Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (ICML, 2020)

Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (CoRR, 2020)

Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice. - Blair L. Bilodeau; Jeffrey Negrea; Daniel M. Roy 001 (CoRR, 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)

Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. - Jeffrey Negrea; Blair L. Bilodeau; Nicolò Campolongo; Francesco Orabona; Daniel M. Roy 001 (NeurIPS, 2021)

Adaptively Exploiting d-Separators with Causal Bandits. - Blair L. Bilodeau; Linbo Wang 003; Daniel M. Roy 001 (NeurIPS, 2022)

Impossibility Theorems for Feature Attribution. - Blair L. Bilodeau; Natasha Jaques; Pang Wei Koh; Been Kim (CoRR, 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)

Don't trust your eyes: on the (un)reliability of feature visualizations. - Robert Geirhos; Roland S. Zimmermann; Blair L. Bilodeau; Wieland Brendel; Been Kim (ICML, 2024)