15 citations · 26 across the 9 of their papers we have counts for
8 papers · 1 filter
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable
Cambridge Yang, Michael Littman, Michael Carbin
In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal p…
Reinforcement Causal Structure Learning on Order Graph
Dezhi Yang, Guoxian Yu, Jun Wang +2
Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed da…
MetaMIML: Meta Multi-Instance Multi-Label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +3
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances. Curren…
Neighbor Embedding Variational Autoencoder
Renfei Tu, Yang Liu, Yongzeng Xue +2
Being one of the most popular generative framework, variational autoencoders(VAE) are known to suffer from a phenomenon termed posterior collapse, i.e. the latent variational distr…
Partial Multi-label Learning with Label and Feature Collaboration
Tingting Yu, Guoxian Yu, Jun Wang +1
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML p…
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Yuying Xing, Guoxian Yu, Carlotta Domeniconi +3
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple…
Ranking-based Deep Cross-modal Hashing
Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi +3
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are…
Multiple Independent Subspace Clusterings
Xing Wang, Jun Wang, Carlotta Domeniconi +3
Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the dis…