Papers
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer
Blake Bordelon, Cengiz Pehlevan
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
Clément Bonet, Christophe Vauthier, Anna Korba
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
Rogerio Bonatti, Dan Zhao, Francesco Bonacci et al.
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
Simone Bombari, Marco Mondelli
Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency
Georg Bökman, David Nordström, Fredrik Kahl
Temporal Misalignment in ANN-SNN Conversion and its Mitigation via Probabilistic Spiking Neurons
Velibor Bojkovic, Xiaofeng Wu, Bin Gu
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
Thibaut Boissin, Franck Mamalet, Thomas Fel et al.
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang et al.
A Variational Perspective on Generative Protein Fitness Optimization
Lea Bogensperger, Dominik Narnhofer, Ahmed Allam et al.
Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound
David Boetius, Stefan Leue, Tobias Sutter
Generative Social Choice: The Next Generation
Niclas Boehmer, Sara Fish, Ariel D. Procaccia
Physics-Informed Generative Modeling of Wireless Channels
Benedikt Böck, Andreas Oeldemann, Timo Mayer et al.
Point Cloud Dataset Distillation
Deyu Bo, Xinchao Wang
Probably Approximately Global Robustness Certification
Peter Blohm, Patrick Indri, Thomas Gärtner et al.
GaussMark: A Practical Approach for Structural Watermarking of Language Models
Adam Block, Alexander Rakhlin, Ayush Sekhari
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
Jan Blechschmidt, Tom-Christian Riemer, Max Winkler et al.
False Coverage Proportion Control for Conformal Prediction
Alexandre Blain, Bertrand Thirion, Pierre Neuvial
Universal Approximation of Mean-Field Models via Transformers
Shiba Biswal, Karthik Elamvazhuthi, Rishi Sonthalia
ENSUR: Equitable and Statistically Unbiased Recommendation
Nitin Bisht, Xiuwen Gong, Guandong Xu
Learning Likelihood-Free Reference Priors
Nicholas George Bishop, Daniel Jarne Ornia, Joel Dyer et al.
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel F. Langer, Michele Ceriotti
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou
Rethinking Aleatoric and Epistemic Uncertainty
Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope et al.
Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism
Aviv Bick, Eric Xing, Albert Gu