SADCBO optimizes contextual variables by balancing relevance and cost.
problem Optimizing contextual variables with varying costs and unknown relevance.
method Adaptive selection of relevant contextual variables using sensitivity analysis and early stopping.
result Consistent improvement in optimization across various examples.
Optimal benchmark design varies based on costs in financial manipulation.
problem Manipulation of price benchmarks in finance.
method Analyzes empirical pattern and cost structures to determine optimal benchmark design.
result The optimal benchmark depends on the relative sizes of fixed and variable costs.
The paper presents efficient methods for identifying causal graphs with latent variables.
problem Recovering causal graphs with latent variables while minimizing intervention costs.
method Two intervention cost models (linear and identity) are considered. Algorithms are provided for both models.
result Upper bounds on the number of interventions needed for recovery, and approximation factors for the linear cost model.
The paper shows how variable discretization and cost-sensitive logistic regression improve credit scoring models on imbalanced data.
problem Bias in classification models on imbalanced datasets.
method Variable discretization and cost-sensitive logistic regression.
result Improves model performance on imbalanced credit scoring data and other domains.
LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.
problem Optimizing systems with costly variable updates in a sequence of modules.
method Lazy Modular Bayesian Optimization (LaMBO) that minimizes switching costs.
result LaMBO achieves vanishing regret and improves over existing cost-aware Bayesian optimization algorithms.
Novel Bayesian Optimization for cost-effective function evaluation.
problem Optimizing functions with variable evaluation costs.
method Bayesian Optimization with adaptive sampling.
result Significantly reduced optimization overhead compared to previous methods.
Efficient adjustment sets found for cost-minimized causal estimations.
problem Estimating interventional means with minimum cost in causal graphical models.
method Defined cost-adjustment sets, constructed flow networks, and used maximum flow algorithms.
result Minimum cost optimal adjustment sets exist and can be found efficiently.
Proposes an efficient method to select models under a budget constraint in cost-sensitive learning.
problem Cost-sensitive variable selection in classification problems.
method Ensemble of model schedules to find near optimal models under a budget constraint.
result Our approach outperforms existing methods in benchmark datasets.
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.
Study develops a cost model for field canals improvement projects in Egypt.
problem Accurately predict the preliminary costs of field canals improvement projects.
method Developed a parametric cost model using machine learning methods.
result Identified key cost drivers and developed a model for FCIPs.
The paper analyzes option pricing with variable transaction costs using a nonlinear model.
problem Analyzing option pricing under variable transaction costs with nonlinear dynamics.
method Transformation of a fully nonlinear parabolic equation into a quasilinear one, existence of classical smooth solutions, numerical approximation.
result Existence of classical smooth solutions and useful bounds on option prices.
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…
New method reduces gradient estimation costs for large datasets.
problem Optimizing large datasets efficiently.
method Stochastically Controlled Stochastic Gradient (SCSG) method.
result Communication and computation costs are independent of dataset size.
Unified approach to training stochastic RNNs with latent variables.
problem Training generative latent variable models with autoregressive decoders.
method Amortized variational inference with backward RNN conditioning and auxiliary reconstruction cost.
result Improved performance on speech and sequential MNIST benchmarks.
Efficient algorithms learn causal graphs with minimal interventions.
problem Learning causal relationships between observed variables in the presence of latents.
method Bi-criteria approximation goal combining intervention design and graph property testing.
result Achieve intervention cost within a small constant factor of the optimal.
Study analyzes costs of managing research funds, developing a model for optimal administration.
problem High variability in administration costs among research funding agencies.
method Identified standard agency activities, developed a model estimating optimum portfolio success rate and administration ratio.
result Model estimates optimum portfolio success rate and administration ratio based on input variables.
Efficiently reduces costs for Bayesian networks in FGrn form.
problem High computational and memory costs of Bayesian networks in FGrn form.
method Detailed algorithmic and structural analysis leading to cost reduction solutions, including an online learning algorithm.
result Proposed solutions and online learning algorithm significantly reduce costs for Bayesian networks.
Bayesian optimization reduces materials design costs by 10x.
problem Expensive materials design search space with mixed variables.
method Uncertainty-aware machine learning models for mixed numerical and categorical variables.
result Frequentist and Bayesian models perform differently in mixed-variable BO.
Paper solves investment problem with transaction costs using spectral method.
problem Optimal investment problem with transaction costs under potential utility.
method Spectral numerical method applied to a reformulated parabolic double obstacle problem.
result Spectral method proves more efficient for high precision solutions.
Spike-and-slab priors are improved for high-dimensional Bayesian regression.
problem Prohibitive computational costs for existing samplers in high-dimensional settings.
method Proposes Scalable Spike-and-Slab (S3) for high-dimensional Bayesian regression. result Improves computational cost to max{n2pt,np} per iteration, demonstrating significant speed-ups and quality gains. A new screening method for high-dimensional data reduces computational cost.
problem Challenges in variable selection for ultrahigh-dimensional linear regression.
method Ordering absolute sample ridge partial correlations to screen variables.
result The method provides sure screening property without strong assumptions.
Algorithms learn and test variable partitions in various groups and error metrics.
problem Learning and testing variable partitions in different groups and error metrics.
method Algorithms for agnostically learning and testing k-partitionability over various groups and error metrics. result Learning algorithms for k-partitionability with polynomial time complexity and testing with adaptive queries. Proposes a novel SVM model for binary classification with different misclassification costs.
problem Real-world classification problems with varying misclassification costs.
method Incorporates performance constraints in SVM formulation to seek a hyperplane with maximal margin and misclassification rates below given thresholds.
result The proposed model gives users control over misclassification rates in one class at the expense of the other.
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…
Study assesses the impact of Basel III reforms on Bangladeshi banks.
problem Impact of Basel III liquidity and capital requirements on Bangladeshi banks.
method Panel data analysis with fixed effects, including macroeconomic variables.
result Higher capital and liquidity requirements negatively affect banks' profitability but positively impact interest rates and private sector lending.
A new Metropolis-Hastings method reduces the cost of testing for large datasets.
problem Reducing the cost of Metropolis-Hastings tests for large datasets.
method Uses small minibatches and a novel Barker acceptance test with additive correction.
result Achieves arbitrarily small batch sizes by adjusting proposal step size or temperature.
We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incom…
Proposes COLA, a communication-efficient algorithm for decentralized optimization.
problem Decentralized consensus optimization over a network.
method Linearization and communication-censoring strategy to reduce computation and communication costs.
result Proven convergence and established convergence rates for COLA.
Variable renewables can avoid market value decline with policy changes.
problem Market value decline due to correlated generation from wind and solar.
method Theoretical analysis and simulation examples of market incentives and prices.
result Market value decline is due to policy assumptions, not inherent technology limitations.
Paper tackles online task allocation in multi-attribute social sensing.
problem Optimized task allocation in dynamic, multi-attribute social sensing.
method Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme using online reinforcement learning.
result Significantly outperforms state-of-the-art baselines in sensing accuracy and cost.
Designs interventions to learn causal graphs with minimum cost.
problem Learning causal graphs with minimum intervention cost.
method Prove NP-hardness, develop greedy and constrained algorithms.
result Achieve nearly optimal intervention design for sparse graphs.
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. While standard auto…
Proposes a new sensitivity measure for optimization problems.
problem Optimization of high-dimensional functions with expensive computer codes.
method Introduces a new influence measure based on the Hilbert-Schmidt Independence Criterion.
result The new measure significantly reduces the number of function evaluations.
A new method reduces computational costs for testing RF variable importance measures.
problem Testing variable importance measures from random forests is computationally expensive and challenging.
method Sequential permutation testing and sequential p-value estimation to reduce computational costs.
result Theoretical properties of sequential tests are confirmed, maintaining type-I error and high power.
Improved exploration in RL with latent state marginalization.
problem Complexity of deep probabilistic models limits their practical use in reinforcement learning.
method Adopting latent variable policies within the MaxEnt framework, with low-cost marginalization of latent states.
result Effective marginalization leads to better exploration and more robust training.
Kernel improves Gaussian process scalability for wide datasets.
problem Under-performance of Gaussian processes on wide data.
method Introduces Bézier Gaussian Process kernel with exponential summarising variables growth and linear cost.
result Empirically demonstrates scalability to both tall and wide datasets.
A new method improves coordinate descent by adaptively selecting coordinates.
problem Coordinate descent's inefficiency due to checking all coordinates.
method Adaptive multi-armed bandit algorithm to select coordinates.
result Improves convergence of coordinate descent methods.
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step. Particle filters fail for models that include such variables, while methods that use Gibbs sampling of parameter variab…
This paper examines how taxation and stochastic interest rates affect GMWB Variable Annuities.
problem Improving the financial cost and withdrawal dynamics of GMWB Variable Annuities.
method Developed a numerical framework to compute fair value of GMWB contracts, accounting for taxation and stochastic interest rates.
result Accounting for both taxation and stochastic interest rate significantly impacts GMWB withdrawal strategy and cost.
Unified framework for transfer learning regression without extra cost.
problem Transfer learning for regression models without additional implementation cost.
method Introduces a density-ratio reweighting function estimated via Bayesian framework.
result Unified integration of three TL methods with no extra cost.
New solar tracking system uses computer vision and low-cost hardware.
problem Current solar tracking systems are expensive and have operational issues.
method Low-cost hardware, computer vision, and deep learning.
result The new system shows great potential and can improve solar tracking performance.
Sparse models help in selecting fewer variables for efficient predictions.
problem Overfitting and high computational costs in learning models.
method Automated variable selection for sparse predictive models.
result Sparse models improve model efficiency and interpretability.
Study on thermodynamic costs of simple linear regression.
problem Understanding thermodynamic costs in machine learning models.
method Approximated thermodynamic lower bounds for exact and stochastic linear regression.
result Derived scaling laws for optimal dataset size based on generalization error.
A new method selects important variables for clustering from dependency networks.
problem Variable selection for clustering in high-cost data scenarios.
method Create dependency networks, rank variables by centrality, select top-n variables.
result Top-n variables improve clustering performance compared to existing methods.
New RL method improves financial index tracking accuracy.
problem Optimizing financial index tracking with dynamic market information.
method Discrete-time dynamic model, Banach fixed point iteration, deep reinforcement learning.
result Proposed RL method outperforms benchmarks in tracking accuracy.
Study examines growth dynamics and trade-off between value and cost in evolving networks.
problem Understanding growth and trade-off in real-world networks.
method Investigates preferential attachment in temporal networks with modified BA model and differential equations.
result Illustrates future equilibrium of evolving networks and trade-off between value and cost.
Paper tackles stochastic reinforcement learning with reduced observation costs.
problem Non-deterministic rewards and punishments with stochastic elements.
method Explicitly models stochastic elements and learning costs.
result Quantitative analysis of learning success criteria and observation cost probabilities.