5 citations · 25 across the 15 of their papers we have counts for
15 papers
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat +3
Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies a…
Knowledge-Informed Kernel State Reconstruction from Heterogeneous Partial Observations
Luca Muscarnera, Silas Ruhrberg Estévez, Samuel Holt +2
Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, noisy, and heterogeneous, providi…
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…
Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem
Qiyao Wei, Samuel Holt, Jing Yang +2
Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues suc…
Automatically Learning Hybrid Digital Twins of Dynamical Systems
Samuel Holt, Tennison Liu, Mihaela van der Schaar
Digital Twins (DTs) are computational models that simulate the states and temporal dynamics of real-world systems, playing a crucial role in prediction, understanding, and decision…
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets +2
Inferring unbiased treatment effects has received widespread attention in the machine learning community. In recent years, our community has proposed numerous solutions in standard…
Retrieval Augmented Thought Process for Private Data Handling in Healthcare
Thomas Pouplin, Hao Sun, Samuel Holt +1
Large Language Models (LLMs) have demonstrated the strong potential to assist both clinicians and the general public with their extensive medical knowledge. However, their applicat…
Dense Reward for Free in Reinforcement Learning from Human Feedback
Alex J. Chan, Hao Sun, Samuel Holt +1
Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce…
Deep Generative Symbolic Regression
Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar
Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challengi…
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…
Numerical simulation projects in micromagnetics with Jupyter
Martin Lonsky, Martin Lang, Samuel Holt +6
We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micr…
Neural Laplace Control for Continuous-time Delayed Systems
Samuel Holt, Alihan Hüyük, Zhaozhi Qian +2
Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays. Such environments are characterized by two distinctive features: firs…
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar
Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dep…