26 citations · 69 across the 20 of their papers we have counts for
8 papers · 1 filter
Linear Independent Component Analysis via Optimal Transport
Ashutosh Jha, Michel Besserve, Simon Buchholz
Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize no…
Logit Distance Bounds Representational Similarity
Beatrix M. G. Nielsen, Emanuele Marconato, Luigi Gresele +2
For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions…
Reparameterized LLM Training via Orthogonal Equivalence Transformation
Zeju Qiu, Simon Buchholz, Tim Z. Xiao +3
While large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's mos…
Algorithmic causal structure emerging through compression
Liang Wendong, Simon Buchholz, Bernhard Schölkopf
We explore the relationship between causality, symmetry, and compression. We build on and generalize the known connection between learning and compression to a setting where causal…
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
Goutham Rajendran, Simon Buchholz, Bryon Aragam +2
To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causa…
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld +3
We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function i…
Flow Matching for Scalable Simulation-Based Inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz +3
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional pro…
AutoML Two-Sample Test
Jonas M. Kübler, Vincent Stimper, Simon Buchholz +2
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of m…