walk away from the data manifold, we construct trajectories that move away from potential goal states. We then learn a goal-conditioned policy to reverse these deviations, analogously to the score function. This approach, which we call Merlin, can reach specified goals from an arbitrary initial state without learning a separate value function. In contrast to recent works utilizing diffusion models in offline RL, Merlin stands out as the first method to perform diffusion in the state space, requiring only one ``denoising"iteration per environment step. We experimentally validate our approach in various offline goal-reaching tasks, demonstrating substantial performance enhancements compared to state-of-the-art methods while improving computational efficiency over other diffusion-based RL methods by an order of magnitude. Our results suggest that this perspective on diffusion for RL is a simple, scalable, and practical direction for sequential decision making.
2023-10-04
ArXiv (preprint)
doi.org
openreview.net