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.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
New method reduces total cost constraints in CBwK to sqrt(T) with fairness application.
Study shows maker-taker fees improve market efficiency but increase costs.
A novel algorithm reduces communication costs in federated best arm identification.
In some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, in order to classify whether a patient has diabetes or not, measuring glucose tolerance can be expensive. Assuming that the cost of each covariate, and the cost of misclassification can be specified by…
Based on a study of the coupling by reflection of diffusion processes, a new monotonicity in time of a time-dependent transportation cost between heat distribution is shown under Bakry-Emery's curvature-dimension condition on a Riemannian manifold. The cost function comes from the total variation between heat distribut…
In this paper, we study a risk process modeled by a Brownian motion with drift (the diffusion approximation model). The insurance entity can purchase reinsurance to lower its risk and receive cash injections at discrete times to avoid ruin. Proportional reinsurance and excess-of-loss reinsurance are considered. The obj…
Improves classifier evaluation by aligning with Total Classification Cost.
Optimizing rewards under budget constraints with correlated costs and rewards.
Buying or selling assets leads to transaction costs for the investor. On one hand, it is well know to all market practionaires that the transaction costs are positive on average and present therefore systematic loss. On the other hand, for every trade, there is a buy side and a sell side, the total amount of asset and …
We study the problem of regret minimization for distributed bandits learning, in which agents work collaboratively to minimize their total regret under the coordination of a central server. Our goal is to design communication protocols with near-optimal regret and little communication cost, which is measured by the…
New model considers varying costs in learning, outperforming existing methods.
We analyze the sample complexity of learning from multiple experiments where the experimenter has a total budget for obtaining samples. In this problem, the learner should choose a hypothesis that performs well with respect to multiple experiments, and their related data distributions. Each collected sample is associat…
Research funding agencies routinely use a proportion of their total revenues to support internal administration and marketing costs. The ratio of administration to total costs, referred to as the administration ratio, is highly variable and within any single fund depends on many factors including the number and average…
Due to the broad range of applications of stochastic multi-armed bandit model, understanding the effects of adversarial attacks and designing bandit algorithms robust to attacks are essential for the safe applications of this model. In this paper, we introduce a new class of attack named action-manipulation attack. In …
Paper proposes a method to estimate project cost contingency reserves considering various types of uncertainty.
Bayesian approach detects changepoints with cost-sensitive data fidelity.
New attacks reduce bad queries in black-box classifiers, improving effectiveness.
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model can be expensive and hurt model performance. To remedy this, we consider feature …
New approach improves stock policies for paper companies, reducing waste and costs.
A new cost-frugal HPO method controls training cost during optimization.
Given a transportation cost , optimal maps minimize the total cost of moving masses from to . We find a pseudo-metric and a calibration form on such that the graph of an optimal map is a calibrated maximal submanifold. We define the mass of space-like current…
We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…
Paper studies fundamental limits of communication in distributed learning.
Federated Q-Learning achieves linear regret speedup with low communication cost.
Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
Thompson Sampling tackles USS, a sequential selection problem without feedback.
In the total least squares problem, one is given an matrix , and an matrix , and one seeks to "correct" both and , obtaining matrices and , so that there exists an satisfying the equation . Typically the problem is overconstrained, meanin…
Proposes an angle-based framework for multicategory cost-sensitive classification.
Bitcoin mining costs remain stable despite increased activity.
New algorithms for planning with adversarial changes in costs.
Optimizes dividend payouts with fixed costs and regime switching.
This paper examines a Markovian model for the optimal irreversible investment problem of a firm aiming at minimizing total expected costs of production. We model market uncertainty and the cost of investment per unit of production capacity as two independent one-dimensional regular diffusions, and we consider a general…
Study examines the impact of employment benefit costs on firm profitability.
Modeling climate change costs with stochastic interest rates shows inequality, but funding abatement can reduce this.
We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second that is separable in the difference between consecutive variable blocks. Examples of such problems include Fused Lasso estima…
This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g. sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of…
New algorithm reduces regret and constraint violation in constrained bandit problems.
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
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 . We show that, for , equilibrium strategie…
New method reduces regret and communication costs in federated Q-learning.
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…
Models of spatial firm competition assume that customers are distributed in space and transportation costs are associated with their purchases of products from a small number of firms that are also placed at definite locations. It has been long known that the competition equilibrium is not guaranteed to exist if the mo…
We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of interventions with minimum total cost, that can uniquely identify any causal graph with…
New framework for online influencer selection considering cost constraints.
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection vi…