599 citations · 1.6k across the 142 of their papers we have counts for
14 papers · 1 filter
What's the next frontier for Data-centric AI? Data Savvy Agents
Nabeel Seedat, Jiashuo Liu, Mihaela van der Schaar
The recent surge in AI agents that autonomously communicate, collaborate with humans and use diverse tools has unlocked promising opportunities in various real-world settings. Howe…
Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models
Christopher Chiu, Silviu Pitis, Mihaela van der Schaar
Clinical reasoning in medicine is a hypothesis-driven process where physicians refine diagnoses from limited information through targeted history, physical examination, and diagnos…
Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents
Tennison Liu, Silas Ruhrberg Estévez, David L. Bentley +1
Large-scale scientific datasets -- spanning health biobanks, cell atlases, Earth reanalyses, and more -- create opportunities for exploratory discovery unconstrained by specific re…
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
Konstantin Hess, Dennis Frauen, Mihaela van der Schaar +1
Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases expone…
Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models
Paulius Rauba, Mihaela van der Schaar
Large neural networks are typically trained for a fixed computational budget, creating a rigid trade-off between performance and efficiency that is ill-suited for deployment in res…
Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe, Changhee Lee, Fergus Imrie +2
Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, indepen…
Interpretable Reward Modeling with Active Concept Bottlenecks
Sonia Laguna, Katarzyna Kobalczyk, Julia E. Vogt +1
We introduce Concept Bottleneck Reward Models (CB-RM), a reward modeling framework that enables interpretable preference learning through selective concept annotation. Unlike stand…
Not All Explanations for Deep Learning Phenomena Are Equally Valuable
Alan Jeffares, Mihaela van der Schaar
Developing a better understanding of surprising or counterintuitive phenomena has constituted a significant portion of deep learning research in recent years. These include double…
Statistical Hypothesis Testing for Auditing Robustness in Language Models
Paulius Rauba, Qiyao Wei, Mihaela van der Schaar
Consider the problem of testing whether the outputs of a large language model (LLM) system change under an arbitrary intervention, such as an input perturbation or changing the mod…
Language Bottleneck Models for Qualitative Knowledge State Modeling
Antonin Berthon, Mihaela van der Schaar
Accurately assessing student knowledge is central to education. Cognitive Diagnosis (CD) models estimate student proficiency at a fixed point in time, while Knowledge Tracing (KT)…
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…
Position: All Current Generative Fidelity and Diversity Metrics are Flawed
Ossi Räisä, Boris van Breugel, Mihaela van der Schaar
Any method's development and practical application is limited by our ability to measure its reliability. The popularity of generative modeling emphasizes the importance of good syn…
Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets
Simpson Zhang, Tennison Liu, Mihaela van der Schaar
Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to …
OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models
Hao Sun, Yunyi Shen, Mihaela van der Schaar
In the era of large language models (LLMs), high-quality, domain-rich, and continuously evolving datasets capturing expert-level knowledge, core human values, and reasoning are inc…