A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study analyzes and predicts airline delays using machine learning models.
In the present scenario of domestic flights in USA, there have been numerous instances of flight delays and cancellations. In the United States, the American Airlines, Inc. have been one of the most entrusted and the world's largest airline in terms of number of destinations served. But when it comes to domestic flight…
Airlines optimize fuel loading with better flight time predictions.
This paper proposes a new method to learn combinatorial patterns for airline crew pairing optimization.
Machine learning predicts flight connections for airline crew scheduling.
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.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
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…
We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departu…
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…
Unified study of stateful replay for streaming learning, reducing forgetting by 2-3x.
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…
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…
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.
Develops a stochastic approach to financial market delays.
Paper tackles action delays in reinforcement learning, proposing a delay-aware framework.
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…
New algorithm tackles delayed feedback in Lipschitz bandits with sublinear regret.
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
Banker-OMD improves online learning with delayed feedback.
New algorithm handles delayed feedback robustly, reducing regret without knowing delay bounds.
Enhanced kernel framework for advanced data forecasting.
Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art …
Paper tackles delays in multi-agent reinforcement learning, improving performance.
Study on synchronization in financial markets with time delays.
New algorithm tackles stochastic bandits with varying arm-dependent delays.
BayTiDe discovers time-delayed differential equations from noisy data.
TSMB handles time delays in multivariate time series data.
In this work, we develop a distributed least squares approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox's model) on a distributed system. By approximating the local objective function using a local quadratic form, we are able to…
We investigate multiarmed bandits with delayed feedback, where the delays need neither be identical nor bounded. We first prove that "delayed" Exp3 achieves the regret bound conjectured by Cesa-Bianchi et al. [2019] in the case of variable, but bounded delays. Here, is the number of actio…
New algorithm reduces regret in delayed feedback generalised linear bandits.
Adapts Exp3 to adversarial bandits with delays and data.
Capacity-Constrained Online Convex Optimization with Delayed Feedback
New algorithm tackles non-stationary delayed feedback in recommender systems.
PCTS optimizes noisy, delayed, multi-fidelity feedbacks in black-box optimization.
Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time de…
We study a variant of the stochastic -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…
Study online learning with delays and capacity constraints, achieving optimal regret bounds.
Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems. In this paper we provide a systematic study of the topic, and analyze the effect of delay on the regret of online learning algorithms. Somewhat surprisingly, it t…
Federated learning technique improves convergence speed with communication delays.
Study a dual risk model with innovation delays, focusing on ruin probability and time.
Study on unimodality of plucking polynomial with delay function.
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements are stacked RNNs, which increases the capacity for learning non-linear functions, and bidirectional processing, which exploits acausal informa…
New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.
We provide tight finite-time convergence bounds for gradient descent and stochastic gradient descent on quadratic functions, when the gradients are delayed and reflect iterates from rounds ago. First, we show that without stochastic noise, delays strongly affect the attainable optimization error: In fact, the error…
We propose a model to study the effects of delayed information on option pricing. We first talk about the absence of arbitrage in our model, and then discuss super replication with delayed information in a binomial model, notably, we present a closed form formula for the price of convex contingent claims. Also, we addr…