16 citations · 51 across the 7 of their papers we have counts for
7 papers
Federated Unbiased Learning to Rank
Chang Li, Hua Ouyang
Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions. In this framework, ULTR algorithms have to rely on a large am…
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
Scaling Submodular Maximization via Pruned Submodularity Graphs
Tianyi Zhou, Hua Ouyang, Yi Chang +2
We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization. The pruning is applied via a "submodularity graph" o…
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Makoto Yamada, Avishek Saha, Hua Ouyang +2
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of t…
Stochastic ADMM for Nonsmooth Optimization
Hua Ouyang, Niao He, Alexander Gray
We present a stochastic setting for optimization problems with nonsmooth convex separable objective functions over linear equality constraints. To solve such problems, we propose a…
Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure
Hua Ouyang, Alexander Gray
In this work we consider the stochastic minimization of nonsmooth convex loss functions, a central problem in machine learning. We propose a novel algorithm called Accelerated Nons…