5 citations · 12 across the 13 of their papers we have counts for
11 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…
From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
Bora Kargi, David Salinas
Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM-as-a-judge offers a cheaper alternative, but judge scores carry systematic e…
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…
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…
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems +2
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba…
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…
Deep Non-Parametric Time Series Forecaster
Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella +4
This paper presents non-parametric baseline models for time series forecasting. Unlike classical forecasting models, the proposed approach does not assume any parametric form for t…
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
David Salinas, Nick Erickson
We introduce TabRepo, a new dataset of tabular model evaluations and predictions. TabRepo contains the predictions and metrics of 1310 models evaluated on 200 classification and re…