arXiv research

Optimizes black-box functions with varying costs across multiple sources.

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian proc…

We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the referenc…

Estimates spatio-temporal Hawkes processes using tensor recovery.

The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a simple kernel such as the Gaussian, however, limits its practical applicability. We provide a scheme for learning a kernel parameterized by …

Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn…

Probabilistic Bisection Algorithm performs root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistical distribution of the oracle is unknown and location-dependent, so that model inference and Bayesian knowledge updating must be performed s…

We consider numerical schemes for root finding of noisy responses through generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical context where the sampling distribution is unknown and location-dependent. As in standard PBA, we rely on a knowledge state for the approximate posterior of the root l…

Deep vanilla transformers trained without shortcuts achieve similar performance to standard models.

Cost-aware BO minimizes function evaluations with varying costs.

Paper optimizes broker performance by estimating execution costs.

Paper proposes new costs for learning multiple centers in MDNs.

Using a methodology similar to that used the in the worldwide research, the cost performance of Dutch large-scale transport infrastructure projects is determined. In the Netherlands, cost overruns are as common as cost underruns but because cost overruns are larger than cost underruns projects on average have a cost ov…

Proposes resilience metrics for large blackout costs with logarithmic resilience.

Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…

Optimal benchmark design varies based on costs in financial manipulation.

The paper analyzes the benefit-cost ratio for feature selection in machine learning.

Study calculates liquidity costs for delta hedging of European options.

New model considers varying costs in learning, outperforming existing methods.

Transaction costs appear in financial markets in more than one form. There are several results in the literature on small proportional transaction cost and not that many on fixed transaction cost. In the present work, we heuristically study the effect of both types of transaction cost by focusing on a portfolio optimiz…

New algorithm reduces misclassification costs in neural networks.

The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …

This paper proposes CSADA to make DNNs cost-sensitive.

The paper improves competitive and dynamic regret bounds for smoothed online learning.

This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.

A new cost-frugal HPO method controls training cost during optimization.

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified {\it average} feature acquisition b…

Investigates how trading boundaries change with transaction costs in portfolio selection.

New RL algorithms reduce costs for single-agent and federated learning.

New method optimizes costly evaluations in Bayesian optimization.

Model shows how price impact and transaction costs affect trading behavior and profits.

New method reduces total cost constraints in CBwK to sqrt(T) with fairness application.

Study how transaction costs impact stock returns and holdings in equilibrium.

The multimodal web elements such as text and images are associated with inherent memory costs to store and transfer over the Internet. With the limited network connectivity in developing countries, webpage rendering gets delayed in the presence of high-memory demanding elements such as images (relative to text). To ove…

Estimates optimal transport maps with known cost functions.

Proposes an angle-based framework for multicategory cost-sensitive classification.

The paper optimizes portfolios with transaction costs in a large asset universe.

We study the high-frequency limits of strategies and costs in a Nash equilibrium for two agents that are competing to minimize liquidation costs in a discrete-time market impact model with exponentially decaying price impact and quadratic transaction costs of size $θ\ge0$. We show that, for $θ=0$, equilibrium strategie…

Study optimal investment and consumption strategies with various transaction costs.

Cost-benefit analysis often assumes accurate estimates, but this study finds significant inaccuracies.

Introduces a Cost function to measure Legendrian knot obstructions.

This work improves cost-aware Bayesian optimization by introducing Pareto-efficient acquisition functions.

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitat…

Paper examines costs of using wrong price impact models in trading.

We consider a Nash equilibrium between two high-frequency traders in a simple market impact model with transient price impact and additional quadratic transaction costs. Extending a result by Schöneborn (2008), we prove existence and uniqueness of the Nash equilibrium and show that for small transaction costs the high-…

Low-cost sensors improve air quality prediction accuracy significantly.

UCRL-CMDP algorithm optimizes RL with constraints on average costs.

This paper examines three independent explanatory variables and their relation with cost overrun in order to decide whether this is different for Dutch infrastructure projects compared to worldwide findings. The three independent variables are project type (road, rail, and fixed link projects), project size (measured i…