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Balázs Kégl

4 accepted papers

2025

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
ICML 2025poster

Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these crit…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Zero-shot Model-based Reinforcement Learning using Large Language Models

Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo +3
ICLR 2025poster

The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state s…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2021

Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

Balázs Kégl, Gabriel Hurtado, Albert Thomas
ICLR 2021poster

We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗