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9 papers · 1 filter
Can Synthetic Data Improve Symbolic Regression Extrapolation Performance?
Fitria Wulandari Ramlan, Colm O'Riordan, Gabriel Kronberger +1
Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR…
Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Bram Silue, Santiago Amaya-Corredor, Patrick Mannion +2
Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation
Muzhaffar Hazman, Susan McKeever, Josephine Griffith
Warning: This paper contains memes that may be offensive to some readers. Multimodal Internet Memes are now a ubiquitous fixture in online discourse. One strand of meme-based resea…
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected ut…
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers +2
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decision…