Information-theoretic generalization bounds for SGLD via data-dependent estimates J Negrea, M Haghifam, GK Dziugaite, A Khisti, DM Roy Advances in Neural Information Processing Systems 32, 2019 | 141 | 2019 |
Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms M Haghifam, J Negrea, A Khisti, DM Roy, GK Dziugaite Advances in Neural Information Processing Systems 33, 2020 | 105 | 2020 |
In defense of uniform convergence: Generalization via derandomization with an application to interpolating predictors J Negrea, GK Dziugaite, D Roy Proceedings of the 37th International Conference on Machine Learning, 2020 | 57 | 2020 |
Concept Algebra for (Score-Based) Text-Controlled Generative Models Z Wang, L Gui, J Negrea, V Veitch Advances in Neural Information Processing Systems 36, 2024 | 24* | 2024 |
Approximations of Geometrically Ergodic Reversible Markov Chains J Negrea, JS Rosenthal Advances in Applied Probability 53 (4), 2021 | 19* | 2021 |
Relaxing the iid assumption: Adaptively minimax optimal regret via root-entropic regularization B Bilodeau, J Negrea, DM Roy The Annals of Statistics 51 (4), 1850-1876, 2023 | 10 | 2023 |
Minimax optimal quantile and semi-adversarial regret via root-logarithmic regularizers J Negrea, B Bilodeau, N Campolongo, F Orabona, D Roy Advances in Neural Information Processing Systems 34, 26237-26249, 2021 | 5 | 2021 |
Optimal Scaling and Shaping of Random Walk Metropolis via Diffusion Limits of Block-IID Targets J Negrea arXiv preprint arXiv:1902.06603, 2019 | 2 | 2019 |
STATISTICAL INFERENCE WITH STOCHASTIC GRADIENT ALGORITHMS J Negrea, J Yang, H Feng, DM Roy, JH Huggins | 2* | |
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics J Negrea, J Yang, H Feng, DM Roy, JH Huggins arXiv preprint arXiv:2207.12395, 2022 | | 2022 |
Approximations and Scaling Limits of Markov Chains with Applications to MCMC and Approximate Inference J Negrea | | 2022 |