1 citations · 1 across the 7 of their papers we have counts for
7 papers
Multi-Objective Hierarchical Optimization with Large Language Models
Andrej Schwanke, Lyubomir Ivanov, David Salinas +2
Despite their widespread adoption in various domains, especially due to their powerful reasoning capabilities, Large Language Models (LLMs) are not the off-the-shelf choice to driv…
Improving LLM-based Global Optimization with Search Space Partitioning
Andrej Schwanke, Lyubomir Ivanov, David Salinas +4
Large Language Models (LLMs) have recently emerged as effective surrogate models and candidate generators within global optimization frameworks for expensive blackbox functions. De…
EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
Michael Arbel, David Salinas, Frank Hutter
Recent foundational models for tabular data, such as TabPFN, excel at adapting to new tasks via in-context learning, but remain constrained to a fixed, pre-defined number of target…
Tuning LLM Judge Design Decisions for 1/1000 of the Cost
David Salinas, Omar Swelam, Frank Hutter
Evaluating Large Language Models (LLMs) often requires costly human annotations. To address this, LLM-based judges have been proposed, which compare the outputs of two LLMs enablin…
One-shot World Models Using a Transformer Trained on a Synthetic Prior
Fabio Ferreira, Moreno Schlageter, Raghu Rajan +2
A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models a…
GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
Andreas Mueller, Julien Siems, Harsha Nori +4
While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack tr…
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…