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Found 13 papers, 4 papers with code
Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task
This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as a collection of sample paths from a Bayesian optimization procedure.
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions
The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or receiving the next goal at any time instance.
SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer
We present a fully convolutional neural network architecture that is capable of estimating full probability surfaces of potential passes in soccer, derived from high-frequency spatiotemporal data.
PassNet: Learning pass probability surfaces from single-location labels. An architecture for visually-interpretable soccer analytics
We propose a fully convolutional network architecture that is able to estimate a full surface of pass probabilities from single-location labels derived from high frequency spatio-temporal data of professional soccer matches.
Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction
Numerous powerful point process models have been developed to understand temporal patterns in sequential data from fields such as health-care, electronic commerce, social networks, and natural disaster forecasting.
Learning Person Trajectory Representations for Team Activity Analysis
Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics.
Classifying X-ray Binaries: A Probabilistic Approach
We use this model to estimate the probabilities that an X-ray binary system contains a black hole, non-pulsing neutron star, or pulsing neutron star.
Fast and optimal nonparametric sequential design for astronomical observations
The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar objects.
Diversifying Sparsity Using Variational Determinantal Point Processes
We propose a novel diverse feature selection method based on determinantal point processes (DPPs).
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.
Sequential Monte Carlo Bandits
In this paper we propose a flexible and efficient framework for handling multi-armed bandits, combining sequential Monte Carlo algorithms with hierarchical Bayesian modeling techniques.
PAWL-Forced Simulated Tempering
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms.
Modeling Non-Stationary Processes Through Dimension Expansion
In this paper, we propose a novel approach to modeling nonstationary spatial fields.
Methodology Applications
