arXiv research

Dynamic pricing model for airline ancillaries improves revenue and conversion.

Adaptive framework improves airline pricing models' performance.

DART2 enhances multiple testing by leveraging ancillary information robustly.

This study predicts flight delays for American Airlines using data mining and machine learning.

This study analyzes and predicts airline delays using machine learning models.

This paper proposes a new method to learn combinatorial patterns for airline crew pairing optimization.

Machine learning predicts flight connections for airline crew scheduling.

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

A federated learning framework improves RUL prognosis for aircraft engines without sharing data.

The paper optimizes cheap and novel flight itineraries using machine learning.

Airlines optimize fuel loading with better flight time predictions.

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

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

Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Som…

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

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

Passenger Name Records (PNRs) are at the heart of the travel industry. Created when an itinerary is booked, they contain travel and passenger information. It is usual for airlines and other actors in the industry to inter-exchange and access each other's PNR, creating the challenge of using them without infringing data…

Proposes ML methods for robust price-sensitivity estimation in dynamic pricing.

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-yea…

Enhanced kernel framework for advanced data forecasting.

Paper forecasts dynamic transportation networks using probabilistic models.

Develops a distributed least squares approximation method for regression problems.

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…

This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric. Parametric Gaussian processes, by construction, are designed to operate in "big data" regimes where one is interested in quantifying the uncertaint…

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations s…

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…

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

In this paper, we propose an active learning algorithm and models which can gradually learn individual's preference through pairwise comparisons. The active learning scheme aims at finding individual's most preferred choice with minimized number of pairwise comparisons. The pairwise comparisons are encoded into probabi…

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral dens…

We introduce a stochastic model to explain a double power-law distribution which exhibits two different Paretian behaviors in the upper and the lower tail and widely exists in social and economic systems. The model incorporates fitness consideration and noise fluctuation. We find that if the number of variables (e.g. t…

We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label…

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…

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…

A new method for real-time anomaly detection in flight data.

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

Deep kernel learning combines the non-parametric flexibility of kernel methods with the inductive biases of deep learning architectures. We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task lea…

Proposes a compensation mechanism for improving individual forecast confidence.

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…

We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition fo…

Auptimizer simplifies hyperparameter tuning for machine learning models.

Flow preserves curvature sharpness on weighted graphs.

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

A distributed bootstrap method for high-dimensional data reduces communication rounds efficiently.

This paper quantifies how well random neural networks can approximate continuous functions.

A novel hash learning approach using codewords in Hamming space.

We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. The method supports multiple outputs and multiple latent functions and does not require detailed knowledge of the conditional likelihood, only needing its evaluation as a black-box function. Usi…

HL algorithms improve resource allocation in cloud environments.

Quantum-assisted Gaussian process speeds up data regression.