2015/06/22 by Akshay Balsubramani, Balsubramani, Akshay
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1506.06573
openalex publication_date 2015/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We give tight concentration bounds for mixtures of martingales that are simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense; and (b) all finite times. These bounds are proved in terms of the martingale variance, extending classical Bernstein inequalities, and sharpening and simplifying prior work.