Hugging Face Trending Papers

Learning Where to Steer: Noise-Space Geometry for Efficient Offline Multi-Objective Optimization with Generative Models

arXiv Machine Learning

2d ago

Learning Where to Steer: Noise-Space Geometry for Efficient Offline Multi-Objective Optimization with Generative Models

arXiv:2609.38920v1 Announce Type: new Abstract: Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives...

By Yuan Lu, Esha Singh, Yi-An Ma, Yusu Wang

diffusion

arXiv AI

Jun 4

ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion

arXiv:2606. 04468v1 Announce Type: cross Abstract: Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions.

By Ruiqing Sun, Sen Yang, Dawei Feng, Bo Ding, Yijie Wang, Huaimin Wang

diffusion benchmarks safety

arXiv Machine Learning

Sep 22

D-IMPL: A Diffusion-based Solver for Parameterized BBOs

arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.

By Yang Hu, Na Li

diffusion multimodal

arXiv AI

Sep 2

Rethinking Learnability in Offline Data-driven Optimization

arXiv:2609.01493v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...

By Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue

diffusion

arXiv Machine Learning

Jun 2

GLENS: Global Search via Learning from Solver Iterates with Diffusion Models

arXiv:2606. 00366v1 Announce Type: new Abstract: We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems.

By Anjian Li, Bartolomeo Stellato, Ryne Beeson

diffusion robotics multimodal benchmarks

arXiv Machine Learning

Jun 16

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.

By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian

benchmarks safety

arXiv Machine Learning

Sep 10

ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front

ParetoTransport is a training‑free guidance method for pre‑trained flow‑matching models that explicitly refines a population‑level distribution in objective space. It iteratively transports the empirical offline distribution toward the Pareto front using Wasserstein matching to intermediate proxy distributions, thereby controlling distributional displacement and mass allocation along the front. The authors prove a convergence result and show state‑of‑the‑art performance on standard offline multi‑objective optimization benchmarks, evaluating beyond hypervolume to generational distance, inverted generational distance, and Wasserstein distance.

By Stephanie Holly, Sepp Hochreiter, Werner Zellinger

benchmarks

arXiv AI

Jul 17

Integration Matters: Rollout-Based Training for Constrained Diffusion Models

arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.

By Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood

diffusion fine-tuning

arXiv Machine Learning

Aug 31

Amortizing intractable inference in diffusion models for vision, language, and control

The paper introduces a data‑free learning objective called relative trajectory balance for training diffusion models to sample from a posterior defined by a diffusion prior and an arbitrary black‑box constraint or likelihood. It proves asymptotic correctness of this objective and demonstrates its use across vision, language, and multimodal tasks, including classifier guidance, language infilling, and text‑to‑image generation. Additionally, the method is applied to continuous control with a score‑based behavior prior, achieving state‑of‑the‑art results in offline reinforcement learning.

By Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Esmeralda S. Whitammer

llms diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia

diffusion fine-tuning benchmarks safety

arXiv Machine Learning

Jul 2

Generative Refinement for Low-Budget Black-Box Optimization

arXiv:2607. 00691v1 Announce Type: new Abstract: Black-box optimization is a fundamental science and engineering tool that makes it possible to optimize objectives without gradient information.

By Edouard R. Dufour, Pascal Fua

arXiv Machine Learning

Jun 2

Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback

arXiv:2606. 01081v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy.

By Wyame Benslimane, Tinghan Ye, Pascal Van Hentenryck, Paul Grigas

reinforcement-learning benchmarks

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