5 citations · 12 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
Shi Bin Hoo, Samuel Müller, David Salinas +1
Recent progress in foundation models has enabled strong zero-shot performance for time series forecasting. In this work, we show that such capabilities can also emerge from tabular…