259 citations · 463 across the 39 of their papers we have counts for
12 papers · 1 filter
Hierarchically Coherent Multivariate Mixture Networks
Kin G. Olivares, David Luo, Cristian Challu +3
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic…
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Kin G. Olivares, Azul Garza, David Luo +5
Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings. It is often…
Double Adaptive Stochastic Gradient Optimization
Kin Gutierrez, Jin Li, Cristian Challu +1
Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of a…
On the Interaction Effects Between Prediction and Clustering
Matt Barnes, Artur Dubrawski
Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects betw…
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh +1
In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain a…
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
Eric Lei, Kyle Miller, Michael R. Pinsky +1
Multi-view data are increasingly prevalent in practice. It is often relevant to analyze the relationships between pairs of views by multi-view component analysis techniques such as…
Noise-Tolerant Interactive Learning from Pairwise Comparisons
Yichong Xu, Hongyang Zhang, Aarti Singh +2
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given…
Scaling Active Search using Linear Similarity Functions
Sibi Venkatesan, James K. Miller, Jeff Schneider +1
Active Search has become an increasingly useful tool in information retrieval problems where the goal is to discover as many target elements as possible using only limited label qu…
Clustering on the Edge: Learning Structure in Graphs
Matt Barnes, Artur Dubrawski
With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples. We show how learning cluster structure using edge…
Canonical Autocorrelation Analysis
Maria De-Arteaga, Artur Dubrawski, Peter Huggins
We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables. Unlike CCA, w…
Lass-0: sparse non-convex regression by local search
William Herlands, Maria De-Arteaga, Daniel Neill +1
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step. Our algorithm, the Lass-0 ("Lass-z…
Performance Bounds for Pairwise Entity Resolution
Matt Barnes, Kyle Miller, Artur Dubrawski
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labe…