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2026 顶会论文

作者位次 #5/7ICML 2026Poster· Deep Learning / Sequential Models, Time series

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations

Dai Shi · Lequan Lin · Andi Han · Luke Thompson · Jose Miguel Hernandez-Lobato · Zhiyong Wang · Junbin Gao

作者位次 #8/9ICML 2026Poster· Deep Learning / Generative Models and Autoencoders

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

Zongyu Guo · Jiajun He · Zhaoyang Jia · Xiaoyi Zhang · Jiahao Li · Xiao Li · Bin Li · Jose Miguel Hernandez-Lobato · Yan Lu

可解释性多模态效率与系统OpenReview

作者位次 #7/7ICML 2026Spotlight· Deep Learning / Graph Neural Networks

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning

Dai Shi · Linhan Luo · Luke Thompson · Lequan Lin · Andi Han · Junbin Gao · Jose Miguel Hernandez-Lobato

效率与系统OpenReview

作者位次 #5/10ICML 2026Poster· Deep Learning / Large Language Models

Towards Diverse Scientific Hypothesis Search with Large Language Models

Haorui Wang · Parshin Shojaee · Kazem Meidani · Kunyang Sun · Jose Miguel Hernandez-Lobato · Teresa Head-Gordon · Jiajun He · Chandan Reddy · Chao Zhang · Yuanqi Du

检索与知识OpenReview

作者位次 #3/3ICML 2026Poster· Applications / Chemistry, Physics, and Earth Sciences

Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models

Fengzhe Zhang · Laurence Midgley · Jose Miguel Hernandez-Lobato

效率与系统社会与人本OpenReview

作者位次 #6/6ICML 2026Poster· Probabilistic Methods / Monte Carlo and Sampling Methods

Conditional Diffusion Sampling

Francisco M Castro-Macías · Pablo Morales-Alvarez · Saifuddin Syed · Daniel Hernández-Lobato · Rafael Molina · Jose Miguel Hernandez-Lobato

OpenReview

作者位次 #6/7ICML 2026Poster· Probabilistic Methods / Bayesian Models and Methods

Richer Bayesian Last Layers with Subsampled NTK Features

Sergio Calvo Ordoñez · Jonathan Plenk · Richard Bergna · Alvaro Cartea · Yarin Gal · Jose Miguel Hernandez-Lobato · Kamil Ciosek

OpenReview

作者位次 #3/3ICML 2026Poster· Deep Learning / Generative Models and Autoencoders

A Diffusive Classification Loss for Learning Energy-based Generative Models

RuiKang OuYang · Louis Grenioux · Jose Miguel Hernandez-Lobato

OpenReview

作者位次 #4/7ICML 2026Poster· Deep Learning / Generative Models and Autoencoders

Training-Free Vector Quantization via Gaussian VAEs

Tongda Xu · Wendi Zheng · Jiajun He · Jose Miguel Hernandez-Lobato · Yan Wang · Ya-Qin Zhang · Jie Tang

效率与系统OpenReview

Jose Miguel Hernandez-Lobato

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2026 顶会论文

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning

Towards Diverse Scientific Hypothesis Search with Large Language Models

Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models

Conditional Diffusion Sampling

Richer Bayesian Last Layers with Subsampled NTK Features

A Diffusive Classification Loss for Learning Energy-based Generative Models

Training-Free Vector Quantization via Gaussian VAEs

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