Cost-aware multi-objective Bayesian optimization for non-uniformly expensive functions.
problem Non-uniform cost of function evaluations in Bayesian optimization.
method Introduces cost-aware constraints and a new acquisition function to optimize multi-objective functions with varying costs.
result Demonstrates improved optimization in hyperparameter tuning of neural networks and random forests.
This work improves cost-aware Bayesian optimization by introducing Pareto-efficient acquisition functions.
problem Cost variability in hyperparameter evaluations affects the efficiency of Bayesian optimization.
method Reformulated cost-aware Bayesian optimization as Pareto efficiency, proposing a novel Pareto-efficient expected improvement.
result Pareto-efficient acquisition functions significantly outperform previous solutions, providing finer control over cost-accuracy trade-offs.
Cost-aware cascading bandits model for optimizing rewards minus costs.
problem Optimizing rewards minus costs in sequential item examination.
method Proposes a cost-aware cascading bandits model with UCR-T1 policy for offline setting and CC-UCB algorithm for online setting.
result The CC-UCB algorithm achieves cumulative regret scaling in O(log T ) and matches lower bound.
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.
Bayesian optimization with cost-awareness using Gittins index.
problem Optimizing unknown functions with limited data evaluations and costs.
method Developed a connection between cost-aware Bayesian optimization and the Pandora's Box problem, using the Gittins index as an acquisition function.
result The Gittins index-based acquisition function performs well in cost-aware Bayesian optimization, especially in high dimensions.
Cost-aware BO minimizes function evaluations with varying costs.
problem Optimization with varying evaluation costs in expensive functions.
method Cost Apportioned Bayesian Optimization (CArBO) combining initial and cost-cooled phases.
result CArBO finds better hyperparameter configurations with the same cost budget.
New model considers varying costs in learning, outperforming existing methods.
problem Learning with varying costs in machine learning models.
method Introduces ε-frugal learning that considers both known and unknown costs.
result ε-frugal learners outperform learners with known costs and random sampling.
A new BO framework reduces costs by using low-fidelity data.
problem Optimizing expensive experiments with low-fidelity data.
method Developed a multi-fidelity cost-aware Bayesian optimization framework.
result Significantly outperforms state-of-the-art BO methods.
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.
This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.
problem Identifying optimal arm pairs with varying costs in multi-armed bandits.
method Introduces a framework for pairwise pure exploration with arm-specific costs, derives a lower bound, and proposes CAET algorithm.
result CAET optimizes cumulative cost and approaches the lower bound asymptotically.
CAPO optimizes LLM prompts more efficiently and cost-effectively.
problem Costly and inefficient automatic prompt optimization for LLMs.
method Integrates AutoML techniques for evolutionary optimization of instructions and few-shot examples.
result Significantly improves prompt optimization efficiency and accuracy.
CAGES optimizes expensive RL problems by efficiently learning gradients from multiple sources.
problem Optimizing expensive-to-evaluate functions in high-dimensional spaces.
method Cost-Aware Gradient Entropy Search (CAGES) for multi-fidelity Bayesian optimization.
result Significant performance improvements on synthetic and RL benchmark problems.
This paper introduces a cost-aware feature acquisition method using denoising autoencoders.
problem Optimizing feature acquisition costs in real-world scenarios.
method Incrementally asks for features based on context and uses denoising autoencoders for unknown features.
result The method efficiently acquires features at test-time in a cost- and context-aware fashion.
Self-regulation improves sequence-to-sequence learning by choosing feedback types.
problem Different types of feedback have varying costs and effects on learning.
method Self-regulation strategies decide when to ask for different types of feedback.
result Self-regulator discovers optimal cost-quality trade-off by mixing feedback types.
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.
The paper explores cost-aware spectrum access strategies in cognitive radio systems.
problem Optimizing spectrum usage in cognitive radio systems with uncertain channel states and costs.
method Discrete time model with sensing and transmission phases, considering random costs and rewards.
result The optimal policy for spectrum access has a recursive double threshold structure, and online algorithms achieve near-optimal performance.
CARROT optimizes LLM routing by choosing the cheapest and most accurate model.
problem Optimizing query routing to the most cost-effective LLM for a given task.
method CARROT uses cost and performance estimates to select the best LLM for each query.
result CARROT is minimax optimal, selecting the best LLM for any query.
Paper tackles best arm identification with cost consideration.
problem Best arm identification with cost consideration in product development.
method Derives a theoretical lower bound and proposes algorithms CTAS and CO.
result Simple algorithms can deliver near-optimal performance.
Optimizes function networks by selectively evaluating parts of the network, reducing costs.
problem Optimizing expensive function networks where full evaluations are costly.
method Knowledge gradient acquisition function for cost-aware partial evaluations.
result Outperforms existing BOFN methods and benchmarks across various problems.
FlexiBO optimizes deep neural networks by balancing cost and performance.
problem Optimizing deep neural networks for multiple objectives incurs high costs.
method Decouples and weights cost in multi-objective Bayesian optimization.
result FlexiBO discovers designs with lower hypervolume error.
This paper proposes an efficient method to acquire features for supervised learning.
problem Feature missing in training data leads to poor learning performance.
method Active feature acquisition through supervised matrix completion and cost-aware selection.
result The proposed method improves learning performance with minimal feature acquisition cost.
Develops a framework for cost-efficient Bayesian optimization with constraints.
problem Optimizing designs with minimal cost in constrained search spaces.
method Constrained multi-fidelity Bayesian optimization (CMFBO) with automatic stopping criterion.
result Minimizes overall sampling costs while ensuring feasibility.
CLQT benchmarks LLM portfolio managers by evaluating their decision-making process, not just returns.
problem Most benchmarks rank LLMs by returns, ignoring their decision-making process and potential for look-ahead leakage.
method CLQT reframes evaluation as diagnosis, using a closed-loop, cost-aware, strategy-consistent environment with a five-stage cycle.
result CLQT provides a durable map of agent competencies and limitations, separating outcome from process.
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.
TRASHFIRE improves model robustness by analyzing training rates and costs.
problem Understanding and predicting model robustness under adversarial conditions.
method Survival models, worst-case examples, cost-aware analysis.
result Deeper models offer marginal robustness gains due to inference time, not inherent robustness.
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.
Improved learning from multiple experiments with cost constraints.
problem Learning from multiple experiments with cost constraints.
method Rademacher complexity approach to show gap between training and generalization error.
result The gap between training and generalization error is O ( C − 1 / 2 ) O(C^{-1/2}) O ( C − 1/2 ) . SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.
problem Unbounded metric movement costs in bandit online convex optimization.
method SCaLE algorithm for high-dimensional dynamic quadratic hitting costs and ℓ 2 \ell_2 ℓ 2 -norm switching costs, with spectral regret analysis. result First algorithm achieving sub-linear dynamic regret without hitting cost knowledge.
Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.
problem Sequential experimental design with varying costs for changing design variables.
method Adapted batch algorithms to sequential problem, proposing cost-aware and cost-ignorant methods.
result Cost-aware algorithm outperforms tuned process-constrained algorithms in all settings considered.
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.
CADO optimizes heatmap-based solvers for cost minimization, overcoming performance limitations.
problem Heatmap-based solvers lack objective alignment for cost minimization.
method CADO uses Reinforcement Learning to optimize solution cost directly, introducing Label-Centered Reward and Hybrid Fine-Tuning.
result CADO achieves state-of-the-art performance across diverse benchmarks.
Paper uses SciPhyRL for optimizing large institutional portfolios.
problem Optimizing large institutional portfolios with cumulative costs and practical short horizons.
method Formulates a continuous-time optimization problem, reduces it to solving an HJB equation, and uses PINN for direct solution.
result Learned Gibbs policy yields substantial out-of-sample Sharpe ratio improvements.
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.
Adaptive portfolio outperforms static alternatives by 120% over 5 years.
problem Achieving strong and stable long-term performance in diversified portfolios.
method RL-BHRP: A two-level, learning-based approach that adjusts sector and stock exposures dynamically.
result Adaptive portfolio outperforms static alternatives by 120% over 5 years.
This paper proposes CSADA to make DNNs cost-sensitive.
problem Over-parameterization challenges cost-sensitive classification in DNNs.
method CSADA framework using adversarial data augmentation.
result CSADA effectively minimizes overall cost and reduces critical errors.
New algorithm reduces decision switching in dynamic environments.
problem Online learning with memory and non-stationary environments.
method Dynamic policy regret, novel ensemble approach, meta-base decomposition.
result Proves optimal dynamic policy regret for memory length, non-stationarity, and time horizon.
Adaptive replication improves stochastic function optimization.
problem Challenges in accurately estimating functions with high variance.
method Trust-region-based Bayesian optimization with adaptive replication.
result Adaptive replication substantially improves solution accuracy and efficiency.
The paper optimizes portfolios with transaction costs in a large asset universe.
problem Optimizing portfolios with transaction costs in a large asset universe.
method Mean-variance optimization with nonconvex penalty for proportional and quadratic transaction costs.
result The proposed models show satisfactory performance and highlight the importance of transaction costs.
FR-LUX optimizes portfolio management by learning cost-aware policies robust to market conditions.
problem Transaction costs and regime shifts cause failure in live trading portfolios.
method Integrates three ingredients: microstructure-consistent execution model, trade-space trust region, and explicit regime conditioning.
result Achieves top average Sharpe ratio, maintains flat cost-performance slope, and superior risk-return efficiency.
PEMC uses ML to enhance Monte Carlo simulations, reducing variance and runtime.
problem Computational inefficiency in Monte Carlo simulations for complex tasks.
method Prediction-Enhanced Monte Carlo (PEMC) framework that uses ML surrogates as predictors.
result PEMC provides unbiased evaluations with reduced variance and runtime compared to standard Monte Carlo.
A RL framework for hedging equity index options with realistic costs.
problem Dynamic hedging of equity index option exposures under transaction costs.
method Reinforcement Learning (RL) with a leak-free environment, cost-aware reward function, and stochastic actor-critic agent.
result The RL policy improves risk-adjusted performance compared to no-hedge, momentum, and volatility-targeting baselines.
Improved MCMC sampling for expensive, irregular likelihoods.
problem Bayesian inference challenges with irregular, expensive likelihoods.
method Adapt subset samplers, introduce data-driven proxies, adaptive controller.
result Improved HINTS algorithm achieves best sampling error in fixed budget.
Neural Lévy model improves risk and density forecasting for financial returns.
problem Financial returns exhibit heavy tails, volatility clustering, and jumps.
method Proposes a neural Lévy jump-diffusion framework that learns conditional drift, diffusion, jump intensity, and size distribution.
result Demonstrates improved calibration, sharper tail control, and risk reduction.
Unified framework for ranking-and-selection with multiple correct answers and non-answerable estimates
problem Fixed-precision ranking-and-selection in structured settings with non-unique answers and non-answerable estimates
method Unified framework based on answer-wise acceptance sets, restricted generalized likelihood ratio stopping, and answer-pitfall decomposition
result Unified recipe performs well across a broad range of pure-exploration problems
Accelerates Bayesian optimization of function networks with partial evaluations.
problem Optimizing expensive-to-evaluate function networks with varying node costs.
method Proposes an accelerated algorithm that uses global Monte Carlo simulations to select node-specific candidate inputs.
result Achieves up to a 16x speedup over the original p-KGFN algorithm while maintaining competitive query efficiency.
Proposes a new framework for investing that adapts to market regimes.
problem Adapting to dynamic market regimes for better investment performance.
method Wasserstein Hidden Markov Model (HMM) with transaction-cost-aware optimization.
result Significantly higher risk-adjusted performance compared to benchmarks.
New rectified flow method improves image generation and converges to optimal transport.
problem Improving computational and statistical guarantees of rectified flow for image generation.
method Introducing c-rectified flow, which projects velocity fields onto a gradient class while preserving marginals.
result Iterative c-rectified flow always converges to the optimal transport coupling under suitable assumptions.
An active learner chooses which data points to label to minimize cost and error in streaming data.
problem Efficiently labeling streaming data points with limited labeling costs.
method Formalizes the problem with a loss function, designs an algorithm with a time and cost dependent threshold, and provides upper and lower bounds.
result The algorithm achieves a worst-case upper bound of O ~ ( B 1 3 K 1 3 T 2 3 ) \widetilde{O}(B^{\frac{1}{3}} K^{\frac{1}{3}} T^{\frac{2}{3}}) O ( B 3 1 K 3 1 T 3 2 ) on the loss after T T T rounds.