research
∙
06/21/2021
Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors
What if deep neural networks can learn from sparsity-inducing priors? Wh...
share
research
∙
01/15/2021
Predictive Optimization with Zero-Shot Domain Adaptation
Prediction in a new domain without any training sample, called zero-shot...
research
∙
06/10/2020
Regret Minimization for Causal Inference on Large Treatment Space
Predicting which action (treatment) will lead to a better outcome is a c...
research
∙
02/20/2020
Do We Need Zero Training Loss After Achieving Zero Training Error?
Overparameterized deep networks have the capacity to memorize training d...
research
∙
10/18/2019
Robust modal regression with direct log-density derivative estimation
Modal regression is aimed at estimating the global mode (i.e., global ma...
research
∙
03/13/2018
Binary Matrix Completion Using Unobserved Entries
A matrix completion problem, which aims to recover a complete matrix fro...
research
∙
10/15/2017
Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data
Capturing input-output dependency is an important task in statistical da...
research
∙
05/04/2017
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Maximizing the area under the receiver operating characteristic curve (A...
research
∙
03/10/2016
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unl...
research
∙
07/30/2009