1.6k citations · 12.4k across the 394 of their papers we have counts for
154 papers · 1 filter
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
RAPTOR: Role-Aware Private Training for Mixture-of-Experts
Duc Dm, Khai Le-Duc, Nguyen Do +18
Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only se…
What Does a Discrete Diffusion Model Learn? One Reverse Process in Three Coordinates
Rodrigo Casado Noguerales, Bernhard Schölkopf, Thomas Hofmann +1
What does a discrete diffusion model actually learn? The literature trains denoisers, bridge plug-ins, and concrete scores, each deriving its own ELBO objective under different con…
Sensorimotor World Models: Perception for Action via Inverse Dynamics
Petr Ivashkov, Randall Balestriero, Bernhard Schölkopf
Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions. At the same time, latent JEPA-st…
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Rishit Dagli, Abir Harrasse, Luke Zhang +4
Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model chan…
PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective
Yangyi Huang, Ruotian Peng, Zeju Qiu +4
Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking t…