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, 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
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (voir plus)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
doi.org
openreview.net
Discovering Data Structures: Nearest Neighbor Search and Beyond
Omar Salemohamed
Laurent Charlin
Shivam Garg
Vatsal Sharan
Gregory Valiant
We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and prov… (voir plus)ides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settings, we are able to reverse-engineer the learned data structures and query algorithms. For 1D nearest neighbor search, the model discovers optimal distribution (in)dependent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions that resemble k-d trees in some regimes, while in others, they have elements of locality-sensitive hashing. The model can also learn useful representations of high-dimensional data and exploit them to design effective data structures. We also adapt our framework to the problem of estimating frequencies over a data stream, and believe it could also be a powerful discovery tool for new problems.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Bailey Trang
Parham Saremi
Alan Q. Wang
Fangrui Huang
Zahra TehraniNasab
Amar Kumar
Tal Arbel
Li Fei-Fei
Ehsan Adeli
Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can l… (voir plus)ead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify the input prompt, which is limited in verbally interpretable diversity. We propose Rainbow, a novel conditional image generation framework, applicable to any pretrained conditional generative model, that addresses inherent condition/prompt uncertainty and generates diverse plausible images. Rainbow is based on a simple yet effective idea: decomposing the input condition into diverse latent representations, each capturing an aspect of the uncertainty and generating a distinct image. First, we integrate a latent graph, parameterized by Generative Flow Networks (GFlowNets), into the prompt representation computation. Second, leveraging GFlowNets' advanced graph sampling capabilities to capture uncertainty and output diverse trajectories over the graph, we produce multiple trajectories that collectively represent the input condition, leading to diverse condition representations and corresponding output images. Evaluations on natural image and medical image datasets demonstrate Rainbow's improvement in both diversity and fidelity across image synthesis, image generation, and counterfactual generation tasks.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Distributional Training Data Attribution: What do Influence Functions Sample?
Bruno Mlodozeniec
Isaac Reid
Samuel Power
David M. Krueger
Murat A Erdogdu
Richard E. Turner
Roger Baker Grosse
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
openreview.net
openreview.net
Energy Loss Functions for Physical Systems
Sékou-Oumar Kaba
Kusha Sareen
Daniel Levy
Siamak Ravanbakhsh
Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous… (voir plus) approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We derive energy loss functions assuming that each data sample is in thermal equilibrium with respect to an approximate energy landscape. By using the reverse KL divergence with a Boltzmann distribution around the data, we obtain the loss as an energy difference between the data and the model predictions. This perspective also recasts traditional objectives like MSE as energy-based, but with a physically meaningless energy. In contrast, our formulation yields physically grounded loss functions with gradients that better align with valid configurations, while being architecture-agnostic and computationally efficient. The energy loss functions also inherently respect physical symmetries. We demonstrate our approach on molecular generation and spin ground-state prediction and report significant improvements over baselines.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Entropy Rectifying Guidance for Diffusion and Flow Models
Tariq Berrada Ifriqi
Adriana Romero-Soriano
Michal Drozdzal
Jakob Verbeek
Karteek Alahari
Guidance techniques are commonly used in diffusion and flow models to improve image quality and input consistency for conditional generative… (voir plus) tasks such as class-conditional and text-to-image generation. In particular, classifier-free guidance (CFG) is the most widely adopted guidance technique. It results, however, in trade-offs across quality, diversity and consistency: improving some at the expense of others. While recent work has shown that it is possible to disentangle these factors to some extent, such methods come with an overhead of requiring an additional (weaker) model, or require more forward passes per sampling step. In this paper, we propose Entropy Rectifying Guidance (ERG), a simple and effective guidance method based on inference-time changes in the attention mechanism of state-of-the-art diffusion transformer architectures, which allows for simultaneous improvements over image quality, diversity and prompt consistency. ERG is more general than CFG and similar guidance techniques, as it extends to unconditional sampling. We show that ERG results in significant improvements in various tasks, including text-to-image, class-conditional and unconditional image generation. We also show that ERG can be seamlessly combined with other recent guidance methods such as CADS and APG, further improving generation results.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators
Lucas Berry
David Meger
This work introduces a novel approach, Pairwise Epistemic Estimators (PairEpEsts), for epistemic uncertainty estimation in ensemble models f… (voir plus)or regression tasks using pairwise-distance estimators (PaiDEs). By utilizing the pairwise distances between model components, PaiDEs establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PairEpEsts can estimate epistemic uncertainty up to 100 times faster and demonstrate superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data, *Pendulum*, *Hopper*, *Ant*, and *Humanoid*, demonstrating PairEpEsts’ advantage over baselines in high-dimensional regression active learning.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
openreview.net
openreview.net
Fast Monte Carlo Tree Diffusion: 100× Speedup via Parallel and Sparse Planning
Jaesik Yoon
Hyeonseo Cho
Yoshua Bengio
Sungjin Ahn
Diffusion models have recently emerged as a powerful approach for trajectory planning. However, their inherently non-sequential nature limit… (voir plus)s their effectiveness in long-horizon reasoning tasks at test time. The recently proposed Monte Carlo Tree Diffusion (MCTD) offers a promising solution by combining diffusion with tree-based search, achieving state-of-the-art performance on complex planning problems. Despite its strengths, our analysis shows that MCTD incurs substantial computational overhead due to the sequential nature of tree search and the cost of iterative denoising. To address this, we propose Fast-MCTD, a more efficient variant that preserves the strengths of MCTD while significantly improving its speed and scalability. Fast-MCTD integrates two techniques: Parallel MCTD, which enables parallel rollouts via delayed tree updates and redundancy-aware selection; and Sparse MCTD, which reduces rollout length through trajectory coarsening. Experiments show that Fast-MCTD achieves up to 100× speedup over standard MCTD while maintaining or improving planning performance. Remarkably, it even outperforms Diffuser in inference speed on some tasks, despite Diffuser requiring no search and yielding weaker solutions. These results position Fast-MCTD as a practical and scalable solution for diffusion-based inference-time reasoning.
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
openreview.net
openreview.net
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Luca Della Libera
Francesco Paissan
Cem Subakan
Mirco Ravanelli
Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by t… (voir plus)his success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples and code are available at https://lucadellalib.github.io/focalcodec-web/.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Yifei Su
Ning Liu
Dong Chen
Zhen Zhao
Kun Wu
Meng Li
Zhiyuan Xu
Zhengping Che
Jian Tang
2025-09-17
NeurIPS.cc/2025/Conference (poster)
openreview.net
openreview.net
From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization
Shoaib Ahmed Siddiqui
Adrian Weller
David M. Krueger
Gintare Karolina Dziugaite
Michael Curtis Mozer
Eleni Triantafillou
Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a s… (voir plus)mall set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning in vision classifiers. We make the surprising discovery that forget-set accuracy can recover from around 50% post-unlearning to nearly 100% with fine-tuning on just the retain set -- i.e., zero examples of the forget set. We observe this effect across a wide variety of unlearning methods, whereas for a model retrained from scratch excluding the forget set (gold standard), the accuracy remains at 50%. We observe that resistance to relearning attacks can be predicted by weight-space properties, specifically,
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net