Author name cluster

Nicolás Astorga

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6 papers

2 author rows

ICLR Conference 2025 Conference Paper

Active Task Disambiguation with LLMs

Kasia Kobalczyk
Nicolás Astorga
Tennison Liu
Mihaela van der Schaar

Despite the impressive performance of large language models (LLMs) across various benchmarks, their ability to address ambiguously specified problems—frequent in real-world interactions—remains underexplored. To address this gap, we introduce a formal definition of task ambiguity and frame the problem of task disambiguation through the lens of Bayesian Experimental Design. By posing clarifying questions, LLM agents can acquire additional task specifications, progressively narrowing the space of viable solutions and reducing the risk of generating unsatisfactory outputs. Yet, generating effective clarifying questions requires LLM agents to engage in a form of meta-cognitive reasoning, an ability LLMs may presently lack. Our proposed approach of active task disambiguation enables LLM agents to generate targeted questions maximizing the information gain. Effectively, this approach shifts the load from implicit to explicit reasoning about the space of viable solutions. Empirical results demonstrate that this form of question selection leads to more effective task disambiguation in comparison to approaches relying on reasoning solely within the space of questions.

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ICML Conference 2025 Conference Paper

Autoformulation of Mathematical Optimization Models Using LLMs

Nicolás Astorga
Tennison Liu
Yuanzhang Xiao
Mihaela van der Schaar

Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a difficult task, often demanding specialized expertise. This paper approaches the problem of $\textit{autoformulation}$: the automated creation of solver-ready optimization models from natural language problem descriptions. We identify three core challenges of autoformulation: $\textit{(1)}$ the vast, problem-dependent hypothesis space, $\textit{(2)}$ efficient and diverse exploration of this space under uncertainty, and $\textit{(3)}$ evaluation of formulation correctness against problem description. To address these challenges, we present a novel method leveraging $\textit{Large Language Models}$ (LLMs) with $\textit{Monte-Carlo Tree Search}$, exploiting the hierarchical nature of optimization modeling to generate and systematically explore possible formulations. To enhance search efficiency, we introduce symbolic pruning to eliminate trivially equivalent search paths (branches), and employ LLM-based evaluation of partial formulations to guide search. Empirical analysis on linear and mixed-integer programming benchmarks demonstrates our method’s effectiveness, with significant performance gains from both LLM-based value estimation and symbolic pruning techniques.

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ICML Conference 2025 Conference Paper

Continuously Updating Digital Twins using Large Language Models

Harry Amad
Nicolás Astorga
Mihaela van der Schaar

Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or incorporate new information without re-training. To address this, we frame digital twinning as an in-context learning problem using large language models, enabling seamless updates to the twin at inference time. We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces using in-context learning alone by utilising fine-tuned encoders for sample retrieval. We empirically demonstrate CALM-DT’s competitive performance with existing digital twin approaches, and its unique ability to adapt to changes in its modelling environment without parameter updates.

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NeurIPS Conference 2025 Conference Paper

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 sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.

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NeurIPS Conference 2024 Conference Paper

Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios

Nicolás Astorga
Tennison Liu
Nabeel Seedat
Mihaela van der Schaar

Conducting experiments and gathering data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive _experiments_ to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs. To perform this task, existing data acquisition methods assume the availability of an initial dataset that is both fully-observed and labeled, crucially overlooking the **partial observability** of features characteristic of many real-world scenarios. In response to this challenge, we present Partially Observable Cost-Aware Active-Learning (POCA), a new learning approach aimed at improving model generalization in data-scarce and data-costly scenarios through label and/or feature acquisition. Introducing $\mu$POCA as an instantiation, we maximise the uncertainty reduction in the predictive model when obtaining labels and features, considering associated costs. $\mu$POCA enhance traditional Active Learning metrics based solely on the observed features by generating the unobserved features through Generative Surrogate Models, particularly Large Language Models (LLMs). We empirically validate $\mu$POCA across diverse tabular datasets, varying data availability, acquisition costs, and LLMs.

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ICLR Conference 2024 Conference Paper

Large Language Models to Enhance Bayesian Optimization

Tennison Liu
Nicolás Astorga
Nabeel Seedat
Mihaela van der Schaar

Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its efficacy depends on efficiently balancing exploration and exploitation. While there has been substantial progress in BO methods, striking this balance remains a delicate process. In this light, we present \texttt{LLAMBO}, a novel approach that integrates the capabilities of Large Language Models (LLM) within BO. At a high level, we frame the BO problem in natural language, enabling LLMs to iteratively \emph{propose} and \emph{evaluate} promising solutions conditioned on historical evaluations. More specifically, we explore how combining contextual understanding, few-shot learning proficiency, and domain knowledge of LLMs can improve model-based BO. Our findings illustrate that \texttt{LLAMBO} is effective at zero-shot warmstarting, and enhances surrogate modeling and candidate sampling, especially in the early stages of search when observations are sparse. Our approach is performed in context and does not require LLM finetuning. Additionally, it is modular by design, allowing individual components to be integrated into existing BO frameworks, or function cohesively as an end-to-end method. We empirically validate \texttt{LLAMBO}'s efficacy on the problem of hyperparameter tuning, highlighting strong empirical performance across a range of diverse benchmarks, proprietary, and synthetic tasks.

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