9 accepted papers
As the availability of different data sources increases, transfer learning has become popular for improving estimation efficiency by incorporating those sources. In this paper, we propose a functional balancing transfer learning algorithm for observational studies integrating external summary inform…
Recently, textual information has been proven to positively affect recommendation systems. However, most of the existing methods only focus on representation learning of textual information in ratings, while potential selection bias induced by the textual information is ignored. In this work, we pro…
The cost of ranking becomes significant in the new stage of deep learning. We propose ST$_k$, a fully differentiable module with a single trainable parameter, designed to solve the Top-k problem without requiring additional time or GPU memory. Due to its fully differentiable nature, ST$_k$ can be em…
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. More- over, transduction with matrix completion is a useful method in multi-label learning. In this paper, we propose a transductive matrix completion algorithm that incorporate…
Tensor completion aims at filling the missing or unobserved en-tries based on partially observed tensors. However, utilization of the observed tensors often raises serious privacy concerns in many practical scenarios. To address this issue, we propose a solid and unified framework that contains seve…
Communication cost and security issues are both important in large-scale distributed machine learning. In this paper, we investigate a multiclass sparse classification problem under two distributed systems. We propose two distributed multiclass sparse discriminant analysis algorithms based on mean-a…
Compressed sensing (CS) aims to recover a high-dimensional signal with structural priors from its low-dimensional linear measurements. Inspired by the huge success of deep neural networks in modeling the priors of natural signals, generative neural networks have been recently used to replace the han…
In this paper, we propose a novel method for matrix completion under general non-uniform missing structures. By controlling an upper bound of a novel balancing error, we construct weights that can actively adjust for the non-uniformity in the empirical risk without explicitly modeling the observatio…
In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix. Despite several appealing properties of median, the non-smooth absolute deviation loss leads to computational challenge for large-scale data sets which are increasingly common amon…