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.
There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effective in reducing annotation costs for these tasks. We have previously developed an AL algorithm called ClosestInitPA that works best with tas…
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.
New algorithm reduces misclassification costs in neural networks.
problem Reduces costs of misclassified instances in neural networks.
method Adaptive Cost-Sensitive Learning (AdaCSL) adjusts loss function to bridge class distribution mismatches.
result Deep neural networks with AdaCSL outperform other methods on cost-sensitive binary classification tasks.
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.
New framework reduces cost of financial option pricing simulations on FPGAs.
problem Efficiently simulate financial option pricing with reduced computational cost.
method Nested MLMC framework with low precision calculations on FPGAs.
result Higher computational savings compared to existing mixed-precision MLMC frameworks.
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.
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.
Hierarchical GANs reduce anomaly detection costs.
problem Balancing anomaly detection accuracy and sampling costs.
method Hierarchical GANs for nonuniform sampling and buffer zones.
result Proposed GAN-based detector outperforms baseline in detection delay and average cost of error.
The paper develops a method to learn robust decision policies from observational data, reducing high-cost outcomes.
problem Learning safe decision policies from observational data with high-risk outcomes.
method Develops a method to learn policies that reduce high-cost outcomes, valid under finite samples and uneven feature overlap.
result Validates the method with real and synthetic data, providing statistical bounds on decision costs.
BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.
problem Communication bottleneck in federated learning, especially with stochastic updates.
method Introduces BICompFL, a bi-directional compression approach for stochastic federated learning.
result Significantly reduces communication costs (by an order of magnitude) while maintaining accuracy.
Resource-efficient oblique trees reduce neural signal classification costs.
problem Implementing efficient neural signal classifiers on resource-constrained devices.
method Integrating model compression, probabilistic routing, and cost-aware learning.
result Significant reduction in model size and feature extraction cost compared to state-of-the-art models.
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.
The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.
problem Creating an arbitrage-free interpolation for option pricing models.
method Generalizing the local variance gamma model to a piecewise quadratic local variance function.
result The quadratic model results in an arbitrage-free interpolation of class C3, reducing knots and computational cost.
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…
Crowdsourced reinforcement learning optimizes knee replacement pathway, reducing costs.
problem Optimizing the sequential decision process for knee replacement surgery.
method Reinforcement learning, value iteration, state compression, kernel representation, cross validation.
result Optimized policy reduces overall cost by 7% and excessive cost premium by 33%.
Reducing ICD-10 code granularity improves cost model accuracy and stability.
problem High-dimensional regression with ICD-10 codes leads to unstable coefficient estimates.
method Log-linear analytics approach to cost model regularization through diagnostic code merging.
result Reducing ICD-10 code granularity from 7 characters to 6 or fewer improves model interpretability and consistency.
State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has address…
A protocol reduces transaction costs for portfolio managers.
problem Transaction costs reduce portfolio returns over time.
method Distributed convex optimization protocol.
result Trades converge to optimal for the firm with adjustments.
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
FedElasticNet reduces communication costs and handles client drift in FL.
problem Expensive communication costs and client drift issues in federated learning.
method Leverages elastic net regularizers to sparsify local updates and limit client drift.
result FedElasticNet effectively resolves communication cost and client drift problems.
HSQ reduces communication costs in federated learning.
problem High cost of communicating gradients in federated learning.
method Hyper-sphere quantization (HSQ) framework for efficient gradient compression.
result HSQ achieves O(logd) per-iteration communication cost, significantly reducing costs without compromising accuracy. Sparsity helps reduce diffusion model costs.
problem High computational costs in diffusion models.
method Introduced sparsity concept to reduce input dimensionality.
result Sparsity reduces computational complexity to intrinsic data dimension.
Cost-effective feature selection improves network model choice.
problem Selecting informative features from noisy candidates in network models.
method Adapted feature selection methods to account for feature costs and used pilot simulations.
result Reduced computational cost by two orders of magnitude without sacrificing model accuracy.
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.
FedLion improves Federated Learning by speeding up convergence and reducing communication costs.
problem Slow convergence and high communication costs in Federated Learning.
method Integrates Lion's adaptive approach into Federated Learning framework, using signed gradients.
result FedLion outperforms existing adaptive algorithms in convergence rate and communication efficiency.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
This work reduces computation cost for on-device CNN training.
problem High computation cost during on-device CNN training.
method Self-supervised instance filtering and error map pruning.
result Substantial computation saving without significant accuracy loss.
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.
Paper proposes EEIPU, a memoization-aware BO algorithm to reduce hyperparameter tuning costs.
problem High costs in GPU-days for training and fine-tuning language models.
method Memoization-aware Bayesian Optimization (EEIPU) algorithm in tandem with pipeline caching.
result EEIPU produces 103% more hyperparameter candidates and 108% more validation metric improvement.
This paper proposes structurally sparse RNNs to reduce computational and memory costs.
problem Heavy computational and memory burden in fully connected RNNs.
method Study structurally sparse RNNs, reducing recurrent operations and weights.
result Structurally sparse RNNs achieve competitive performance with reduced costs.
Improved method for encoding contingency tables reduces mutual information bias.
problem Mutual information bias in measuring label similarity.
method Improved method for encoding contingency tables to reduce information cost.
result Better bound on reduced mutual information in typical use cases.
New method reduces labeling costs in semi-supervised learning.
problem Efficiently label data with limited labeled samples.
method Formulates batch acquisition as bilevel optimization.
result Shows effectiveness in keyword detection tasks.
Collage-CNN reduces cloud inference latency by 1.47X with 9X reduced latency variation.
problem Reducing latency variance in cloud machine learning inference.
method Proposes a novel Collage-CNN model that combines multiple images for classification, providing redundancy and cost efficiency.
result Significant reduction in 99th percentile tail latency and variation in inference latency.
New algorithms reduce communication costs in collaborative learning.
problem Reducing communication costs in collaborative learning.
method Distributed boosting and adaptation to classification noise.
result Communication-efficient algorithms for collaborative PAC learning robust to noise.
We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example. We propose a novel 0-1 integer program formulation …
Optimized portfolio turnover strategies enhance wealth and reduce costs.
problem Minimizing transaction costs and maximizing wealth in small to medium-sized portfolios.
method Dynamic multi-period model with column generation algorithm to minimize turnover constraints.
result The proposed model leads to higher portfolio values and lower transaction costs compared to a naive model.
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.
Bayesian approach detects changepoints with cost-sensitive data fidelity.
problem Detecting abrupt shifts in time series data with limited resources.
method Bayesian approach with active, cost-sensitive data fidelity switching.
result Information-based approach reduces total cost while maintaining accuracy.
PaGoDA reduces diffusion model training costs by 64x.
problem Diffusion models are computationally expensive during training.
method Three-stage pipeline: downsampled training, distillation, progressive super-resolution.
result PaGoDA achieves state-of-the-art performance with reduced training costs.
Linear cost method approximates Gaussian Matérn processes with exponentially convergent accuracy.
problem High computational cost for Gaussian process inference and prediction.
method Optimal rational approximation of spectral density for Gaussian processes on bounded intervals.
result Exponential decrease in covariance error with increasing order of approximation.
SAEP prunes sub-architectures to reduce search cost while maintaining performance.
problem Redundancy in ensemble sub-architectures leads to high computational cost.
method SAEP leverages diversity to prune sub-architectures, reducing ensemble size.
result SAEP reduces the number of sub-architectures without degrading performance.
New framework reduces strategic manipulation cost for minority groups in fair classification.
problem Strategic manipulation disparities in fair classification.
method Constrained optimization framework that constructs classifiers to reduce strategic manipulation cost for minority groups.
result Empirically, the approach reduces strategic manipulation cost for minority groups over multiple real-world datasets.
Pruning filters in neural networks improves performance and reduces costs.
problem Large neural networks have high inference costs and limited resource usage.
method Batch Bridgeout for efficient pruning of convolutional filters.
result Batch Bridgeout trained networks achieve higher accuracy across various pruning intensities.
Deep learning involves a difficult non-convex optimization problem with a large number of weights between any two adjacent layers of a deep structure. To handle large data sets or complicated networks, distributed training is needed, but the calculation of function, gradient, and Hessian is expensive. In particular, th…
A new method reduces bootstrap simulation cost and improves accuracy.
problem Efficiently simulating input uncertainty with large sample sizes.
method Orthogonal Bootstrap: Decomposes into Infinitesimal Jackknife and orthogonal parts.
result Significantly reduces computational cost and maintains accuracy.
This paper optimizes AI inference on edge devices with reduced communication and computation costs.
problem Efficiently performing AI inference on resource-constrained edge devices with reduced communication and computation costs.
method A three-step framework for effective inference: model split point selection, communication-aware model compression, and task-oriented encoding of intermediate features.
result Our proposed framework achieves a better trade-off and significantly reduces inference latency compared to baseline methods.
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.