Alexis Bellot
23 papers · 2018–2025 · 7 venues · across top CS/AI venues
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Machine Learning
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Optimization & Theory
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(7)
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(6)
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Keywords
causal inference
(11)
survival analysis
(4)
domain adaptation
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observational data
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treatment effect
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bayesian inference
(2)
competing risk
(2)
two-sample test
(2)
policy evaluation
(2)
bayesian optimization
(2)
causal intervention
(2)
controlled differential equation
(2)
transfer learning
(2)
kernel methods
(2)
reproducing kernel hilbert space
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ensemble learning
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hypothesis testing
(2)
markov chain monte carlo
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adversarial training
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hyperparameter optimization
(1)
Papers
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
ICML 2025
The Limits of Predicting Agents from Behaviour
ICML 2025
Efficient Policy Evaluation Across Multiple Different Experimental Datasets
NeurIPS 2024
Mind the Graph When Balancing Data for Fairness or Robustness
NeurIPS 2024
Partial Transportability for Domain Generalization
NeurIPS 2024
Scores for Learning Discrete Causal Graphs with Unobserved Confounders
AAAI 2024
Towards Bounding Causal Effects under Markov Equivalence
UAI 2024
Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
NeurIPS 2024
Functional causal Bayesian optimization
UAI 2023
Transportability for Bandits with Data from Different Environments
NeurIPS 2023
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
ICML 2022
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
ICLR 2022
A kernel two-sample test with selection bias
UAI 2021
Application of kernel hypothesis testing on set-valued data
UAI 2021
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
NeurIPS 2021
Policy Analysis using Synthetic Controls in Continuous-Time
ICML 2021
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
AISTATS 2020
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
AISTATS 2020
Boosting Transfer Learning with Survival Data from Heterogeneous Domains
AISTATS 2019
Conditional Independence Testing using Generative Adversarial Networks
NeurIPS 2019
Boosted Trees for Risk Prognosis
MLHC 2018
Multitask Boosting for Survival Analysis with Competing Risks
NeurIPS 2018
Tree-based Bayesian Mixture Model for Competing Risks
AISTATS 2018