Alexis Bellot
23 papers · 2018–2025 · 7 conferences · across top CS/AI conferences
Achievements
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(37)
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(7)
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(10)
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(11)
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(2)
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Keyword Collector
(84)
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Prolific Year
(6)
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(8)
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Century Club
(23)
Conferences
NIPS (8)
AISTATS (4)
ICML (4)
UAI (4)
AAAI (1)
ICLR (1)
MLHC (1)
Top co-authors
Keywords
causal inference
(11)
survival analysis
(4)
domain adaptation
(3)
treatment effect
(3)
transfer learning
(2)
bayesian inference
(2)
two-sample test
(2)
observational datum
(2)
competing risk
(2)
controlled differential equation
(2)
hypothesis testing
(2)
policy evaluation
(2)
ensemble learning
(2)
reproducing kernel hilbert space
(2)
kernel methods
(2)
gaussian process
(1)
marginal likelihood
(1)
markov chain monte carlo
(1)
multitask learning
(1)
adversarial training
(1)
Papers
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
ICML 2025
The Limits of Predicting Agents from Behaviour
ICML 2025
Scores for Learning Discrete Causal Graphs with Unobserved Confounders
AAAI 2024
Mind the Graph When Balancing Data for Fairness or Robustness
NIPS 2024
Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
NIPS 2024
Efficient Policy Evaluation Across Multiple Different Experimental Datasets
NIPS 2024
Partial Transportability for Domain Generalization
NIPS 2024
Towards Bounding Causal Effects under Markov Equivalence
UAI 2024
Functional causal Bayesian optimization
UAI 2023
Transportability for Bandits with Data from Different Environments
NIPS 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
Policy Analysis using Synthetic Controls in Continuous-Time
ICML 2021
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
NIPS 2021
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
AISTATS 2020
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
AISTATS 2020
Boosting Transfer Learning with Survival Data from Heterogeneous Domains
AISTATS 2019
Conditional Independence Testing using Generative Adversarial Networks
NIPS 2019
Boosted Trees for Risk Prognosis
MLHC 2018
Multitask Boosting for Survival Analysis with Competing Risks
NIPS 2018
Tree-based Bayesian Mixture Model for Competing Risks
AISTATS 2018