Aurélien Bibaut

Orcid: 0000-0002-3496-9296

Affiliations:
  • Netflix
  • University of California, Berkeley, Division of Biostatistics, Ca, USA


According to our database1, Aurélien Bibaut authored at least 11 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Learning the Covariance of Treatment Effects Across Many Weak Experiments.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2021
Statistical methods for causal inference fromsequentially collected data and sequential decisionmaking.
PhD thesis, 2021

Adaptive Sequential Design for a Single Time-Series.
CoRR, 2021

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Post-Contextual-Bandit Inference.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms.
CoRR, 2020

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019
On the Design of Estimators for Bandit Off-Policy Evaluation.
Proceedings of the 36th International Conference on Machine Learning, 2019

More Efficient Off-Policy Evaluation through Regularized Targeted Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

2017
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification.
CoRR, 2017


  Loading...