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Found 12 papers, 2 papers with code
Manifold learning: what, how, and why
Manifold learning (ML), known also as non-linear dimension reduction, is a set of methods to find the low dimensional structure of data.
Asset Bundling for Wind Power Forecasting
This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks.
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
The paper also proposes an optimization proxy to address the computational challenges of the optimization models.
Transfer Learning for Causal Effect Estimation
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data.
Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area
This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown.
The Parametric Stability of Well-separated Spherical Gaussian Mixtures
We quantify the parameter stability of a spherical Gaussian Mixture Model (sGMM) under small perturbations in distribution space.
Dictionary-based Manifold Learning
We propose a paradigm for interpretable Manifold Learning for scientific data analysis, whereby we parametrize a manifold with $d$ smooth functions from a scientist-provided dictionary of meaningful, domain-related functions.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Distribution free optimality intervals for clustering
We demonstrate the practical relevance of this method by obtaining guarantees for the K-means and the Normalized Cut clustering criteria on realistic data sets.
Public Transit for Special Events: Ridership Prediction and Train Optimization
This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events.
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.
Manifold Coordinates with Physical Meaning
Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space.
