arXiv research

Proposes a new method to find overconfident predictions in classifiers.

The paper addresses fairness in facility location by considering local population densities.

Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.

MPNN improves on UniFL approximation with provable guarantees.

New tools analyze data selection methods, reducing information losses.

Develops a Bayesian model to predict business revenue and demand.

Apricot selects subsets from large data sets efficiently using submodular optimization.

A framework for real-time gesture recognition in tomato grafting.

This paper develops a framework for predicting patient workload across multiple VA facilities.

A new house price prediction model using location data and multi-task learning.

Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree…

The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of this problem is that submodular optimization algorithms require the calculation of pairwise benefits for all items in the dataset. This is i…

Deployable probabilistic programming for Go and other languages.

Closed-form optimal portfolios for exponential utility in small/large markets.

Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.

Study examines how industrial emissions evolve over time in response to various factors.

Deep learning optimizes gas storage operations.

Graph neural networks improve residential location choice predictions.

SimTensor is a multi-platform, open-source software for generating artificial tensor data (either with CP/PARAFAC or Tucker structure) for reproducible research on tensor factorization algorithms. SimTensor is a stand-alone application based on MATALB. It provides a wide range of facilities for generating tensor data w…

Paper develops a gradient-like proposal for discrete distributions without requiring natural differentiability.

Flusion combines multiple data sources to improve flu forecasts.

Based on the concept of self-decomposable random variables we discuss the application of a model for a pair of dependent Poisson processes to energy facilities. Due to the resulting structure of the jump events we can see the self-decomposability as a form of cointegration among jumps. In the context of energy faciliti…

A machine learning approach optimizes data obfuscation for privacy.

Integrates learning and optimization on graphs, improving prediction accuracy.

Trading floors need to be twice as deep as electronic markets to compete.

A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…

Differentially private log-location-scale regression models improve privacy in statistical analysis.

Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.

Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.

Unified approach tackles logical constraints in mixed-integer optimization.

Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of high costs involved in human labeling efforts. Training data subset selection and active learning techniques have been proposed as possible s…

CMDRNN predicts user location using WiFi fingerprints with deep learning.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of…

Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.

We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…

Kriformer uses graph transformers to estimate data in sparse sensor areas.

The paper optimizes daily storage trading of electricity using dynamic spread densities.

New model for multivariate discrete event data with flexible interactions.

The paper introduces submodular information measures for machine learning applications.

Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the latter suffers from intractable computation and can lead to overfitting. Taking the i…

GeoShapley uses game theory to measure spatial effects in ML models.

New method for discrete-time survival analysis with competing risks.

DeepPlace learns to place applications in clusters using RL.

Deep learning predicts user identity, activity, and location from Wi-Fi signals.

This paper tackles fair same-day delivery service by optimizing regional service rates.

Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks a theore…

We build a model using Gaussian processes to infer a spatio-temporal vector field from observed agent trajectories. Significant landmarks or influence points in agent surroundings are jointly derived through vector calculus operations that indicate presence of sources and sinks. We evaluate these influence points by us…

A new algorithm finds optimal centers for sets in metric spaces.