arXiv research

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.

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.

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…

New method optimizes costly functions with unknown costs and budget constraints.

The study extends SPT to account for real-world transaction costs, improving portfolio performance.

Deep neural network predicts health costs better than traditional models.

In this short note, we consider mean-variance optimized portfolios with transaction costs. We show that introducing quadratic transaction costs makes the optimization problem more difficult than using linear transaction costs. The reason lies in the specification of the budget constraint, which is no longer linear. We …

CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.

Investigates the cost-effectiveness of security features in smart card chips.

SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.

Improved option pricing model with transaction costs.

Cost overruns in transport infrastructure projects know no geographical limits, overruns are a global phenomenon. Nevertheless, the size of cost overruns varies with location. In the Netherlands, cost overruns appear to be smaller compared to the rest of the world. This paper tests whether Dutch projects perform signif…