1 citations · 2 across the 8 of their papers we have counts for
8 papers
GameTalk: Training LLMs for Strategic Conversation
Victor Conchello Vendrell, Max Ruiz Luyten, Mihaela van der Schaar
Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conv…
The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving
Max Ruiz Luyten, Mihaela van der Schaar
State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops: sampling diverse chains of thought and reinforcing the highest-scoring ones, mainly opti…
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon +1
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often st…
Fact-Augmented Lookahead Planning for LLM Agents
Samuel Holt, Max Ruiz Luyten, Thomas Pouplin +1
Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search…
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten +1
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on differen…
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples
Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar
Diffusion models are mainly studied on image data. However, non-image data (e.g., tabular data) are also prevalent in real applications and tend to be noisy due to some inevitable…
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar
Transformer-based large language models (LLMs) are constrained by the fixed context window of the underlying transformer architecture, hindering their ability to produce long and c…
Transfer Learning with Kernel Methods
Adityanarayanan Radhakrishnan, Max Ruiz Luyten, Neha Prasad +1
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine lear…