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Found 15 papers, 0 papers with code
Generative Bayesian Computation for Maximum Expected Utility
Generative methods assume only the ability to simulate from the model and parameters and as such are likelihood-free.
Deep Learning: A Tutorial
Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data.
The Value of Chess Squares
We propose a neural network-based approach to calculate the value of a chess square-piece combination.
Feature Selection for Personalized Policy Analysis
In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characteristics.
Quantum Bayesian Computation
Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computation.
Bayesian Calibration for Activity Based Models
We consider the problem of calibration and uncertainty analysis for activity-based transportation simulators.
Deep Generative Models for Vehicle Speed Trajectories
Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars.
Merging Two Cultures: Deep and Statistical Learning
We show that prediction, interpolation and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model.
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
Bayesian Regularization: From Tikhonov to Horseshoe
Bayesian regularization is a central tool in modern-day statistical and machine learning methods.
Methodology
Deep Learning: Computational Aspects
In this article we review computational aspects of Deep Learning (DL).
Deep Learning for Energy Markets
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids.
Deep Reinforcement Learning for Dynamic Urban Transportation Problems
We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation.
Clusters of Driving Behavior from Observational Smartphone Data
We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs.
Deep Learning: A Bayesian Perspective
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction.
