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.
New method assesses energy storage value beyond cost reduction.
Digital twin reduces costs in various fields.
We discuss investment allocation to multiple alpha streams traded on the same execution platform with internal crossing of trades and point out differences with allocating investment when alpha streams are traded on separate execution platforms with no crossing. First, in the latter case allocation weights are non-nega…
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
New method reduces deep learning training costs by approximating vector-jacobian products.
Designs a neural network to reduce training cost by mapping to higher dimensions.
We present a theoretical analysis and empirical evaluations of a novel set of techniques for computational cost reduction of classifiers that are based on learned transform and soft-threshold. By modifying optimization procedures for dictionary and classifier training, as well as the resulting dictionary entries, our t…
Effective dimensionality reduction improves accuracy and reduces costs in estimating option Greeks.
Optimizes package types for e-commerce to reduce damage and costs.
Low redispatch prices boost green hydrogen production cost, encouraging electrolyzer siting.
Enhances LMC for log-concave sampling, reducing computational cost.
New auction design uses statistical learning to reduce costs and improve fairness.
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model…
In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis. Such dimension reduction can be very efficient when the signals of interest have a strong structure…
Subsidized insurance reduces poverty by providing social benefits and lowering government costs.
This paper studies a theoretical pruning method for RNNs to reduce computational costs.
In many classification systems, sensing modalities have different acquisition costs. It is often {\it unnecessary} to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, m…
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…
SVD-based methods reduce computational cost for stochastic systems.
New approach improves stock policies for paper companies, reducing waste and costs.
Ensemble learning has had many successes in supervised learning, but it has been rare in unsupervised learning and dimensionality reduction. This study explores dimensionality reduction ensembles, using principal component analysis and manifold learning techniques to capture linear, nonlinear, local, and global feature…
Efficiently identifies promising hyperparameters for online learning models.
CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.
The paper analyzes how to combine self-protection and self-insurance for risk reduction.
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of mechanical and dynamical systems (reliability, probability of failure, stability, etc.).…
Paper proposes FedQ-Advantage for federated Q-learning with near-optimal regret and low communication cost.
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the …
MPC framework reduces execution costs and schedule deviations in trading.
Cost-aware SBI reduces expensive simulations in complex models.
Eldredge, Gordina and Saloff-Coste recently conjectured that, for a given compact connected Lie group , there is a positive real number such that for all left-invariant metrics on . In this short note, we establish the conjecture for the small subclass of natural…
The paper reduces xVA calculations by approximating sensitivities.
Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in runtime efficiency on hardware, yet it often comes at the cost of reduced accuracy. This paper proposes a quantization approach that increases…
New method reduces PDE surrogate model training costs by selectively acquiring time steps.
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which…
Motivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these subspaces exist across different blades---under the same parametrization---largely independent of their Mach number or Reynolds number. This is…
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
Algorithm optimizes electricity procurement costs by 1.65%.
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
This thesis explores fast algorithms for large matrices and data augmentation to improve model efficiency.
Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing values that minimize the prediction error on a held out dataset. We argue that error reduction is o…
In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashing), for large-scale high-dimensional classification. Previous theoretical results on randomized reduction methods hinge on strong assumptio…
In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size. MBS adaptively reduces each CNN macroblock depending on its information redundancy measured by our proposed effective flops. Empirical studies conducted with ImageNet and CIFAR-…
New RL algorithms reduce costs for single-agent and federated learning.
The problem of quantile hedging for basket derivatives in the Black-Scholes model with correlation is considered. Explicit formulas for the probability maximizing function and the cost reduction function are derived. Applicability of the results for the widely traded derivatives as digital, quantos, outperformance and …