New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
Digital twin reduces costs in various fields.
problem High costs in decision-making processes.
method Use of digital twin model for cost reduction.
result Digital twin acts as a cost reduction method.
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…
New sparsity operator reduces variance reduction methods' computational cost.
problem Reduce computational cost of variance reduction methods.
method Introduce random-top-k operator to estimate gradient sparsity and reduce operations per update.
result Our algorithm consistently outperforms SpiderBoost in various tasks.
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
problem Sparse-group lasso's computational expense and need for tuning.
method Dual Feature Reduction (DFR) using strong screening rules and dual norms.
result DFR drastically reduces computational cost without affecting solution optimality.
New method reduces deep learning training costs by approximating vector-jacobian products.
problem Efficiently training deep neural networks with reduced computational and memory costs.
method Randomized, unbiased approximations of vector-jacobian products during backpropagation.
result Validated potential for reducing deep learning training costs through unbiased estimates.
Designs a neural network to reduce training cost by mapping to higher dimensions.
problem High training cost in neural networks.
method Maps feature vectors to higher dimensional space, designs weight matrices to reduce cost, uses convex constraints.
result Reduces training cost as the number of layers increases, without cross-validation.
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.
problem Estimating Greeks for barrier and arithmetic average Asian options.
method Global sensitivity analysis, Chebyshev interpolation, conditional pathwise method, randomized Quasi Monte Carlo, Brownian bridge discretization, importance sampling.
result Reduced effective dimensionality enhances convergence rate and accuracy of randomized Quasi Monte Carlo integration.
Optimizes package types for e-commerce to reduce damage and costs.
problem Sub-optimal package types lead to damaged shipments and high costs.
method Multi-stage approach that balances shipment and damage costs using a scalable algorithm.
result Significant cost savings of tens of millions of dollars achieved.
Bayesian framework reduces high-dimensional GP modeling costs.
problem Challenges in fitting Gaussian processes to high-dimensional inputs.
method Hierarchical Bayesian model with orthonormal projection matrix, incorporating Deep Gaussian Processes.
result Improves predictive performance and uncertainty quantification.
Low redispatch prices boost green hydrogen production cost, encouraging electrolyzer siting.
problem Uncertainty in redispatch power availability and its impact on green hydrogen production cost.
method Historic redispatch time series analysis and power purchase scenarios evaluation.
result Low price levels can lead to notable production cost reductions, incentivizing electrolyzer siting.
Enhances LMC for log-concave sampling, reducing computational cost.
problem High computational cost of LMC for high-dimensional problems.
method Random coordinate descent (RCD) combined with variance reduction techniques (SAGA, SVRG).
result Achieves computational cost reduction compared to classical LMC, same number of iterations as LMC.
New auction design uses statistical learning to reduce costs and improve fairness.
problem Designing efficient multi-item auctions with reduced implementation costs and fairness.
method Nonparametric density estimation for credible intervals, two new strategies.
result Strategies consistently outperform alternative methods in revenue maximization and cost reduction.
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.
problem Reducing poverty through effective social protection mechanisms.
method Modeling household capital dynamics under four insurance frameworks (uninsured, insured, insured with subsidies, insured with flexible premiums) to assess poverty reduction and governmental costs.
result Subsidized insurance schemes provide maximum social benefits while reducing governmental costs, effectively reducing poverty.
This paper studies a theoretical pruning method for RNNs to reduce computational costs.
problem High computational costs in recurrent neural networks (RNNs).
method Spectral pruning inspired approach for RNNs.
result Generalization error bounds for compressed RNNs are provided.
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.
problem High dimensionality and Monte Carlo runs in stochastic systems.
method Extending SVD-based model reduction to stochastic differential equations.
result Preserving symplectic structures improves accuracy and energy conservation.
New approach improves stock policies for paper companies, reducing waste and costs.
problem Improving stock policies for integrated paper companies.
method Developed a new approach to determine near-optimal stock policies.
result Reduction in total waste by 9% and logistics 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.
problem Expensive hyperparameter search for non-stationary model training.
method Two-stage approach: efficient configuration identification followed by full training.
result Up to 10x reduction in hyperparameter search cost on public benchmark.
CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.
problem Optimizing solutions under uncertainty with reduced conservatism.
method CADRO uses a cost-aware ambiguity set to reduce DRO's conservatism.
result CADRO provides high-confidence upper bounds and consistent estimators of out-of-sample expected cost.
The paper analyzes how to combine self-protection and self-insurance for risk reduction.
problem Combining self-protection and self-insurance for risk reduction when market insurance is absent.
method The approach uses Value-at-Risk and Tail Value-at-Risk to evaluate residual risk and solves the problem using isoquant geometry based on marginal-balance curves.
result The analysis identifies the conditions under which self-protection and self-insurance behave as substitutes or complements.
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
problem Efficiently estimating quantities of interest from multi-fidelity simulations.
method Bayesian sequential strategy that maximizes the ratio of expected uncertainty reduction to simulation cost.
result MR-SUR strategy unifies and provides principled approaches to develop new methods.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
problem High tax administrative costs in Germany and other jurisdictions.
method Statistical data, surveys, and a novel approach to measure total administrative cost as a percentage of total tax revenue.
result Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue.
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.
problem Near-optimal federated Q-learning with low communication cost.
method Reference-advantage decomposition for variance reduction, synchronization between agents and server, policy update.
result Achieves almost optimal regret and near-linear regret speedup compared to single-agent learning.
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.
problem Executing large orders in markets under time and liquidity constraints.
method Model Predictive Control (MPC) framework balancing order completion, market impact, and opportunity cost.
result Significant reductions in slippage and schedule shortfall compared to benchmarks.
Cost-aware SBI reduces expensive simulations in complex models.
problem High computational cost in simulating complex models.
method Combination of rejection and self-normalised importance sampling.
result Significant reduction in overall cost of inference.
Eldredge, Gordina and Saloff-Coste recently conjectured that, for a given compact connected Lie group G, there is a positive real number C such that λ1(G,g)diam(G,g)2≤C for all left-invariant metrics g on G. In this short note, we establish the conjecture for the small subclass of natural…
The paper reduces xVA calculations by approximating sensitivities.
problem Nested expectation problem and computational expense in xVA calculations.
method Polynomial approximations of shocked and unshocked valuation functions, and their difference.
result High accuracy and remarkable computational cost reduction demonstrated.
A deep learning algorithm designs low-cost SPP films.
problem Designing SPP films with high accuracy and low cost.
method Guided deep learning algorithm with low-cost sample replacement.
result Deep CNN learns to replace precious metals with ordinary metals, reducing cost.
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.
problem High computational cost of generating training data for PDE surrogate models.
method STAP (Selective Time-Step Acquisition for PDEs) framework that acquires only important time steps.
result Demonstrated effectiveness on several benchmark PDEs, reducing training costs.
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.
problem Sparse estimate distributions, high computational cost, and high sampling variance in EHR outcome prediction.
method Proposed SCOPE and REACH estimators that leverage next-token probability distributions.
result SCOPE and REACH match Monte Carlo accuracy with token reductions of 2.5-3.4 times and variance guarantees.
Algorithm optimizes electricity procurement costs by 1.65%.
problem Minimizing energy cost while covering forecast consumption.
method Deep learning forecasting and deviation indicator.
result Reduction of 1.65% in costs compared to uniform policy.
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
problem Tedium and subjectivity in traditional cost of equity and comparable company analysis methods.
method Uses spectral and agglomerative clustering to compute cost of equity and perform comparable company analysis.
result Reduces time required for comps by orders of magnitude and improves consistency and reliability.
This thesis explores fast algorithms for large matrices and data augmentation to improve model efficiency.
problem Efficient handling of large models and data in scientific computing and machine learning.
method Randomized low-rank decomposition algorithms and data augmentation techniques.
result Improved sample efficiency and generalization of machine learning models.
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…