research
∙
07/14/2023
A simple deterministic near-linear time approximation scheme for transshipment with arbitrary positive edge costs
We describe a simple deterministic near-linear time approximation scheme...
share
research
∙
11/07/2022
A deterministic near-linear time approximation scheme for geometric transportation
Given a set of points P = (P^+ ⊔ P^-) ⊂ℝ^d for some constant d and a sup...
research
∙
08/06/2020
Learning Insulin-Glucose Dynamics in the Wild
We develop a new model of insulin-glucose dynamics for forecasting blood...
research
∙
03/27/2020
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
One of the challenges in machine learning research is to ensure that pre...
research
∙
02/17/2018
Interpretable VAEs for nonlinear group factor analysis
Deep generative models have recently yielded encouraging results in prod...
research
∙
02/16/2018
Neural Granger Causality for Nonlinear Time Series
While most classical approaches to Granger causality detection assume li...
research
∙
10/23/2017
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Providing long-range forecasts is a fundamental challenge in time series...
research
∙
11/04/2014
Expectation-Maximization for Learning Determinantal Point Processes
A determinantal point process (DPP) is a probabilistic model of set dive...
research
∙
01/11/2011
