The Diffusion Process as a Correlation Machine: Linear Denoising Insights Dana Weitzner, Mauricio Delbracio, Peyman Milanfar, Raja Giryes Action editor: Arno Solin https://openreview.net/forum?id=FGDJOc27rt #denoising #denoisers #denoiser

Mesh-Informed Neural Operator : A Transformer Generative Approach Yaozhong Shi, Zachary E Ross, Domniki Asimaki, Kamyar Azizzadenesheli Action editor: Andriy Mnih https://openreview.net/forum?id=K8qAuRfv0G #generative #mesh #functional

Improved seeding strategies for k-means and k-GMM Guillaume Carrière, Frederic Cazals Action editor: Kejun Huang https://openreview.net/forum?id=4Ut2YnekhN #seeding #clustering #randomized

Slicing the Gaussian Mixture Wasserstein Distance Moritz Piening, Robert Beinert Action editor: Makoto Yamada https://openreview.net/forum?id=yPBtJ4JPwi #wasserstein #generative #minimization

Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson: Out-of-Distribution Detection Methods Answer the Wrong Questions https://arxiv.org/abs/2507.01831 https://arxiv.org/pdf/2507.01831 https://arxiv.org/html/2507.01831

Sloan Nietert, Ziv Goldfeld Estimation of Stochastic Optimal Transport Maps https://arxiv.org/abs/2512.09499

A Comprehensive Survey on Knowledge Distillation Amir M. Mansourian, Rozhan Ahmadi, Masoud Ghafouri et al. Action editor: Changyou Chen https://openreview.net/forum?id=3cbJzdR78B #distillation #dnns #knowledge

Survey of Video Diffusion Models: Foundations, Implementations, and Applications Yimu Wang, Xuye Liu, Wei Pang, Li Ma, Shuai Yuan, Paul Debevec, Ning Yu Action editor: Anurag Arnab https://openreview.net/forum?id=2ODDBObKjH #video #generative #visual

Open Problems in Mechanistic Interpretability Lee Sharkey, Bilal Chughtai, Joshua Batson et al. Action editor: Sarath Chandar https://openreview.net/forum?id=91H76m9Z94 #interpretability #ai #mechanistic

Read last night. Very nice. arxiv.org/abs/2512.01868

Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E Vogt Action editor: Shinichi Nakajima https://openreview.net/forum?id=Bt0zdsnWYc #outliers #anomaly #anomalies

Wondering how DeepSeek v3.2 rivals SOTA models (e.g., GPT5/Gemini 3 pro) while being ~30x cheaper? 🤔 Let's learn how the base model works! We'll focus on attention, the need for KV caching, and key ideas for improving attention (MQA/GQA/MLA/DSA). youtu.be/Y-o545eYjXM

Label Embedding via Low-Coherence Matrices Jianxin Zhang, Clayton Scott Action editor: Jake C. Snell https://openreview.net/forum?id=vrcWXcr4On #embedding #classification #label

🚨 OpenReview might have leaked names, but it won't leak the best hyperparameters, unfortunately! 😅 Tired of the drama? Solve your HPO problems before the ICML deadline with this new monograph by our own Luca Franceschi & Massimiliano Pontil (& colleagues). arxiv.org/abs/2410.22854

I'm quite intrigued by possibility theory, so I must say this looks quite exciting! arxiv.org/abs/2511.21223

🔄 Updated Arxiv Paper Title: Modelling Global Trade with Optimal Transport Authors: Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram, Andrew Duncan Read more: https://arxiv.org/abs/2409.06554

A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models Evelyn Mannix, Howard Bondell Action editor: Gabriel Loaiza-Ganem https://openreview.net/forum?id=xpKqnSJtE4 #classifier #detection #classification

A Unified Approach Towards Active Learning and Out-of-Distribution Detection Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann Action editor: Chicheng Zhang https://openreview.net/forum?id=HL75La10FN #detection #deep #feature

Reading group tomorrow: "How to build a consistency model: Learning flow maps via self-distillation" with Nicholas Boffi! arxiv.org/abs/2505.18825 Join us on zoom at 9am PT, 12pm ET, 6pm CET: portal.valencelabs.com/starklyspeak...

Unifying Self-Supervised Clustering and Energy-Based Models Emanuele Sansone, Robin Manhaeve Action editor: Ole Winther https://openreview.net/forum?id=NW0uKe6IZa #generative #supervised #models

Entangled Schrödinger Bridge Matching][new] Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.

Does equivariance matter at scale? Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen Action editor: Marcus Brubaker https://openreview.net/forum?id=wilNute8Tn #models #equivariance #equivariant

"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890 It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances

New paper on arXiv! And I think it's a good'un 😄 Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.

Samuel Duffield, Maxwell Aifer, Denis Melanson, Zach Belateche, Patrick J. Coles Lattice Random Walk Discretisations of Stochastic Differential Equations https://arxiv.org/abs/2508.20883

Luca Ambrogioni The Information Dynamics of Generative Diffusion https://arxiv.org/abs/2508.19897

Great stuff: arxiv.org/abs/2508.18175

A random old one: "Kernels and Decision Trees" hackmd.io/@sp-monte-ca...

It’s a thing! www.microsoft.com/en-us/resear...

Struggling to control a system under noise and uncertainty? Willem Esterhuizen's new article dives into Model Predictive Control (MPC), a powerful feedback loop that uses a model to anticipate and correct system behavior. Learn how to handle hard constraints and disturbances.

Eliot Beyler (SIERRA), Francis Bach (SIERRA): Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance https://arxiv.org/abs/2508.03210 https://arxiv.org/pdf/2508.03210 https://arxiv.org/html/2508.03210

Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization https://arxiv.org/abs/2508.03314

Sergio Calvo-Ordonez, Matthieu Meunier, Alvaro Cartea, Christoph Reisinger, Yarin Gal, Jose Miguel Hernandez-Lobato: Weighted Conditional Flow Matching https://arxiv.org/abs/2507.22270 https://arxiv.org/pdf/2507.22270 https://arxiv.org/html/2507.22270

Alan Jeffares, Liyuan Liu: An Introduction to Discrete Variational Autoencoders https://arxiv.org/abs/2505.10344 https://arxiv.org/pdf/2505.10344 https://arxiv.org/html/2505.10344

Tomorrow we discuss diffusion models for sampling unnormalized densities "Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching" arxiv.org/abs/2504.11713 Join us on zoom at 9am PT / 12pm ET / 6pm CEST: portal.valencelabs.com/starklyspeak...

Yaxin Ma, Benjamin Colburn, Jose C. Principe: A Simple and Effective Method for Uncertainty Quantification and OOD Detection https://arxiv.org/abs/2508.00754 https://arxiv.org/pdf/2508.00754 https://arxiv.org/html/2508.00754

A very nice article from @physrevx.bsky.social journals.aps.org/prx/abstract...

Another very nice paper arxiv.org/abs/2502.02300

I had a great time presenting "It's Time to Say Goodbye to Hard Constraints" at the Flatiron Institute. In this talk, I describe a philosophy for model construction in machine learning. Video now online! www.youtube.com/watch?v=LxuN...

Join Dr. Lianrui Zuo, Postdoctoral Researcher in ECE at VU and AI Scholar with VALIANT, at AI Summer School! 🌟 He'll be presenting "The Shape of Data in Noise: Diffusion Models as a Programmable Prior" Registration ends on July 31st. Register here: buff.ly/4fyVotP

Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems Hongkai Zheng, Wenda Chu, Austin Wang et al. Action editor: Valentin De Bortoli https://openreview.net/forum?id=XPEEsKneKs #diffusion #kalman #inverse

Personalization of Large Language Models: A Survey Zhehao Zhang, Ryan A. Rossi, Branislav Kveton et al. Action editor: Sarath Chandar https://openreview.net/forum?id=tf6A9EYMo6 #personalization #personalized #formalization

New in the #DeeperLearningBlog: #KempnerInstitute researchers @binxuwang.bsky.social and John J. Vastola explain their work uncovering the linear Gaussian structure in diffusion models and the potential to use it to enhance performance. bit.ly/4lCauDv #AI #DiffusionModels

Join Dr. Daniel Moyer, Assistant Professor of Computer Science at Vanderbilt University, at AI Summer School! 🌟 He'll be presenting "Second Steps in Neural Networks" Space is limited. Registration ends on July 31st. Register here: buff.ly/x4pyQDo

Join Dr. Soheil Kolouri, Assistant Professor of CS and ECE at Vanderbilt University, at AI Summer School! 🌟 He'll be presenting "A Crash Course on Optimal Transport and Wasserstein Distances" Registration ends on July 31st. Register here: buff.ly/4fyVotP

Gianluigi Silvestri, Luca Ambrogioni CoVAE: Consistency Training of Variational Autoencoders https://arxiv.org/abs/2507.09103

Diffusion Model Predictive Control Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju et al. Action editor: Stephen James https://openreview.net/forum?id=pvtgffHtJm #planning #reinforcement #learns

Pascanu, Lyle, Modoranu, Borras, Alistarh, Velickovic, Chandar, De, Martens: Optimizers Qualitatively Alter Solutions And We Should Leverage This https://arxiv.org/abs/2507.12224 https://arxiv.org/pdf/2507.12224 https://arxiv.org/html/2507.12224

New #Featured Certification: The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities Manato Yaguchi, Kotaro Sakamoto, Ryosuke Sakamoto et al. https://openreview.net/forum?id=ahVFKFLYk2 #diffusion #models #phase

Neta Shaul, Uriel Singer, Itai Gat, Yaron Lipman: Transition Matching: Scalable and Flexible Generative Modeling https://arxiv.org/abs/2506.23589 https://arxiv.org/pdf/2506.23589 https://arxiv.org/html/2506.23589