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Alexis Bellot
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2020 – today
- 2024
- [j3]Alexis Bellot
, Mihaela van der Schaar:
Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4948-4962 (2024) - [c17]Alexis Bellot, Junzhe Zhang, Elias Bareinboim:
Scores for Learning Discrete Causal Graphs with Unobserved Confounders. AAAI 2024: 11043-11051 - [i15]Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa:
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. CoRR abs/2406.04824 (2024) - [i14]Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa:
Mind the Graph When Balancing Data for Fairness or Robustness. CoRR abs/2406.17433 (2024) - 2023
- [c16]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. AAAI Bridge Program 2023: 1-10 - [c15]Alexis Bellot, Alan Malek, Silvia Chiappa:
Transportability for Bandits with Data from Different Environments. NeurIPS 2023 - [c14]Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa:
Functional causal Bayesian optimization. UAI 2023: 756-765 - [e1]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
AAAI Bridge Program on Continual Causality, 7-8 February 2023, Washington, DC, USA. Proceedings of Machine Learning Research 208, PMLR 2023 [contents] - [i13]Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa:
Functional Causal Bayesian Optimization. CoRR abs/2306.06409 (2023) - [i12]Alexis Bellot:
Towards Bounding Causal Effects under Markov Equivalence. CoRR abs/2311.07259 (2023) - 2022
- [c13]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Neural graphical modelling in continuous-time: consistency guarantees and algorithms. ICLR 2022 - [c12]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. ICML 2022: 19497-19521 - [i11]Alexis Bellot, Anish Dhir, Giulia Prando:
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage. CoRR abs/2205.14692 (2022) - [i10]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. CoRR abs/2206.08311 (2022) - 2021
- [c11]Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. ICML 2021: 759-768 - [c10]Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. NeurIPS 2021: 23806-23817 - [c9]Alexis Bellot, Mihaela van der Schaar:
Application of kernel hypothesis testing on set-valued data. UAI 2021: 194-204 - [c8]Alexis Bellot, Mihaela van der Schaar:
A kernel two-sample test with selection bias. UAI 2021: 205-214 - [i9]Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. CoRR abs/2102.01577 (2021) - [i8]Alexis Bellot, Mihaela van der Schaar:
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding. CoRR abs/2103.15106 (2021) - [i7]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Consistency of mechanistic causal discovery in continuous-time using Neural ODEs. CoRR abs/2105.02522 (2021) - [i6]Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. CoRR abs/2111.03187 (2021) - 2020
- [j2]Alexis Bellot, Mihaela van der Schaar:
Flexible Modelling of Longitudinal Medical Data: A Bayesian Nonparametric Approach. ACM Trans. Comput. Heal. 1(1): 3:1-3:15 (2020) - [c7]Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. AISTATS 2020: 1005-1014 - [c6]Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Mihaela van der Schaar, Jem Rashbass:
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. AISTATS 2020: 3295-3305 - [i5]Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Jem Rashbass, Mihaela van der Schaar:
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. CoRR abs/2001.02585 (2020) - [i4]Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. CoRR abs/2001.04754 (2020) - [i3]Alexis Bellot, Mihaela van der Schaar:
Generalization and Invariances in the Presence of Unobserved Confounding. CoRR abs/2007.10653 (2020)
2010 – 2019
- 2019
- [j1]Alexis Bellot
A Hierarchical Bayesian Model for Personalized Survival Predictions. IEEE J. Biomed. Health Informatics 23(1): 72-80 (2019) - [c5]Alexis Bellot, Mihaela van der Schaar:
Boosting Transfer Learning with Survival Data from Heterogeneous Domains. AISTATS 2019: 57-65 - [c4]Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. NeurIPS 2019: 2199-2208 - [i2]Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. CoRR abs/1907.04068 (2019) - [i1]Alexis Bellot, Mihaela van der Schaar:
A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records. CoRR abs/1912.09086 (2019) - 2018
- [c3]Alexis Bellot, Mihaela van der Schaar:
Tree-based Bayesian Mixture Model for Competing Risks. AISTATS 2018: 910-918 - [c2]Alexis Bellot, Mihaela van der Schaar:
Boosted Trees for Risk Prognosis. MLHC 2018: 2-16 - [c1]Alexis Bellot, Mihaela van der Schaar:
Multitask Boosting for Survival Analysis with Competing Risks. NeurIPS 2018: 1397-1406
Coauthor Index
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