322 citations · 432 across the 23 of their papers we have counts for
24 papers
Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization
Yan Scholten, Rachel Lawrence, James Hensman +3
Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but quantization errors then compou…
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth +2
How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused on agent skills: reusable capab…
Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task
Alicia Curth, Rachel Lawrence, Sushrut Karmalkar +1
We investigate whether transformers use their depth adaptively across tasks of increasing difficulty. Using a controlled multi-hop relational reasoning task based on family stories…
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
Alan Jeffares, Alicia Curth, Mihaela van der Schaar
Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate mo…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs
Alicia Curth
The sudden appearance of modern machine learning (ML) phenomena like double descent and benign overfitting may leave many classically trained statisticians feeling uneasy -- these…
Defining Expertise: Applications to Treatment Effect Estimation
Alihan Hüyük, Qiyao Wei, Alicia Curth +1
Decision-makers are often experts of their domain and take actions based on their domain knowledge. Doctors, for instance, may prescribe treatments by predicting the likely outcome…
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
Despite their remarkable effectiveness and broad application, the drivers of success underlying ensembles of trees are still not fully understood. In this paper, we highlight how i…
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth +3
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to…
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a trans…
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Toon Vanderschueren, Alicia Curth, Wouter Verbeke +1
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment str…
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
Alicia Curth, Mihaela van der Schaar
We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this pro…
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
Alicia Curth, Mihaela van der Schaar
Personalized treatment effect estimates are often of interest in high-stakes applications -- thus, before deploying a model estimating such effects in practice, one needs to be sur…
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial…
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Jonathan Crabbé, Alicia Curth, Ioana Bica +1
Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatm…
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan Cebere, Tennison Liu +2
Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability o…
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Alex J. Chan, Alicia Curth, Mihaela van der Schaar
Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability t…
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt, Jeroen Berrevoets, Alicia Curth +2
Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from ob…
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Jeroen Berrevoets, Alicia Curth, Ioana Bica +2
Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observ…
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
Alicia Curth, Changhee Lee, Mihaela van der Schaar
We study the problem of inferring heterogeneous treatment effects from time-to-event data. While both the related problems of (i) estimating treatment effects for binary or continu…
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
Alicia Curth, Mihaela van der Schaar
The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on…
On Inductive Biases for Heterogeneous Treatment Effect Estimation
Alicia Curth, Mihaela van der Schaar
We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treat…
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Alicia Curth, Mihaela van der Schaar
The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a…
Estimating Structural Target Functions using Machine Learning and Influence Functions
Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar
We aim to construct a class of learning algorithms that are of practical value to applied researchers in fields such as biostatistics, epidemiology and econometrics, where the need…