no code implementations • 30 Sep 2022 • Konstantina Christakopoulou, Can Xu, Sai Zhang, Sriraj Badam, Trevor Potter, Daniel Li, Hao Wan, Xinyang Yi, Ya Le, Chris Berg, Eric Bencomo Dixon, Ed H. Chi, Minmin Chen
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?
no code implementations • 2 Apr 2022 • Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
Users who come to recommendation platforms are heterogeneous in activity levels.
no code implementations • 26 Jan 2022 • Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
no code implementations • 6 May 2021 • Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen
We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.
no code implementations • 9 Oct 2015 • Eric Bax, Ya Le
WAG withholds a validation set, trains a holdout classifier on the remaining data, uses the validation data to validate that classifier, then adds the rate of disagreement between the holdout classifier and one trained using all in-sample data, which is an upper bound on the difference in error rates.
no code implementations • 31 Oct 2014 • Ya Le, Eric Bax, Nicola Barbieri, David Garcia Soriano, Jitesh Mehta, James Li
We introduce a technique to compute probably approximately correct (PAC) bounds on precision and recall for matching algorithms.
no code implementations • 17 Jul 2014 • Ya Le, Trevor Hastie
We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models.