arXiv research

DART2 enhances multiple testing by leveraging ancillary information robustly.

Dataset for rainfall modeling in central Europe from 1981-2011.

This paper improves bidding price prediction for ancillary services markets, boosting revenues.

Simpler model outperforms state-of-the-art for disaggregating census data.

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI te…

Proposes a new GAN architecture for generating data conditioned on partial information.

Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.

We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference. Each pixel intensity is treated as a Poisson random variable, with a rate parameter dependent on latent properties of stars and galaxies. Key latent properties are themselves random, with scientif…

We consider a natural model of random knotting- choose a knot diagram at random from the finite set of diagrams with n crossings. We tabulate diagrams with 10 and fewer crossings and classify the diagrams by knot type, allowing us to compute exact probabilities for knots in this model. As expected, most diagrams with 1…

Suppose $C$ is a compact, $n$-edged two-cell of the centered dual decomposition of a locally finite set in the hyperbolic plane, a coarsening of the Delaunay tessellation which was introduced in the author's prior work. We describe an effectively computable lower bound on the area of $C$, given an $n$-tuple of positive…

In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks. First, the codewords are utilized in the Hamming space as ancillary techniques to accomplish its hash learning task. These codewords, which are inferred from the data, attempt …

This paper strengthens the central limit theorem for order statistics using relative entropy.

Quantum-assisted Gaussian process speeds up data regression.

Paper uses tensor completion to estimate HVAC fan power baselines.

Multiple machine learning and prediction models are often used for the same prediction or recommendation task. In our recent work, where we develop and deploy airline ancillary pricing models in an online setting, we found that among multiple pricing models developed, no one model clearly dominates other models for all…

Auptimizer simplifies hyperparameter tuning for machine learning models.

This paper improves hierarchical community detection efficiency using local structural properties.

Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction. More recently, the framework of copula modeling has gained popularity due to its modular parametrization of joint distributions. Among o…

The general theory of boundary value problems for linear elliptic wedge operators (on smooth manifolds with boundary) leads naturally, even in the scalar case, to the need to consider vector bundles over the boundary together with general smooth fiberwise multiplicative group actions. These actions, essentially trivial…

Rigidity theorem for critical points of Allen-Cahn equation on S³.

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the po…

TeLeS improves ASR confidence estimation by considering temporal alignment and lexical errors.

This paper investigates the approximation power of three types of random neural networks: (a) infinite width networks, with weights following an arbitrary distribution; (b) finite width networks obtained by subsampling the preceding infinite width networks; (c) finite width networks obtained by starting with standard G…

Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with onl…

Bayesian transfer learning improves predictive performance with limited source data.

Flow preserves curvature sharpness on weighted graphs.

Methods for prediction and tolerance intervals in non-normal models.

Paper proposes a new KPI for early fault detection in hydropower plants.

The problem of finding a reduced dimensionality representation of categorical variables while preserving their most relevant characteristics is fundamental for the analysis of complex data. Specifically, given a co-occurrence matrix of two variables, one often seeks a compact representation of one variable which preser…

An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…

Study uses SAR data to estimate forest vegetation indices, improving monitoring of temperate forests.

This paper operationalizes the Exponential Mechanism using Normalizing Flows for private optimization.

HAMN combines CF models to improve drug repositioning.

Quantum algorithm solves financial option pricing using Hamiltonian simulation.

Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.

New method uses rank-conditioned Horvitz-Thompson estimation for unbiased sample reuse in Plackett-Luce best-of-K objective.

Data preprocessing improves data quality for robust data mining.

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…

A new method for handling imbalanced big data using ensembles and smart data.

Prevents sensitive data generation in diffusion models using labeled and unlabeled data.

Study reveals Data Shapley's inconsistent performance in data selection tasks.

PRRO generates synthetic tabular data that improves SL performance and class distribution.

Defines data science as a natural ecosystem with challenges and missions.

Synthetic data enhances analytics but requires careful volume management.

This paper introduces C-DSL to improve data mining outcomes by considering context.

Proposes using probabilistic models for privacy-preserving synthetic data.

New test ensures quality of shared data in machine learning.

Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.