2 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
Orthogonal Finetuning Made Scalable
Zeju Qiu, Weiyang Liu, Adrian Weller +1
Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deploymen…
Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models
David Guzman Piedrahita, Irene Strauss, Bernhard Schölkopf +2
As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior…
Improving Large Language Model Safety with Contrastive Representation Learning
Samuel Simko, Mrinmaya Sachan, Bernhard Schölkopf +1
Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to…
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo +3
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth c…
Counterfactual reasoning: an analysis of in-context emergence
Moritz Miller, Bernhard Schölkopf, Siyuan Guo
Large-scale neural language models exhibit remarkable performance in in-context learning: the ability to learn and reason about the input context on the fly. This work studies in-c…
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Frederike Lübeck, Jonas Wildberger, Frederik Träuble +4
Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the c…
From Pixels to Components: Eigenvector Masking for Visual Representation Learning
Alice Bizeul, Thomas Sutter, Alain Ryser +3
Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches o…