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10 papers · 2 filters
Explainability of Traditional and Deep Learning Models on Longitudinal Healthcare Records
Lin Lee Cheong, Tesfagabir Meharizghi, Wynona Black +2
Recent advances in deep learning have led to interest in training deep learning models on longitudinal healthcare records to predict a range of medical events, with models demonstr…
Fair Effect Attribution in Parallel Online Experiments
Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto +3
A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous exp…
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
Yaoyao Ding, Cody Hao Yu, Bojian Zheng +3
As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide effic…
Condensing Graphs via One-Step Gradient Matching
Wei Jin, Xianfeng Tang, Haoming Jiang +4
As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning mode…
Pessimistic Off-Policy Optimization for Learning to Rank
Matej Cief, Branislav Kveton, Michal Kompan
Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging du…
Mixed-Effect Thompson Sampling
Imad Aouali, Branislav Kveton, Sumeet Katariya
A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In th…