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Multiway clustering via tensor block models
Neural Information Processing Systems http://nips.cc/
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data.
FederatedTransformer: Multi-PartyVertical FederatedLearningonPracticalFuzzilyLinkedData
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data.
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization
Neural Information Processing Systems http://nips.cc/
Regret Bounds for Learning State Representations in Reinforcement Learning
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/
Quadrature-based features for kernel approximation
Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
Neural Information Processing Systems http://nips.cc/
