Africa
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Switching Autoregressive Low-rank Tensor Models
An important problem in time-series analysis is modeling systems with time-varying dynamics.
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning Wei T ang
In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels.
New Complexity-Theoretic Frontiers of Tractability for Neural Network Training
A neural network (cf. Figure 1) can be thought of as a directed acyclic network consisting of
