Tree-based Bayesian Mixture Model for Competing Risks. - Alexis Bellot; Mihaela van der Schaar (AISTATS, 2018)

Boosted Trees for Risk Prognosis. - Alexis Bellot; Mihaela van der Schaar (MLHC, 2018)

Multitask Boosting for Survival Analysis with Competing Risks. - Alexis Bellot; Mihaela van der Schaar (NeurIPS, 2018)

Boosting Transfer Learning with Survival Data from Heterogeneous Domains. - Alexis Bellot; Mihaela van der Schaar (AISTATS, 2019)

Conditional Independence Testing using Generative Adversarial Networks. - Alexis Bellot; Mihaela van der Schaar (NeurIPS, 2019)

A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records. - Alexis Bellot; Mihaela van der Schaar (CoRR, 2019)

A Hierarchical Bayesian Model for Personalized Survival Predictions. - Alexis Bellot; Mihaela van der Schaar (IEEE J. Biomed. Health Informatics, 2019)

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. - Zhaozhi Qian; Ahmed M. Alaa; Alexis Bellot; Mihaela van der Schaar; Jem Rashbass (AISTATS, 2020)

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. - Yao Zhang; Alexis Bellot; Mihaela van der Schaar (AISTATS, 2020)

Generalization and Invariances in the Presence of Unobserved Confounding. - Alexis Bellot; Mihaela van der Schaar (CoRR, 2020)

Flexible Modelling of Longitudinal Medical Data: A Bayesian Nonparametric Approach. - Alexis Bellot; Mihaela van der Schaar (ACM Trans. Comput. Heal., 2020)

Policy Analysis using Synthetic Controls in Continuous-Time. - Alexis Bellot; Mihaela van der Schaar (ICML, 2021)

MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. - Trent Kyono; Yao Zhang; Alexis Bellot; Mihaela van der Schaar (NeurIPS, 2021)

Application of kernel hypothesis testing on set-valued data. - Alexis Bellot; Mihaela van der Schaar (UAI, 2021)

A kernel two-sample test with selection bias. - Alexis Bellot; Mihaela van der Schaar (UAI, 2021)

Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding. - Alexis Bellot; Mihaela van der Schaar (CoRR, 2021)

Consistency of mechanistic causal discovery in continuous-time using Neural ODEs. - Alexis Bellot; Kim Branson 001; Mihaela van der Schaar (CoRR, 2021)

Neural graphical modelling in continuous-time: consistency guarantees and algorithms. - Alexis Bellot; Kim Branson 001; Mihaela van der Schaar (ICLR, 2022)

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. - Nabeel Seedat; Fergus Imrie; Alexis Bellot; Zhaozhi Qian; Mihaela van der Schaar (ICML, 2022)

Generalization bounds and algorithms for estimating conditional average treatment effect of dosage. - Alexis Bellot; Anish Dhir; Giulia Prando (CoRR, 2022)

Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. - Martin Mundt; Keiland W. Cooper; Devendra Singh Dhami; Adèle H. Ribeiro; James Seale Smith; Alexis Bellot; Tyler L. Hayes (AAAI Bridge Program, 2023)

Transportability for Bandits with Data from Different Environments. - Alexis Bellot; Alan Malek; Silvia Chiappa (NeurIPS, 2023)

Functional causal Bayesian optimization. - Limor Gultchin; Virginia Aglietti; Alexis Bellot; Silvia Chiappa (UAI, 2023)

Scores for Learning Discrete Causal Graphs with Unobserved Confounders. - Alexis Bellot; Junzhe Zhang 001; Elias Bareinboim (AAAI, 2024)

Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding. - Alexis Bellot; Silvia Chiappa (NeurIPS, 2024)

Partial Transportability for Domain Generalization. - Kasra Jalaldoust; Alexis Bellot; Elias Bareinboim (NeurIPS, 2024)

Efficient Policy Evaluation Across Multiple Different Experimental Datasets. - Yonghan Jung; Alexis Bellot (NeurIPS, 2024)

Mind the Graph When Balancing Data for Fairness or Robustness. - Jessica Schrouff; Alexis Bellot; Amal Rannen-Triki; Alan Malek; Isabela Albuquerque; Arthur Gretton; Alexander D'Amour; Silvia Chiappa (NeurIPS, 2024)

Towards Bounding Causal Effects under Markov Equivalence. - Alexis Bellot (UAI, 2024)

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. - Virginia Aglietti; Ira Ktena; Jessica Schrouff; Eleni Sgouritsa; Francisco J. R. Ruiz; Alan Malek; Alexis Bellot; Silvia Chiappa (CoRR, 2024)

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding. - Alexis Bellot; Mihaela van der Schaar (IEEE Trans. Neural Networks Learn. Syst., 2024)

Evaluating the Goal-Directedness of Large Language Models. - Tom Everitt; Cristina Garbacea; Alexis Bellot; Jonathan Richens; Henry Papadatos; Siméon Campos; Rohin Shah (CoRR, 2025)

General agents need world models. - Jonathan Richens; David Abel; Alexis Bellot; Tom Everitt (CoRR, 2025)

The Limits of Predicting Agents from Behaviour. - Alexis Bellot; Jonathan Richens; Tom Everitt (CoRR, 2025)