239 Posters
Poster Sessions & Presentations
All posters will be presented during dedicated poster sessions on Wednesday, December 3 and Thursday, December 4 from 10:30-12:30 in Hall D3.
Oral and Spotlight presentations will be given during the Paper Talks sessions in the Plenary Hall (D1-D2). Check the conference program for exact timing.
Presenters can find information about poster sizes and talk durations in the FAQ.
Differentiable Sparsity via $D$-Gating: Simple, Modular and Versatile Structured Penalization
Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer
Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
Bastien Dubail, Stefan Stojanovic, Alexandre Proutiere
From Self-Check to Consensus: Bayesian Strategic Decoding in Large Language Models
Weitong Zhang, Chengqi Zang, Bernhard Kainz
Multi-View Oriented GPLVM: Expressiveness and Efficiency
Zi Yang, Ying Li, Zhidi Lin, Michael Minyi Zhang, Pablo Martinez Olmos
Understanding and Enhancing Message Passing on Heterophilic Graphs via Compatibility Matrix
Zhuonan Zheng, Yuanchen Bei, Zhiyao Zhou, Sheng Zhou, Yao Ma, Ming Gu, HONGJIA XU, Jiawei Chen, Jiajun Bu
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu, Jiawei Chen, Qiaoyu Tan, Liangcheng Li, Jiajun Bu
Is Limited Participant Diversity Impeding EEG-based Machine Learning?
Philipp Bomatter, Henry Gouk
Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
Julius Vetter, Manuel Gloeckler, Daniel Gedon, Jakob H Macke
TabArena: A Living Benchmark for Machine Learning on Tabular Data
Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Desai, David Salinas, Frank Hutter
FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators
Guy Moss, Leah Muhle, Reinhard Drews, Jakob H Macke, Cornelius Schröder
Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
Dipam Goswami, Simone Magistri, KAI WANG, Bartłomiej Twardowski, Andrew Bagdanov, Joost van de Weijer
Efficient Low-Rank Model Merging in Core Space
Aniello Panariello, Daniel Marczak, Simone Magistri, Angelo Porrello, Bartłomiej Twardowski, Andrew Bagdanov, SIMONE CALDERARA, Joost van de Weijer
Graph Diffusion that can Insert and Delete
Matteo Ninniri, Marco Podda, Davide Bacciu
Neurosymbolic Diffusion Models
Emile van Krieken, Pasquale Minervini, Edoardo Maria Ponti, Antonio Vergari
Dynamic Regret Reduces to Kernelized Static Regret
Andrew Jacobsen, Alessandro Rudi, Francesco Orabona, Nicolò Cesa-Bianchi
Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring
Federico Di Gennaro, Khaled Eldowa, Nicolò Cesa-Bianchi
Differentially Private Quantiles with Smaller Error
Jacob Imola, Fabrizio Boninsegna, Hannah Keller, Anders Aamand, Amrita Roy Chowdhury, Rasmus Pagh
FAIR Universe HiggsML Uncertainty Dataset and Competition
Wahid Bhimji, Ragansu Chakkappai, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Aishik Ghosh, Cristina Giordano, Isabelle Guyon, Chris Harris, Yota Hashizume, Shih-Chieh Hsu, Elham E Khoda, Claudius Krause, Ang Li, Benjamin Nachman, David Rousseau, Robert Schöfbeck, Maryam Shooshtari, Dennis Schwarz, Ihsan Ullah, Daohan Wang, Yulei Zhang
Integral Imprecise Probability Metrics
Siu Lun (Alan) Chau, Michele Caprio, Krikamol Muandet
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
Beatrix M. G. Nielsen, Emanuele Marconato, Andrea Dittadi, Luigi Gresele
Multilevel neural simulation-based inference
Yuga Hikida, Ayush Bharti, Niall Jeffrey, Francois-Xavier Briol
Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
Hidde Fokkema, Tim van Erven, Sara Magliacane
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
Tim van Erven, Jack Mayo, Julia Olkhovskaya, Chen-Yu Wei
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
Hossein Goli, Michael Gimelfarb, Nathan de Lara, Haruki Nishimura, Masha Itkina, Florian Shkurti
Online Learning in the Repeated Mediated Newsvendor Problem
Nataša Bolić, Tom Cesari, Roberto Colomboni, Christian Paravalos
Online Bilateral Trade With Minimal Feedback: Don’t Waste Seller’s Time
Francesco Bacchiocchi, Matteo Castiglioni, Roberto Colomboni, Alberto Marchesi
Cached Token Similarity Is a Strong Prior for Fine-grained Visual Question Answering
Liangyu Zhong, Fabio Rosenthal, Joachim Sicking, Fabian Hüger, Thorsten Bagdonat, Hanno Gottschalk, Leo Schwinn
Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
Kumar Kshitij Patel, Ali Zindari, Sebastian Stich, Lingxiao Wang
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
François Rozet, Ruben Ohana, Michael McCabe, Gilles Louppe, Francois Lanusse, Shirley Ho
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
Antoine Collas, Ce Ju, Nicolas Salvy, Bertrand Thirion
Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs
Sonia Mazelet, Rémi Flamary, Bertrand Thirion
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era.
Elizaveta Semenova, Siobhan Mackenzie Hall, Timothy James Hitge, Alisa Sheinkman, Jon Cockayne
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus, César Ali Ojeda Marin, Ramsés J. Sánchez
Lorentz Local Canonicalization: How to make any Network Lorentz-Equivariant
Jonas Spinner, Luigi Favaro, Peter Lippmann, Sebastian Pitz, Gerrit Gerhartz, Tilman Plehn, Fred Hamprecht
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning
Marlon Tobaben, Hibiki Ito, Joonas Jälkö, Yuan He, Antti Honkela
StateSpaceDiffuser: Bringing Long-Context Content to Diffusion World Models
Nedko Savov, Naser Kazemi, Deheng Zhang, Danda Pani Paudel, Xi Wang, Luc V Gool
Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes
Hossein Zakerinia, Christoph Lampert
Continual Release Moment Estimation with Differential Privacy
Nikita Kalinin, Jalaj Upadhyay, Christoph Lampert
Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers
Peter Súkeník, Christoph Lampert, Marco Mondelli
FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
Amin Parchami-Araghi, Sukrut Rao, Jonas Fischer, Bernt Schiele
The Curse of Depth in Large Language Models
Wenfang Sun, Xinyuan Song, Pengxiang Li, Lu Yin, Yefeng Zheng, Shiwei Liu
Learning conformational ensembles of proteins based on backbone geometry
Nicolas Wolf, Leif Seute, Vsevolod Viliuga, Simon Wagner, Jan Stühmer, Frauke Gräter
Quartet: Towards Optimal End-to-End FP4 Training for Large Language Models
Roberto Castro, Andrei Panferov, Rush Tabesh, Oliver Sieberling, Jiale Chen, Mahdi Nikdan, Saleh Ashkboos, Dan Alistarh
Unified Scaling Laws for Learning over Compressed Representations
Andrei Panferov, Alexandra Volkova, Ionut-Vlad Modoranu, Vage Egiazarian, Mher Safaryan, Dan Alistarh
Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso, Karim Farid, Thomas Brox
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas, Junwei Ma, Bingru Li, Jesse Cresswell, Rahul Krishnan
When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
Moritz Willig, Tim Woydt, Devendra Singh Dhami, Kristian Kersting
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
Amir Rezaei Balef, Claire Vernade, Katharina Eggensperger
On the rankability of visual embeddings
Ankit Sonthalia, Arnas Uselis, Seong Joon Oh
RIGNO: A Graph-based Framework For Robust And Accurate Operator Learning For PDEs On Arbitrary Domains
Sepehr Mousavi, Shizheng Wen, Levi Lingsch, Maximilian Herde, Bogdan Raonic, Siddhartha Mishra
Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Yulu Qin, Dheeraj Varghese, Adam Dahlgren Lindström, Lucia Donatelli, Kanishka Misra, Najoung Kim
ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals
Jonas Elsborg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
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
Connecting Neural Models Latent Geometries with Relative Geodesic Representations
Hanlin Yu, Berfin Inal, Georgios Arvanitidis, Søren Hauberg, Francesco Locatello, Marco Fumero
Inference-Time Hyper-Scaling with KV Cache Compression
Adrian Łańcucki, Konrad Staniszewski, Piotr Nawrot, Edoardo Maria Ponti
Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototype-Augmented Neurosymbolic AI
Luca Andolfi, Eleonora Giunchiglia
Scalable Generalized Bayesian Online Neural Network Training for Sequential Decision Making
Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alvaro Cartea, Kevin Murphy
Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
Luca Arnaboldi, Bruno Loureiro, Ludovic Stephan, Florent Krzakala, Lenka Zdeborová
The third pillar of causal analysis? A measurement perspective on causal representations
Dingling Yao, Shimeng Huang, Riccardo Cadei, Kun Zhang, Francesco Locatello
Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators
Albert Matveev, Sanmitra Ghosh, Aamal Hussain, James-Michael Leahy, Michalis Michaelides
EyeBench: Predictive Modeling from Eye Movements in Reading
Omer Shubi, David Robert Reich, Keren Gruteke Klein, Yuval Angel, Paul Prasse, Lena A. Jäger, Yevgeni Berzak
Sequence Modeling with Spectral Mean Flows
Jinwoo Kim, Max Beier, Petar Bevanda, Nayun Kim, Seunghoon Hong
Efficient Randomized Experiments Using Foundation Models
Piersilvio De Bartolomeis, Javier Abad, Guanbo Wang, Konstantin Donhauser, Raymond Duch, Fanny Yang, Issa Dahabreh
On the sample complexity of semi-supervised multi-objective learning
Tobias Wegel, Geelon So, Junhyung Park, Fanny Yang
Effects of Dropout on Performance in Long-range Graph Learning Tasks
Jasraj Singh, Keyue Jiang, Brooks Paige, Laura Toni
Many LLMs Are More Utilitarian Than One
Anita Keshmirian, Razan Baltaji, Babak Hemmatian, Hadi Asghari, Lav Varshney
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Ahmed Heakl, Yahia Salaheldin Shaaban, Salem Lahlou, Martin Takac, Zangir Iklassov
Marginal-Nonuniform PAC Learnability
Steve Hanneke, Shay Moran, Maximilian Thiessen
Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference
Weizhi Fei, Xueyan Niu, XIE GUOQING, Yingqing Liu, Bo Bai, Wei Han
Why Popular MOEAs are Popular: Proven Advantages in Approximating the Pareto Front
Mingfeng Li, Qiang Zhang, Weijie Zheng, Benjamin Doerr
Elastic ViTs from Pretrained Models without Retraining
Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort, Cees Snoek, Yuki Asano
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Tim Genewein, Kevin Li, Jordi Grau-Moya, Anian Ruoss, Laurent Orseau, Marcus Hutter
Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens
Zijian Dong, Ruilin Li, Joanna Chong, Niousha Dehestani, Yinghui Teng, Yi Lin, Zhizhou Li, Yichi Zhang, Yapei Xie, Leon Ooi, B.T. Yeo, Juan Helen Zhou
ConfTuner: Training Large Language Models to Express Their Confidence Verbally
Yibo Li, Miao Xiong, Jiaying Wu, Bryan Hooi
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning
Viet Anh Khoa Tran, Emre Neftci, Willem Wybo
Sparse Optimistic Information Directed Sampling
Hamish Flynn, Gergely Neu, Ludovic Schwartz
Offline imitation learning in $Q^\pi$-realizable MDPs without expert realizability
Antoine Moulin, Gergely Neu, Luca Viano
Distances for Markov chains from sample streams
Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas
Collapsing Taylor Mode Automatic Differentiation
Felix Dangel, Tim Siebert, Marius Zeinhofer, Andrea Walther
When Lower-Order Terms Dominate: Adaptive Expert Algorithms for Heavy-Tailed Losses
Antoine Moulin, Emmanuel Esposito, Dirk van der Hoeven
LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
Florian Sestak, Artur Toshev, Andreas Fürst, Günter Klambauer, Andreas Mayr, Johannes Brandstetter
Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
Andrea Pugnana, Riccardo Massidda, Francesco Giannini, Pietro Barbiero, Mateo Espinosa Zarlenga, Roberto Pellungrini, Gabriele Dominici, Fosca Giannotti, Davide Bacciu
SONAR: Long-Range Graph Propagation Through Information Waves
Alessandro Trenta, Alessio Gravina, Davide Bacciu
RespoDiff: Dual-Module Bottleneck Transformation for Responsible & Faithful T2I Generation
Silpa Vadakkeeveetil Sreelatha, Sauradip Nag, Muhammad Awais, Serge Belongie, Anjan Dutta
Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurelien Lucchi
Linear Attention for Efficient Bidirectional Sequence Modeling
Arshia Afzal, Elias Abad Rocamora, Leyla Candogan, Pol Puigdemont, Francesco Tonin, Yongtao Wu, Mahsa Shoaran, Volkan Cevher
Learning long range dependencies through time reversal symmetry breaking
Guillaume Pourcel, Maxence Ernoult
Deep variational inference with stochastic projections
Samuel Matthiesen, Hrittik Roy, Nicholas Krämer, Yevgen Zainchkovskyy, Stas Syrota, Alejandro Valverde Mahou, Carl Henrik Ek, Søren Hauberg
Ascent Fails to Forget
Ioannis Mavrothalassitis, Pol Puigdemont, Noam Levi, Volkan Cevher
Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?
Yihao Li, Saeed Salehi, Lyle Ungar, Konrad Kording
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, Esther Wershof, Sebastian Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Zichao Yan, Rory Stark, Kun Zhang, Thore Graepel
scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling
Joel Dapello, Marcel Nassar, Ridvan Eksi, Ban Wang, Jules Gagnon-Marchand, Kenneth Gao, akram Baharlouei, Kyra Thrush, Nina Riehs, Amy Peterson, Aniket Tolpadi, Abhejit Rajagopal, Henry Miller, Ashley Conard, David Alvarez-Melis, Rory Stark, Simone Bianco, Morgan Levine, Ava Amini, Alex X Lu, Nicolo Fusi, Ravi Pandya, Valentina Pedoia, Hana El-Samad
Geometry Aware Operator Transformer as an efficient and accurate neural surrogate for PDEs on arbitrary domains
Shizheng Wen, Arsh Kumbhat, Levi Lingsch, Sepehr Mousavi, Yizhou Zhao, Praveen Chandrashekar, Siddhartha Mishra
Transferring Causal Effects using Proxies
Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen, Jonas Peters
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
Jingfeng Wu, Pierre Marion, Peter Bartlett
Analog Foundation Models
Julian Büchel, Iason Chalas, Giovanni Acampa, An Chen, Omobayode Fagbohungbe, Hsinyu Tsai, Kaoutar El Maghraoui, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian
Scaling Image Geo-Localization to Continent Level
Philipp Lindenberger, Eduard Trulls, Paul-Edouard Sarlin, Jan Hosang, Marc Pollefeys, Simon Lynen
DeCaFlow: A deconfounding causal generative model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras, Santiago Zazo, Isabel Valera
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
Michael Plainer, Hao Wu, Leon Klein, Stephan Günnemann, Frank Noe
Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
Till Freihaut, Luca Viano, Volkan Cevher, Matthieu Geist, Giorgia Ramponi
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting
Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Jürgen Gall
Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
Jiang Qin, Alexandra Gomez-Villa, Senmao Li, Shiqi Yang, Yaxing Wang, KAI WANG, Joost van de Weijer
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
Image Super-Resolution with Guarantees via Conformalized Generative Models
Eduardo Adame, Daniel Csillag, Guilherme Tegoni Goedert
Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2
Joel Valdivia Ortega, Lorenz Lamm, Franziska Eckardt, Benedikt Schworm, Marion Jasnin, Tingying Peng
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan Williams, David Zoro, Amy Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles Cranmer, Nathan Kutz
RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
Wenzhe Ouyang, Qi Ye, Jinghua Wang, Zenglin Xu, Jiming Chen
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
Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
Tony Bonnaire, Raphaël Urfin, Giulio Biroli, Marc Mezard
Multi-Environment POMDPs: Discrete Model Uncertainty Under Partial Observability
Eline M. Bovy, Caleb Probine, Marnix Suilen, Ufuk Topcu, Nils Jansen
Incentivizing Time-Aware Fairness in Data Sharing
Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Sim, Arun Verma, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics
Indrashis Das, Mahmoud Safari, Steven Adriaensen, Frank Hutter
Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation
Chenyang Jiang, Hang Zhao, Xinyu Zhang, Zhengcen Li, Qiben Shan, Shaocong Wu, Jingyong Su
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
Diffusion-Guided Graph Data Augmentation
Maria Marrium, Arif Mahmood, Muhammad Haris Khan, M. Shakeel, Wenxiong Kang
IR-OptSet: An Optimization-Sensitive Dataset for Advancing LLM-Based IR Optimizer
Zi Yang, Lei Qiu, FANG LYU, Ming Zhong, Zhilei Chai, Haojie Zhou, Huimin Cui, Xiaobing Feng
MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents
Lukas Aichberger, Alasdair Paren, Guohao Li, Philip Torr, Yarin Gal, Adel Bibi
Optimal kernel regression bounds under energy-bounded noise
Amon Lahr, Johannes Köhler, Anna Scampicchio, Melanie Zeilinger
Unsupervised Trajectory Optimization for 3D Registration in Serial Section Electron Microscopy using Neural ODEs
Zhenbang Zhang, Jingtong Feng, Hongjia Li, Haythem El-Messiry, Zhiqiang Xu, Renmin Han
Safely Learning Controlled Stochastic Dynamics
Luc Brogat-Motte, Alessandro Rudi, Riccardo Bonalli
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang ZHANG, Yingzhen Li
Recurrent Memory for Online Interdomain Gaussian Processes
Wenlong Chen, Naoki Kiyohara, Harrison Zhu, Jacob Curran-Sebastian, Samir Bhatt, Yingzhen Li
Variational Uncertainty Decomposition for In-Context Learning
I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro, Wenlong Chen, Aldo Faisal, Yingzhen Li
Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
Naoki Kiyohara, Edward Johns, Yingzhen Li
Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Simon Matrenok, Skander Moalla, Caglar Gulcehre
Estimating protein stability using inverse folding models
Jes Frellsen, Maher Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg, Wouter Boomsma
Valid Selection among Conformal Sets
Mahmoud Hegazy, Liviu Aolaritei, Michael Jordan, Aymeric Dieuleveut
SpaceServe: Spatial Multiplexing of Complementary Encoders and Decoders for Multimodal LLMs
zhicheng li, Shuoming Zhang, Jiacheng Zhao, Siqi Li, Xiyu Shi, Yangyu Zhang, Shuaijiang Li, Donglin Yu, Zheming Yang, YUAN WEN, Huimin Cui
FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
Jiacheng Cui, Xinyue Bi, Yaxin Luo, Xiaohan Zhao, Jiacheng Liu, Zhiqiang Shen
Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents
Yaxin Luo, Zhaoyi Li, Jiacheng Liu, Jiacheng Cui, Xiaohan Zhao, Zhiqiang Shen
Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations
Gaia Di Lorenzo, Federico Tombari, Marc Pollefeys, Daniel Barath
An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Johanna Düngler, Amartya Sanyal
Orochi: Versatile Biomedical Image Processor
Gaole Dai, Chenghao Zhou, Yu Zhou, Rongyu Zhang, Yuan Zhang, Chengkai Hou, Tiejun Huang, Jianxu Chen, Shanghang Zhang
ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay, Zhensong Zhang, Greg Slabaugh, Eduardo Pérez-Pellitero
Covariate-moderated Empirical Bayes Matrix Factorization
William Denault, Karl Tayeb, Peter Carbonetto, Jason Willwerscheid, Matthew Stephens
Compressed and Smooth Latent Space for Text Diffusion Modeling
Viacheslav Meshchaninov, Egor Chimbulatov, Alexander Shabalin, Aleksandr Abramov, Dmitry Vetrov
TransferBench: Benchmarking Ensemble-based Black-box Transfer Attacks
Fabio Brau, Maura Pintor, Antonio Cinà, Raffaele Mura, Luca Scionis, Luca Oneto, Fabio Roli, Battista Biggio
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL
Kale-ab Tessera, Muhammad Arrasy Rahman, Amos Storkey, Stefano Albrecht
Causal Explanation-Guided Learning for Organ Allocation
Alessandro Marchese, Jeroen Berrevoets, Sam Verboven
Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic, Franziska Boenisch
ChemX: A Collection of Chemistry Datasets for Benchmarking Automated Information Extraction
Anastasia Vepreva, Julia Razlivina, Mariia Eremeyeva, Nina Gubina, Anastasia Orlova, Aleksei Dmitrenko, Kapranova Xenia, Susan Jyakhwo, Nikita Vasilev, Arsen Sarkisyan, Ivan Chernyshov, Vladimir Vinogradov, Andrei Dmitrenko
In Search of Adam’s Secret Sauce
Antonio Orvieto, Robert Gower
Generalized Linear Mode Connectivity for Transformers
Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis, Antonio Orvieto, Sidak Pal Singh, Valentina Boeva
FlashMD: long-stride, universal prediction of molecular dynamics
Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti
Position: Bridge the Gaps between Machine Unlearning and AI Regulation
Bill Marino, Meghdad Kurmanji, Nicholas Lane
LLM Unlearning via Neural Activation Redirection
William Shen, Xinchi Qiu, Meghdad Kurmanji, Alexandru-Andrei Iacob, Lorenzo Sani, Yihong Chen, Nicola Cancedda, Nicholas Lane
Decoding Causal Structure: End-to-End Mediation Pathways Inference
Yulong Li, Xiwei Liu, feilong tang, Ming Hu, Jionglong Su, Zongyuan Ge, Imran Razzak, Eran Segal
Do-PFN: In-context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf
Quantization-Free Autoregressive Action Transformer
Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach, Claire Vernade
PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs
Xinzhe Zheng, Hao Du, Fanding Xu, Jinzhe Li, ZHIYUAN LIU, Wenkang Wang, Tao Chen, Wanli Ouyang, Stan Z. Li, Yan Lu, Nanqing Dong, Yang Zhang
Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Dukre, feilong tang, Sara Atito, Sudipta Roy, Muhammad Awais, Muhammad Haris Khan, Imran Razzak
FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition
Pragya Singh, Ankush Gupta, Somay Jalan, Mohan Kumar, Pushpendra Singh
Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers
Thomas Klein, Sascha Meyen, Wieland Brendel, Felix A. Wichmann, Kristof Meding
Emergent Properties of Efficient Fine-Tuning in Text-to-Image Models
Komal Kumar, Rao Anwer, Fahad Shahbaz Khan, Salman Khan, Ivan Laptev, Hisham Cholakkal
Compositional World Modeling with Products of Programmatic Experts
Top Piriyakulkij, Yichao Liang, Hao Tang, Adrian Weller, Marta Kryven, Kevin Ellis
Distributed Multi-agent Bandits Over Erdős-Rényi Random Networks
Jingyuan Liu, Hao Qiu, Lin Yang, Mengfan Xu
Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
Shengkun Tang, Cong Zeng, Yuanzhou Chen, Zhiqiang Shen, Wenchao Yu, Xujiang Zhao, Haifeng Chen, Wei Cheng, Zhiqiang Xu
GaussianWorld: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
Mengjiao Ma, Qi Ma, Yue Li, Jiahuan Cheng, Runyi Yang, Bin Ren, Nikola Popovic, Mingqiang Wei, Nicu Sebe, Ender Konukoglu, Luc V Gool, Theo Gevers, Martin R. Oswald, Danda Pani Paudel
Neural Mutual Information Estimation with Vector Copulas
Yanzhi Chen, Zijing Ou, Adrian Weller, Michael Gutmann
HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passing
Johann Flemming Gloy, Simon Olsson
Scalable Feature Learning on Huge Knowledge Graphs for Downstream Machine Learning
Félix Lefebvre, Gael Varoquaux
On Evaluating Policies for Robust POMDPs
Merlijn Krale, Eline M. Bovy, Maris F. L. Galesloot, Thiago Simão, Nils Jansen
The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?
Denis Sutter, Julian Minder, Thomas Hofmann, Tiago Pimentel
Spike4DGS: Towards High-Speed Dynamic Scene Rendering with 4D Gaussian Splatting via a Spike Camera Array
Qinghong Ye, Yiqian Chang, Jianing Li, Haoran Xu, Wei Zhang, Xuan Wang, Yonghong Tian, Peixi Peng
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu, Lincen Yang, Mohammad Mohammadi Amiri, Niki van Stein, Matthijs van Leeuwen
Attention-based clustering
Rodrigo Maulen Soto, Claire Boyer, Pierre Marion
Towards A Translative Model of Sperm Whale Vocalization
Orr Paradise, Liangyuan Chen, Pranav Muralikrishnan, Hugo Flores, Bryan Pardo, Roee Diamant, David Gruber, Shane Gero, Shafi Goldwasser
ConTextTab: A Semantics-Aware Tabular In-Context Learner
Marco Spinaci, Marek Polewczyk, Maximilian Schambach, Sam Thelin
Semantic-KG: Using Knowledge Graphs to Construct Benchmarks for Measuring Semantic Similarity
Qiyao Wei, Edward R Morrell, Lea Goetz, Mihaela van der Schaar
Two Causally Related Needles in a Video Haystack
MIAOYU LI, Qin Chao, Boyang Li
Revisiting Frank-Wolfe for Structured Nonconvex Optimization
Hoomaan Maskan, Yikun Hou, Suvrit Sra, Alp Yurtsever
Kernel conditional tests from learning-theoretic bounds
Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck, Friedrich Solowjow, Sebastian Trimpe
Root Cause Analysis of Outliers with Missing Structural Knowledge
William Roy Orchard, Nastaran Okati, Sergio Garrido Mejia, Patrick Blöbaum, Dominik Janzing
Correlation Dimension of Autoregressive Large Language Models
Xin Du, Kumiko Tanaka-Ishii
Second-Order Convergence in Private Stochastic Non-Convex Optimization
Youming Tao, Zuyuan Zhang, Dongxiao Yu, Xiuzhen Cheng, Falko Dressler, Di Wang
The Narrow Gate: Localized Image-Text Communication in Vision-Language Models
Alessandro Serra, Francesco Ortu, Emanuele Panizon, Lucrezia Valeriani, Lorenzo Basile, Alessio Ansuini, Diego Doimo, Alberto Cazzaniga
Computational Efficiency under Covariate Shift in Kernel Ridge Regression
Andrea Della Vecchia, Arnaud Mavakala Watusadisi, Ernesto De Vito, Lorenzo Rosasco
vHector and HeisenVec: Scalable Vector Graphics Generation Through Large Language Models
Leonardo Zini, Elia Frigieri, Sebastiano Aloscari, Lorenzo Baraldi
EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identification
Lin Zhang, Wenshuo Dong, Zhuoran Zhang, Shu Yang, Lijie Hu, Ninghao Liu, Pan Zhou, Di Wang
Spiral: Semantic-Aware Progressive LiDAR Scene Generation
Dekai Zhu, Yixuan Hu, Youquan Liu, Dongyue Lu, Lingdong Kong, Slobodan Ilic
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
Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
Qing Zhao, Weijian Deng, Pengxu Wei, ZiYi Dong, hannan lu, Xiangyang Ji, Liang Lin
One Sample is Enough to Make Conformal Prediction Robust
Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski
Sequential Attention-based Sampling for Histopathological Analysis
Tarun Gogisetty, Naman Malpani, Gugan Chandrashekhar Mallika Thoppe, Sridharan Devarajan
BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models
Louis Kerner, Michel Meintz, Bihe Zhao, Franziska Boenisch, Adam Dziedzic
Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm
Clément Bénard
Physicist: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors
Yimeng Chen, Piotr Piękos, Mateusz Ostaszewski, Firas Laakom, Jürgen Schmidhuber
Learning single index models via harmonic decomposition
Hugo Koubbi, Nirmit Joshi, Theodor Misiakiewicz, Nati Srebro
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
Beyond Oracle: Verifier-Supervision for Instruction Hierarchy in Reasoning and Instruction-Tuned LLMs
Sian-Yao Huang, Li-Hsien Chang, Che-Yu Lin, Cheng-Lin Yang
CHASM: Unveiling Covert Advertisements on Chinese Social Media
Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng, Xinlei He
Don't Just Chase “Highlighted Tokens” in MLLMs: Revisiting Visual Holistic Context Retention
Xin Zou, Di Lu, Yizhou Wang, Yibo Yan, Yuanhuiyi Lyu, Xu Zheng, Linfeng Zhang, Xuming Hu
Learning from positive and unlabeled examples -Finite size sample bounds
Farnam Mansouri, Shai Ben-David
Unveiling Transformer Perception by Exploring Input Manifolds
Alessandro Benfenati, Alfio Ferrara, Alessio Marta, Davide Riva, Elisabetta Rocchetti
Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models
Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge Belongie, Zeynep Akata
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova, Rembert Daems, Christoph Knochenhauer, Manfred Opper, Tolga Birdal, Wojciech Samek
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
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
Dennis Frauen, Maresa Schröder, Konstantin Hess, Stefan Feuerriegel
Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World
Qinting Jiang, chuyang ye, Dongyan Wei, Bingli Wang, Yuan Xue, Jingyan Jiang, Zhi Wang
Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
Antonio Ribeiro, David Vävinggren, Dave Zachariah, Thomas Schön, Francis Bach
Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets
Zhiyuan Zhou, Yueming Yin, Yiming Yang, Yuguang Mu, Hoi-Yeung Li, Adams Wai Kin Kong
Subgraph Federated Learning via Spectral Methods
Javad Aliakbari, Johan Östman, Ashkan Panahi, Alexandre Graell i Amat
Feedback Guidance of Diffusion Models
Felix Koulischer, Florian Handke, Johannes Deleu, Thomas Demeester, Luca Ambrogioni
Strategic Cost Selection in Participatory Budgeting
Piotr Faliszewski, Łukasz Janeczko, Andrzej Kaczmarczyk, Grzegorz Lisowski, Piotr Skowron, Stanisław Szufa, Mateusz Szwagierczak
Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont, Maxime Darrin, Banafsheh Karimian, Eric Granger, Jackie CK Cheung, Ismail Ayed, Mohammadhadi Shateri, Pablo Piantanida
You Have to Be Realistic: On Investigating Feature Emergence in Deep Learning-based Side-channel Analysis
Sengim Karayalcin, Marina Krček, Stjepan Picek
Language Models Are Inefficient Reasoners: An Analysis on Arithmetic Proof Search
Andreas Opedal, Yanick Zengaffinen, Haruki Shirakami, Clemente Pasti, Mrinmaya Sachan, Abulhair Saparov, Ryan Cotterell, Bernhard Schölkopf
FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
Arthur Bizzi, Matias Grynberg Portnoy, Vitor Pereira Matias, Daniel Perazzo, João Paulo Silva do Monte Lima, Luiz Velho, Nuno Gonçalves, João Pereira, Guilherme Schardong, Tiago Novello
Pay Attention to Small Weights
chao zhou, Advait Gadhikar, Tom Jacobs, Rebekka Burkholz
Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach
Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Mélodie Monod, Alessandro Micheli, Samir Bhatt
Fixed-Point RNNs: Interpolating from Diagonal to Dense
Sajad Movahedi, Felix Sarnthein, Nicola Muca Cirone, Antonio Orvieto
Finding Low-Rank Matrix Weights in DNNs via Riemannian Optimization: RAdaGrad and RAadmW
Fengmiao Bian, Jinyang ZHENG, Ziyun Liu, Jianzhou Luo, Jian-Feng CAI
Learning-Augmented Online Bidding in Stochastic Settings
Spyros Angelopoulos, Bertrand Simon
The quest for the GRAph Level autoEncoder (GRALE)
Paul Krzakala, Gabriel Melo, Charlotte Laclau, Florence d'Alché-Buc, Rémi Flamary
Learning to Steer: Input-dependent Steering for Multimodal LLMs
Jayneel Parekh, Pegah KHAYATAN, Mustafa Shukor, Arnaud Dapogny, Alasdair Newson, Matthieu Cord
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
Redefining Experts: Interpretable Decomposition of Language Models for Toxicity Mitigation
Zuhair Hasan Shaik, Abdullah Mazhar, Aseem Srivastava, Md Shad Akhtar
Setting $\varepsilon$ is not the Issue in Differential Privacy
Edwige Cyffers
Revisiting Agnostic Boosting
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice, Yuxin Sun
On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity
Quentin Bertrand, Anne Gagneux, Mathurin Massias, Rémi Emonet
Generalizable Insights for Graph Transformers in Theory and Practice
Timo Stoll, Luis Müller, Christopher Morris
Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations
Kaibo Wang, Jianda Mao, Tong Wu, Yang Xiang
Private Training Large-scale Models with Efficient DP-SGD
Liangyu Wang, Junxiao Wang, Jie Ren, Zihang Xiang, David Keyes, Di Wang
DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method
Qingwen Zhang, Xiaomeng Zhu, Yushan Zhang, Yixi Cai, Olov Andersson, Patric Jensfelt
CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models
Ayan Banerjee, Fernando Vilariño,Josep Llados
Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen
SAL-V: Signal-Aware Learning for Verilog Code Generation
Yang Zhang, Rui Zhang, Jiaming Guo, Huang Lei, Di Huang, Yunpu Zhao, Shuyao Cheng, Pengwei Jin, Chongxiao Li, Zidong Du, Xing Hu, Qi Guo, Yunji Chen
A Circular Argument: Does RoPE need to be Equivariant for Vision?
Chase van de Geijn, Polina Turishcheva, Alexander Ecker, Timo Lüddecke
Do LLMs Comply Differently During Tests? And Can We Steer That?
Sahar Abdelnabi, Ahmed Salem
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
Bogdan Kulynych, Juan Gomez, Jamie Hayes, Borja Balle, Flavio Calmon, Georgios Kaissis, Jean Raisaro
Parameterized Synthetic Text Generation with SimpleStories
Lennart Finke, Chandan Sreedhara, Thomas Dooms, Mat Allen, Juan Rodriguez, Noa Nabeshima, Thomas Marshall, Dan Braun
How Benchmark Prediction from Fewer Data Misses the Mark
Guanhua Zhang, Florian E. Dorner, Moritz Hardt
Quantum Doubly Stochastic Transformers
Jannis Born, Filip Skogh, Nico Wagner, Kahn Rhrissorrakrai, Filippo Utro, Aleksandros Sobczyk
HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class
James Roggeveen, Erik Wang, Will Flintoft, Peter Donets, Raglan Ward, Ahmed Roman, Anton Graf, Siddharth Dandavate, Ava Williamson, David Ettel, Felix Yeung, Kacper Migacz, Yijun Wang, Egemen Bostan, Duy Thuc Nguyen, Zhe He, Marc Descoteaux, Anne Mykland, Shida Liu, Jorge Garcia Ponce, Luke Zhu, Yuyang Chen, Ekaterina Ivshina, Miguel Fernandez, Minjae Kim, Kennan Gumbs, Matthew Tan, Russell Yang, Mai Hoang, David Brown, Isabella Silveira, Lavon Sykes, Arjun Nageswaran, William Fredenberg, Yiming Chen, Lucas Martin, Yixing Tang, Kelly Smith, Hongyu Liao, Logan Wilson, Alexander D. Cai, Lucy Nathwani, Nickholas Gutierrez, Andrea Elizabeth Biju, Michael Brenner
Generalizing while preserving monotonicity in comparison-based preference learning models
Gilles Bareilles, Peva Blanchard, Julien Fageot, Lê-Nguyên Hoang
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs
Di He, Ajay Jaiswal, Songjun Tu, Li Shen, Ganzhao Yuan, Shiwei Liu, Lu Yin
GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling
Tianhao Chen, Xin Xu, Zijing Liu, Pengxiang Li, Xinyuan Song, AJAY JAISWAL, Fan Zhang, Jishan Hu, Yang Wang, Hao CHEN, Shizhe Diao, Shiwei Liu, Yu Li, Lu Yin, Can Yang
Uncertainty reduction of class conditional conformal prediction via multi-inputs aggregation
Jean-Baptiste Fermanian, Mohamed Hebiri, Joseph Salmon