Blair L. Bilodeau

According to our database, Blair L. Bilodeau authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Don't trust your eyes: on the (un)reliability of feature visualizations.

Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

High-priority expected waiting times in the delayed accumulating priority queue with applications to health care KPIs.

Blair L. Bilodeau, David A. Stanford

INFOR Inf. Syst. Oper. Res., 2022

Impossibility Theorems for Feature Attribution.

Blair L. Bilodeau, Natasha Jaques, Pang Wei Koh, Been Kim

CoRR, 2022

Adaptively Exploiting d-Separators with Causal Bandits.

Blair L. Bilodeau, Linbo Wang, Daniel M. Roy

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers.

Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Simulated co-location of patients admitted to an inpatient internal medicine teaching unit: potential impacts on efficiency and physician-nurse collaboration.

Blair L. Bilodeau, David A. Stanford, Mark Goldszmidt, Andrew Appleton

INFOR Inf. Syst. Oper. Res., 2020

Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice.

Blair L. Bilodeau, Jeffrey Negrea, Daniel M. Roy

CoRR, 2020

Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance.

Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy

CoRR, 2020

Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance.

Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy

Proceedings of the 37th International Conference on Machine Learning, 2020