6 citations · 28 across the 20 of their papers we have counts for
6 papers · 1 filter
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Stephanie Schoch, Haifeng Xu, Yangfeng Ji
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label det…
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li +1
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advert…
Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov +2
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from…
Least Square Calibration for Peer Review
Sijun Tan, Jibang Wu, Xiaohui Bei +1
Peer review systems such as conference paper review often suffer from the issue of miscalibration. Previous works on peer review calibration usually only use the ordinal informatio…
Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov +2
We propose a new problem setting to study the sequential interactions between a recommender system and a user. Instead of assuming the user is omniscient, static, and explicit, as…
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang
We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an adversary aims to control the behaviour of the bandit algorithm. Perhaps surprisingly, we…