Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. - Jeffrey Negrea; Mahdi Haghifam; Gintare Karolina Dziugaite; Ashish Khisti; Daniel M. Roy 001 (NeurIPS, 2019)

In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. - Jeffrey Negrea; Gintare Karolina Dziugaite; Daniel M. Roy 001 (ICML, 2020)

Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. - Mahdi Haghifam; Jeffrey Negrea; Ashish Khisti; Daniel M. Roy 001; Gintare Karolina Dziugaite (NeurIPS, 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)

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)

Statistical Inference with Stochastic Gradient Algorithms. - Jeffrey Negrea; Jun Yang; Haoyue Feng; Daniel M. Roy 001; Jonathan H. Huggins (CoRR, 2022)

Concept Algebra for (Score-Based) Text-Controlled Generative Models. - Zihao Wang; Lin Gui 007; Jeffrey Negrea; Victor Veitch (NeurIPS, 2023)

Concept Algebra for Text-Controlled Vision Models. - Zihao Wang; Lin Gui 007; Jeffrey Negrea; Victor Veitch (CoRR, 2023)

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms. - Xiaoyu Wang; Mikolaj Kasprzak; Jeffrey Negrea; Solesne Bourguin; Jonathan H. Huggins (CoRR, 2025)

An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces. - Alexander Terenin; Jeffrey Negrea (CoRR, 2025)