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.
Method minimizes total cost of classification by acquiring covariates efficiently.
We study partial hedging for game options in markets with transaction costs bounded from below. More precisely, we assume that the investor's transaction costs for each trade are the maximum between proportional transaction costs and a fixed transaction costs. We prove that in the continuous time Black--Scholes (BS) mo…
Cost-aware BO minimizes function evaluations with varying costs.
CADO optimizes heatmap-based solvers for cost minimization, overcoming performance limitations.
The paper constructs upper bounds for cost minimization in shallow neural networks.
Optimizes insurance processing capacity to minimize costs.
In the present work, the optimal portfolio minimizing the investment risk with cost is discussed analytically, where this objective function is constructed in terms of two negative aspects of investment, the risk and cost. We note the mathematical similarity between the Hamiltonian in the mean-variance model and the Ha…
We study shortfall risk minimization for American options with path dependent payoffs under proportional transaction costs in the Black--Scholes (BS) model. We show that for this case the shortfall risk is a limit of similar terms in an appropriate sequence of binomial models. We also prove that in the continuous time …
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…
LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret"). We merge these two objectives by providing the theoretical analysis of cost minimizing algorithms that are also delta-PAC (with a proven guaranteed bound on th…
Study schedules jobs with unknown holding costs to minimize expected cumulative cost.
New algorithm for multi-fidelity bandits reduces costs and improves regret.
Improves classifier evaluation by aligning with Total Classification Cost.
New algorithms minimize regret with global costs in online learning.
Study minimizes risk in MDPs with spectral measures.
New algorithm reduces costs in wind energy systems by minimizing decision changes.
A new method for optimal transport using neural ODEs that preserves marginal constraints.
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…
We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. regularization impedes the …
New RL algorithms reduce costs for single-agent and federated learning.
Previous studies into the budget constraint of portfolio optimization problems based on statistical mechanical informatics have not considered that the purchase cost per unit of each asset is distinct. Moreover, the fact that the optimal investment allocation differs depending on the size of investable funds has also b…
Dynamic reinsurance minimizes insurer's cost of capital over time.
New method minimizes regret in AMDP with high probability.
Designs a neural network to reduce training cost by mapping to higher dimensions.
New model considers varying costs in learning, outperforming existing methods.
The paper constructs minimizers for deep learning networks and analyzes their geometric structure.
Optimal control in changing systems without strong convexity assumptions.
Let and be compact smooth oriented Riemannian -manifolds without boundary embedded in . Several problems about minimal distortion bending and morphing of to are posed. Cost functionals that measure distortion due to stretching or bending produced by a diffeomorphism are …
A new algorithm finds minimizers in dueling optimization with a monotone adversary.
We study Monge's optimal transportation problem, where the cost is given by optimal control cost. We prove the existence and uniqueness of an optimal map under certain regularity conditions on the Lagrangian, absolute continuity of the measures with respect to Lebesgue, and most importantly the absence of sharp abnorma…
New learning algorithm for real analytic functions without gradient descent.
Survey of universal portfolio techniques for minimizing investment regret.
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…
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we minimize the worst-case cost, accounting for system uncertainty given the observed …
Paper introduces a new cost function to improve deep learning model generalization.
MCAL reduces labeling costs by 6x for auto-labeling data sets.
The paper improves competitive and dynamic regret bounds for smoothed online learning.
Zero loss is achievable in overparametrized DL networks under specific conditions.
COF algorithm minimizes cost in multi-armed bandits with known costs and reward constraints.
Develops a framework for cost-efficient Bayesian optimization with constraints.
Agents collaborate to minimize regret while keeping costs under a threshold.
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…
We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gat…
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…
In this research we study a finite horizon optimal purchasing problem for items with a mean reverting price process. Under this model a fixed amount of identical items are bought under a given deadline, with the objective of minimizing the cost of their purchasing price and associated holding cost. We prove that the op…
We present a dynamic model selection approach for resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and predict…
In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function. We exhibit and compute a polynomial cost of adaptation to the H{ö}lder regularity for regret minimization. To do this, we first reconsider the recent lower bound of Locatelli an…