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20062026
most citedEstimating divergence functionals and the likelihood ratio by convex risk minimization

665 citations · 942 across the 38 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.DB2019

Learning Models over Relational Data: A Brief Tutorial

Maximilian Schleich, Dan Olteanu, Mahmoud Abo-Khamis +2

This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in da…

cs.LG2019★ 5 cited

Rk-means: Fast Clustering for Relational Data

Ryan Curtin, Ben Moseley, Hung Q. Ngo +3

Conventional machine learning algorithms cannot be applied until a data matrix is available to process. When the data matrix needs to be obtained from a relational database via a f…

stat.ML2019★ 1 cited

On Efficient Multilevel Clustering via Wasserstein Distances

Viet Huynh, Nhat Ho, Nhan Dam +4

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a pote…

cs.DB2019

A Layered Aggregate Engine for Analytics Workloads

Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis +2

This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database.…

stat.ML2019★ 1 cited

Dirichlet Simplex Nest and Geometric Inference

Mikhail Yurochkin, Aritra Guha, Yuekai Sun +1

We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting f…

math.ST2019★ 1 cited

On posterior contraction of parameters and interpretability in Bayesian mixture modeling

Aritra Guha, Nhat Ho, XuanLong Nguyen

We study posterior contraction behaviors for parameters of interest in the context of Bayesian mixture modeling, where the number of mixing components is unknown while the model it…