Alicia Curth
16 papers · 2021–2024 · 5 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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๐ฃ Hot Topic Early Bird ๐บ๏ธ Taxonomy Completionist (30) ๐ Interdisciplinary Bridge ยท Silver ๐ Conference Polyglot ยท Silver ๐ฌ Deep Specialist (10)
๐ค
Dynamic Duo
(15)
๐
Triple Crown
๐
Century Club ยท Bronze
๐๏ธ
Keyword Collector
(63)
โก
Prolific Year ยท Productive
Top co-authors
Research topics
Machine Learning
(15)
Artificial Intelligence
(12)
Core AI
(11)
Learning Types
(8)
Core Methods
(6)
Optimization & Theory
(6)
Application Areas
(4)
Healthcare & Medicine
(3)
Clinical
(3)
Deep Learning
(2)
Data Science & Analytics
(1)
Mathematics & Optimization
(1)
Applications
(1)
Architectures
(1)
Bayesian & Probabilistic
(1)
Keywords
causal inference
(10)
heterogeneous treatment effect
(6)
treatment effect
(4)
covariate shift
(3)
treatment effect estimation
(3)
potential outcome
(3)
survival analysis
(2)
inductive bia
(2)
domain adaptation
(1)
model complexity
(1)
model selection
(1)
nonparametric estimation
(1)
gradient boosting
(1)
generalization error
(1)
double descent
(1)
feature importance
(1)
neural network training
(1)
informative sampling
(1)
domain knowledge
(1)
machine learning
(1)
Papers
A Neural Framework for Generalized Causal Sensitivity Analysis
ICLR 2024
Cautionary Tales on Synthetic Controls in Survival Analyses
CLeaR 2024
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
NeurIPS 2024
Defining Expertise: Applications to Treatment Effect Estimation
ICLR 2024
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
ICML 2023
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
ICML 2023
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
NeurIPS 2023
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
ICML 2023
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
AISTATS 2023
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
NeurIPS 2022
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
ICML 2022
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
ICLR 2022
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
NeurIPS 2021
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
AISTATS 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation
NeurIPS 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
NeurIPS 2021