48 citations · 236 across the 62 of their papers we have counts for
8 papers · 1 filter
Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces
Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar
Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making. Many real-world tasks require findin…
CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
Nicolás Astorga, Anita Kriz, Mihaela van der Schaar
Despite surpassing human performance across mathematics, coding, and other knowledge-intensive tasks, large language models (LLMs) continue to struggle with causal reasoning. A cor…
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
Seokhyun Lee, Jaeho Kim, Changjun Oh +2
Time-series generative models often lack control over temporal granularity, forcing users to accept whatever granularity the model produces. To enable truly user-driven generation,…
Timely Clinical Diagnosis through Active Test Selection
Silas Ruhrberg Estévez, Nicolás Astorga, Mihaela van der Schaar
There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequenti…
Learning Reasoning Rewards from Expert Demonstrations with Inverse Reinforcement Learning
Claudio Fanconi, Nicolás Astorga, Mihaela van der Schaar
Teaching large language models (LLMs) to reason during post-training typically relies on reinforcement learning with explicit outcome- or process-based reward functions. However, i…
Preference Learning for AI Alignment: a Causal Perspective
Katarzyna Kobalczyk, Mihaela van der Schaar
Reward modelling from preference data is a crucial step in aligning large language models (LLMs) with human values, requiring robust generalisation to novel prompt-response pairs.…