Federated Unbiased Learning to Rank

no code implementations • 11 May 2021 • Chang Li, Hua Ouyang

Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions.

Federated Learning Learning-To-Rank +1

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

no code implementations • 14 Aug 2016 • Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha, Avishek Saha, Hua Ouyang, Dawei Yin, Hiroshi Mamitsuka, Cenk Sahinalp, Predrag Radivojac, Filippo Menczer, Yi Chang

However, sophisticated learning models are computationally unfeasible for data with millions of features.

Cloud Computing Dimensionality Reduction +2

Scaling Submodular Maximization via Pruned Submodularity Graphs

no code implementations • 1 Jun 2016 • Tianyi Zhou, Hua Ouyang, Yi Chang, Jeff Bilmes, Carlos Guestrin

We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization.

Video Summarization

N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

no code implementations • 10 Nov 2014 • Makoto Yamada, Avishek Saha, Hua Ouyang, Dawei Yin, Yi Chang

We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way.

Distributed Computing feature selection +1

Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions

no code implementations • NeurIPS 2009 • Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray

The long-standing problem of efficient nearest-neighbor (NN) search has ubiquitous applications ranging from astrophysics to MP3 fingerprinting to bioinformatics to movie recommendations.

Vocal Bursts Intensity Prediction