Talks - A NeurIPS-endorsed conference in Europe

Keynotes

Emtiyaz Khan

Team director at the RIKEN center for Advanced Intelligence Project

Adaptive Bayesian Intelligence and the Road to Sustainable AI

Wednesday, Dec 3

13:30 - 14:30

D1-D2 Plenary

Sepp Hochreiter

Professor at Johannes Kepler Universität Linz.

Sustainable, Low-Energy, and Fast AI Made in Europe

Thursday, Dec 4

13:30 - 14:30

D1-D2 Plenary

Michael I. Jordan

Inria Paris and the University of California, Berkeley

A Collectivist, Economic Perspective on AI

Thursday, Dec 4

16:00 - 17:00

D1-D2 Plenary

Judith Rousseau

Professor at Paris Dauphine University

Bayesian semiparametric inference: a blessing or a curse.

Friday, Dec 5

10:30 - 11:30

D1-D2 Plenary

Gaël Varoquaux

Research director, INRIA

Uncertainty in the LLM era - Science, more than scale

Friday, Dec 5

13:30 - 14:30

D1-D2 Plenary

Selected papers will be presented during dedicated Paper Talks sessions ↓ All presentations take place in the Plenary Hall (D1-D2).

Wednesday, December 3

Session 1 09:00 - 10:30

Oral

Generalized Linear Mode Connectivity for Transformers

Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis, Antonio Orvieto, Sidak Pal Singh, Valentina Boeva

Model Optimization, Representation Learning

Spotlight

Differentiable Sparsity via $D$-Gating: Simple, Modular and Versatile Structured Penalization

Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer

Model Optimization, Representation Learning, Computer Vision

Spotlight

TabArena: A Living Benchmark for Machine Learning on Tabular Data

Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Desai, David Salinas, Frank Hutter

Tabular Data, Model Optimization

Spotlight

Hogwild! Inference: Parallel LLM Generation via Concurrent Attention

Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Erik Schultheis, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, Dan Alistarh

Large Language Models, Model Optimization, In-Context Learning

Spotlight

Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference

Weizhi Fei, Xueyan Niu, XIE GUOQING, Yingqing Liu, Bo Bai, Wei Han

Large Language Models, Model Optimization, In-Context Learning

Spotlight

DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe, Giovanni Bellitto, Simone Palazzo, Daniela Giordano, Mubarak Shah, Concetto Spampinato

Diffusion Models, Large Language Models, Computer Vision

Spotlight

Orochi: Versatile Biomedical Image Processor

Gaole Dai, Chenghao Zhou, Yu Zhou, Rongyu Zhang, Yuan Zhang, Chengkai Hou, Tiejun Huang, Jianxu Chen, Shanghang Zhang

Computer Vision, Representation Learning

Session 2 16:00 - 17:45

Oral

Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training

Tony Bonnaire, Raphaël Urfin, Giulio Biroli, Marc Mezard

Diffusion Models, Model Optimization, Membership Inference

Spotlight

Do-PFN: In-context Learning for Causal Effect Estimation

Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf

Causal Inference, In-Context Learning, Tabular Data

Spotlight

ConTextTab: A Semantics-Aware Tabular In-Context Learner

Marco Spinaci, Marek Polewczyk, Maximilian Schambach, Sam Thelin

Tabular Data, In-Context Learning, Large Language Models

Spotlight

Fixed-Point RNNs: Interpolating from Diagonal to Dense

Sajad Movahedi, Felix Sarnthein, Nicola Muca Cirone, Antonio Orvieto

Representation Learning, Model Optimization, Large Language Models

Spotlight

Generalizable Insights for Graph Transformers in Theory and Practice

Timo Stoll, Luis Müller, Christopher Morris

Graph Neural Networks, Representation Learning

Spotlight

Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations

Kaibo Wang, Jianda Mao, Tong Wu, Yang Xiang

Diffusion Models, Vision-Language Models, Model Optimization

Spotlight

DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method

Qingwen Zhang, Xiaomeng Zhu, Yushan Zhang, Yixi Cai, Olov Andersson, Patric Jensfelt

Computer Vision, Model Optimization, Representation Learning

Thursday, December 4

Session 1 09:00 - 10:30

Oral

Infinity*: Uniﬁed Spacetime AutoRegressive Modeling for Visual Generation

Jinlai Liu, Jian Han, Bin Yan, Yi Jiang, Hui Wu, Fengda Zhu, Xing Wang, BINGYUE PENG, Zehuan Yuan

Computer Vision, Vision-Language Models, Representation Learning

Spotlight

Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning

Bastien Dubail, Stefan Stojanovic, Alexandre Proutiere

Reinforcement Learning, Representation Learning

Spotlight

STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation

Hossein Goli, Michael Gimelfarb, Nathan de Lara, Haruki Nishimura, Masha Itkina, Florian Shkurti

Off-Policy Evaluation, Diffusion Models, World Models

Spotlight

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas, Junwei Ma, Bingru Li, Jesse Cresswell, Rahul Krishnan

Causal Inference, In-Context Learning, Bayesian Methods

Spotlight

ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals

Jonas Elsborg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

Equivariant Networks, Surrogate Models, Representation Learning

Spotlight

Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators

Albert Matveev, Sanmitra Ghosh, Aamal Hussain, James-Michael Leahy, Michalis Michaelides

Uncertainty Quantification, Bayesian Methods, Model Optimization

Spotlight

On the sample complexity of semi-supervised multi-objective learning

Tobias Wegel, Geelon So, Junhyung Park, Fanny Yang

Multi-Task Learning, Representation Learning

Session 2 14:30 - 15:30

Oral

In Search of Adam’s Secret Sauce

Antonio Orvieto, Robert Gower

Model Optimization, Large Language Models, Bayesian Methods

Spotlight

Understanding Prompt Tuning and In-Context Learning via Meta-Learning

Tim Genewein, Kevin Li, Jordi Grau-Moya, Anian Ruoss, Laurent Orseau, Marcus Hutter

In-Context Learning, Bayesian Methods, Model Optimization

Spotlight

Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?

Yihao Li, Saeed Salehi, Lyle Ungar, Konrad Kording

Computer Vision, Representation Learning, Neurosymbolic AI

Spotlight

DeCaFlow: A deconfounding causal generative model

Alejandro Almodóvar, Adrián Javaloy, Juan Parras, Santiago Zazo, Isabel Valera

Causal Inference, Representation Learning

Session 3 17:00 - 17:45

Oral

On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity

Quentin Bertrand, Anne Gagneux, Mathurin Massias, Rémi Emonet

Diffusion Models, Computer Vision, Model Optimization

Spotlight

Restoring Pruned Large Language Models via Lost Component Compensation

Zijian Feng, Hanzhang Zhou, Zixiao Zhu, Tianjiao Li, Chua Deryl, Lee Onn Mak, Gee Ng, Kezhi Mao

Large Language Models, Model Optimization, Representation Learning

Spotlight

Quantization-Free Autoregressive Action Transformer

Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach, Claire Vernade

Reinforcement Learning, Representation Learning

Friday, December 5

Session 1 09:00 - 10:00

Oral

Learning long range dependencies through time reversal symmetry breaking

Guillaume Pourcel, Maxence Ernoult

Model Optimization, Equivariant Networks, Surrogate Models

Spotlight

A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone

Jitai Hao, Qiang Huang, Hao Liu, Xinyan Xiao, Zhaochun Ren, Jun Yu

Large Language Models, Model Optimization, Representation Learning

Spotlight

FlashMD: long-stride, universal prediction of molecular dynamics

Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti

Surrogate Models, World Models, Equivariant Networks

Spotlight

Quantum Doubly Stochastic Transformers

Jannis Born, Filip Skogh, Nico Wagner, Kahn Rhrissorrakrai, Filippo Utro, Aleksandros Sobczyk

Optimal Transport, Computer Vision, Representation Learning

Spotlight

The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?

Denis Sutter, Julian Minder, Thomas Hofmann, Tiago Pimentel

Representation Learning, Causal Inference

Session 2 14:30 - 15:30

Oral

Position: Bridge the Gaps between Machine Unlearning and AI Regulation

Bill Marino, Meghdad Kurmanji, Nicholas Lane

Membership Inference, Differential Privacy

Spotlight

Computational Efficiency under Covariate Shift in Kernel Ridge Regression

Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco

Model Optimization, Representation Learning

Spotlight

A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking

Zixiang Zhao, Haowen Bai, Bingxin Ke, Yukun Cui, Lilun Deng, Yulun Zhang, Kai Zhang, Konrad Schindler

Computer Vision, Multi-Task Learning, Representation Learning

Spotlight

OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents

Thomas Kuntz, Agatha Duzan, Hao Zhao, Francesco Croce, Zico Kolter, Nicolas Flammarion, Maksym Andriushchenko

Vision-Language Models, Large Language Models, Computer Vision

Spotlight

A machine learning approach that beats large Rubik's cubes

Alexander Chervov, Kirill Khoruzhii, Nikita Bukhal, Jalal Naghiyev, Vladislav Zamkovoy, Ivan Koltsov, Lyudmila Cheldieva, Arsenii Sychev, Arsenii Lenin, Mark Obozov, Egor Urvanov, Alexey Romanov

Reinforcement Learning, Graph Neural Networks, Diffusion Models