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Found 11 papers, 3 papers with code
A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems
We provide a theoretical characterization and an associated data-driven method to evaluate (a) the presence of any form of racial bias, and (b) if so, the primary source of such a bias in terms of race and criminality.
Hidden Markov Models with Momentum
In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models.
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions.
Adaptive Synthetic Characters for Military Training
Observation-based behavior model adaptation that leverages machine learning and the experience of synthetic entities in combination with appropriate prior knowledge can address the issues in the existing computational behavior models to create a better training experience in military training simulations.
An Empirical Analysis of Privacy in the Lightning Network
Payment channel networks, and the Lightning Network in particular, seem to offer a solution to the lack of scalability and privacy offered by Bitcoin and other blockchain-based cryptocurrencies.
Cryptography and Security
Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
Smart contracts are applications that execute on blockchains.
Cryptography and Security
Sprites and State Channels: Payment Networks that Go Faster than Lightning
Our construction is also modular, making use of a generic abstraction from folklore, called the "state channel," which we are the first to formalize.
Cryptography and Security
A Gaussian Process Model of Quasar Spectral Energy Distributions
We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e. g., stars, galaxies, and quasars) at extremely different spectral resolutions.
Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars
This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational-wave transients.
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA.
