10 citations · 19 across the 8 of their papers we have counts for
4 papers · 1 filter
On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks
Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar
We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sp…
Selectively Contextual Bandits
Claudia Roberts, Maria Dimakopoulou, Qifeng Qiao +2
Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects…
Control Variates for Slate Off-Policy Evaluation
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil +1
We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems a…
Off-Policy Evaluation of Slate Policies under Bayes Risk
Nikos Vlassis, Fernando Amat Gil, Ashok Chandrashekar
We study the problem of off-policy evaluation for slate bandits, for the typical case in which the logging policy factorizes over the slots of the slate. We slightly depart from th…