5 papers
Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
Shuai Liu, Alireza Bakhtiari, Alex Ayoub +2
We study stochastic logistic bandits with -dimensional action features under the simple-regret objective, where a learner uses rounds of exploration to output a single final…
Eluder dimension: localise it!
Alireza Bakhtiari, Alex Ayoub, Samuel Robertson +2
We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot lead to first-order regret boun…
Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang +2
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic…
Bernstein-type dimension-free concentration for self-normalised martingales
Arya Akhavan, Amitis Shidani, Alex Ayoub +1
We introduce a dimension-free Bernstein-type tail inequality for self-normalised martingales, where the normalisation uses the predictable quadratic variation and the radius depend…
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
Shuai Liu, Alex Ayoub, Flore Sentenac +2
We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential fami…