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- University of OxfordGB179 papers
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11 papers · 1 filter
Batched Thompson Sampling for Multi-Armed Bandits
Nikolai Karpov, Qin Zhang
We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small…
Escaping Saddle Points with Compressed SGD
Dmitrii Avdiukhin, Grigory Yaroslavtsev
Stochastic gradient descent (SGD) is a prevalent optimization technique for large-scale distributed machine learning. While SGD computation can be efficiently divided between multi…
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Tingyi Wanyan, Jing Zhang, Ying Ding +3
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, o…
ThetA -- fast and robust clustering via a distance parameter
Eleftherios Garyfallidis, Shreyas Fadnavis, Jong Sung Park +4
Clustering is a fundamental problem in machine learning where distance-based approaches have dominated the field for many decades. This set of problems is often tackled by partitio…
Efficient Competitive Self-Play Policy Optimization
Yuanyi Zhong, Yuan Zhou, Jian Peng
Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterati…
Accelerated solving of coupled, non-linear ODEs through LSTM-AI
Camila Faccini de Lima, Juliano Ferrari Gianlupi, John Metzcar +1
The present project aims to use machine learning, specifically neural networks (NN), to learn the trajectories of a set of coupled ordinary differential equations (ODEs) and decrea…