2 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
On Negative Sampling for Audio-Visual Contrastive Learning from Movies
Mahdi M. Kalayeh, Shervin Ardeshir, Lingyi Liu +2
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal a plethora of information about what happens in a scene, make the audio-visual space an in…
Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows
Mahdi M. Kalayeh, Nagendra Kamath, Lingyi Liu +1
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal so much about what happens in the scene, make the audio-visual space a perfectly intuitive…
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…
Learning Representations of Hierarchical Slates in Collaborative Filtering
Ehtsham Elahi, Ashok Chandrashekar
We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchic…