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.
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.
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.
Optimal trade execution strategies show adaptive methods reduce costs.
problem Optimal trade execution with short-term price predictive signals.
method Comparison of static and adaptive strategies with transient and instantaneous market impacts.
result Adaptive strategies significantly reduce transaction costs compared to static strategies.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Paper tackles robust control policy learning for uncertain systems.
problem Learning control policies for an unknown linear dynamical system with quadratic cost.
method Convex optimization method balancing exploitation and exploration.
result Minimizes worst-case cost by reducing uncertainty in model parameters.
Poyiadjis et al. (2011) show how particle methods can be used to estimate both the score and the observed information matrix for state space models. These methods either suffer from a computational cost that is quadratic in the number of particles, or produce estimates whose variance increases quadratically with the am…
FTPL policy achieves best-of-both-worlds regret in decoupled bandits with reduced computational cost.
problem Decoupled multi-armed bandit problem with observed and unobserved losses.
method Follow-the-Perturbed-Leader (FTPL) policy that avoids convex optimization and resampling.
result Achieves constant regret in stochastic regime and optimal O ( K T ) O(\sqrt{KT}) O ( K T ) regret in adversarial regime. 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.
SGLBO optimizes quantum circuits with fewer measurements, improving accuracy and noise resilience.
problem Efficiently optimizing parameterized quantum circuits with reduced measurement shots and noise.
method Developed SGLBO combining SGD and BO, with adaptive measurement-shot strategy and suffix averaging.
result Significantly reduces measurement-shot cost while improving accuracy and noise resilience.
LanguaShrink compresses prompts using psycholinguistic principles to reduce costs.
problem Reducing computational cost and efficiency in large language model inference.
method Leverages psycholinguistic principles and the Ebbinghaus memory curve to compress prompts.
result Achieves up to 26 times compression while maintaining semantic similarity.
New algorithm reduces bandit regret by graph domination number.
problem Adversarial multi-armed bandit with partial observations and switching costs.
method New algorithm with improved policy regret bounds.
result Regret depends only on the domination number of the feedback graph.
Paper shows how meta-learning can reduce prior learning cost.
problem Learning the prior in meta-learning with fast rates.
method Examined Gibbs algorithm in meta-learning context.
result Bernstein's condition holds at meta level, reducing prior learning cost.
ROAD-EnKFs use learned low-dimensional models to improve state reconstruction and forecasting.
problem Reconstructing and forecasting states of unknown or expensive systems.
method Learned low-dimensional surrogate models and ensemble Kalman filter integration.
result ROAD-EnKFs achieve higher accuracy at lower computational cost than existing methods.
Standard micro-economics concentrate on the description of markets but is seldom interested in production. Several economists discussed the concept of a firm, as opposed to an open labour market where entrepreneurs would recrute workers on the occasion of each business opportunity. Coase \cite{Coase} is one of them, wh…
Less frequent retraining improves forecast accuracy in retail demand forecasting.
problem Balancing forecast accuracy and computational efficiency in global models.
method Analysis of ten machine learning and deep learning models across two large retail datasets with various retraining scenarios.
result Less frequent retraining strategies maintain forecast accuracy while reducing computational costs.
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.
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-base…
We study the high-frequency limits of strategies and costs in a Nash equilibrium for two agents that are competing to minimize liquidation costs in a discrete-time market impact model with exponentially decaying price impact and quadratic transaction costs of size θ ≥ 0 θ\ge0 θ ≥ 0 . We show that, for θ = 0 θ=0 θ = 0 , equilibrium strategie…
Unified framework reduces NFEs for inverse problems.
problem High computational costs and degraded reconstruction quality in existing LDM-based inverse solvers.
method Consistency Regularised Gradient Flows for posterior sampling and prompt optimization.
result Significantly reduced computational cost with state-of-the-art performance.
New sparsification technique for SGD reduces communication costs.
problem High communication costs in distributed SGD for large-scale models.
method Statistical estimation model for sparsity and skewness of stochastic gradients.
result Concatenated top-k and random-k sparsification outperforms individual methods.
SpeakerStew verifies 46 languages with reduced training and inference costs.
problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
problem Improving LLM inference efficiency at a fixed budget.
method Reset-and-Discard (ReD) query method.
result ReD increases coverage@cost for a given budget, reducing attempts and cost.
Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection
problem Estimating parameters in time series
method Composite likelihood method
result The method reduces computational cost
New algorithm for multi-fidelity bandits reduces costs and improves regret.
problem Optimizing decisions with varying costs and accuracy in multi-fidelity bandits.
method Cost complexity bounds, algorithmic framework, elimination-based algorithm.
result New regret definition and matching upper and lower bounds for elimination-based algorithm.
Dark blockchain venues increase miners' profits but raise users' execution risk.
problem Exploitable information leakage in blockchain transactions.
method Economic incentive analysis and empirical study of dark venues.
result Dark venues increase miners' profits but raise users' execution risk.
The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.
problem Learning navigation costs from expert demonstrations in partially observable environments.
method Develops a cost function representation composed of a probabilistic occupancy encoder and a cost encoder, optimized by differentiating the error between demonstrated controls and a control policy computed from the cost encoder.
result The method outperforms baseline IRL algorithms in robot navigation tasks, improving both training and test-time efficiency.
Method generates prototypes from small datasets for efficient learning.
problem Efficiently learning from small datasets with soft labels.
method Modular method for generating soft-label prototypical lines and Hierarchical Soft-Label Prototype k-Nearest Neighbor algorithm.
result High classification accuracy with significantly fewer prototypes than classes.
The paper teaches robots to navigate by learning costs from expert demonstrations.
problem Teaching robots to navigate autonomously using only expert observations.
method Developed a map encoder and cost encoder to infer semantic class probabilities and a cost function from expert observations.
result Robots can learn to follow traffic rules in a simulator using only semantic observations.
Smart grid uses deep learning to optimize household energy use.
problem Optimizing household energy use under real-time pricing schemes.
method Multi-agent deep actor-critic learning for decentralized agents with partial observability.
result Deep reinforcement learning reduces peak-to-average energy consumption and costs.
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples to reduce cost. For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effectiv…
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.
Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.
problem Improving decision-making in contexts with uncertain cost coefficients using partial feedback.
method Unified class of offline learning algorithms for CLO with different types of feedback, using IERM framework.
result Fast-rate regret bound for IERM with partial feedback and misspecified model classes.
CobBO optimizes expensive functions in high dimensions by using a two-stage kernel approach.
problem Bayesian optimization struggles in high dimensions due to computational inefficiency.
method Coordinate backoff Bayesian Optimization with two-stage kernels.
result CobBO finds solutions comparable to or better than other methods in high dimensions.
Machine learning predicts Bitcoin returns but trading performance drops with costs.
problem Trading Bitcoin predictions with transaction costs.
method XGBoost, LSTM, iTransformer models evaluated in walk-forward protocol; cost-aware execution filter implemented.
result Cost-aware execution filter restores profitability; XGBoost strategy outperforms.
The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.
problem Learning cost-optimal sequential decision policies from retrospective data with missing test results.
method Doubly robust Q-learning framework with path-specific inverse probability weights.
result The method reduces testing cost without compromising predictive accuracy.
New bid shading algorithm reduces costs by 55%.
problem Minimizing costs in online advertising auctions.
method Win-rate prediction and surplus maximization using logistic regression.
result 7% more profit for advertisers compared to benchmarks.
New pruning method breaks power law scaling, potentially reducing error to exponential.
problem Improving neural network performance through scaling alone is costly.
method Developed a new data pruning metric to break power law scaling.
result Pruned datasets show better than power law scaling on various image datasets.
Reduced numerical precision is a common technique to reduce computational cost in many Deep Neural Networks (DNNs). While it has been observed that DNNs are resilient to small errors and noise, no general result exists that is capable of predicting a given DNN system architecture's sensitivity to reduced precision. In …
Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…
New estimator reduces nested expectation estimation costs.
problem Estimating repeatedly nested expectations is computationally expensive.
method Recursive Estimator for Arbitrary Depth (READ) using randomized multilevel Monte Carlo.
result Optimal computational cost of O(ε^(-2)) for every fixed D.
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…
New algorithm reduces dynamic regret in time-varying movement costs.
problem Dynamic regret in online convex optimization with time-varying movement costs.
method Introduced a novel algorithm for time-varying movement costs, achieving comparator-adaptive dynamic regret bound.
result Established first comparator-adaptive dynamic regret bound of O ~ ( ( M 2 + M P T ) ( T + ∑ t λ t ) ) \widetilde{\mathcal{O}}(\sqrt{(M^2+MP_T)(T+\sum_t λ_t)}) O ( ( M 2 + M P T ) ( T + ∑ t λ t ) ) . VFDS selects dynamic features for efficient HAR tasks, optimizing performance-cost trade-offs.
problem Optimizing feature selection for varying costs and dynamic contexts in machine learning tasks.
method Bayesian learning framework with variational dynamic selection policy.
result VFDS selects different features under changing contexts, saving sensory costs while maintaining HAR accuracy.
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.