16 citations · 59 across the 10 of their papers we have counts for
10 papers
MABSplit: Faster Forest Training Using Multi-Armed Bandits
Mo Tiwari, Ryan Kang, Je-Yong Lee +4
Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the…
Faster Maximum Inner Product Search in High Dimensions
Mo Tiwari, Ryan Kang, Je-Yong Lee +5
Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems. Given a query vector and atom vectors in -dimension…
MLDemon: Deployment Monitoring for Machine Learning Systems
Antonio Ginart, Martin Zhang, James Zou
Post-deployment monitoring of ML systems is critical for ensuring reliability, especially as new user inputs can differ from the training distribution. Here we propose a novel appr…
BanditPAM: Almost Linear Time -Medoids Clustering via Multi-Armed Bandits
Mo Tiwari, Martin Jinye Zhang, James Mayclin +3
Clustering is a ubiquitous task in data science. Compared to the commonly used -means clustering, -medoids clustering requires the cluster centers to be actual data points an…
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin J. Zhang, James Zou, David Tse
Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fai…
Minimax Optimality of Sign Test for Paired Heterogeneous Data
Martin J. Zhang, Meisam Razaviyayn, David Tse
Comparing two groups under different conditions is ubiquitous in the biomedical sciences. In many cases, samples from the two groups can be naturally paired; for example a pair of…
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Fei Xia, Martin J. Zhang, James Zou +1
As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We…
Medoids in almost linear time via multi-armed bandits
Vivek Bagaria, Govinda M. Kamath, Vasilis Ntranos +2
Computing the medoid of a large number of points in high-dimensional space is an increasingly common operation in many data science problems. We present an algorithm Med-dit which…
Contrastive Principal Component Analysis
Abubakar Abid, Martin J. Zhang, Vivek K. Bagaria +1
We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in…
Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection
Jinye Zhang, Zhijian Ou
Existing MAP inference algorithms for determinantal point processes (DPPs) need to calculate determinants or conduct eigenvalue decomposition generally at the scale of the full ker…