arXiv research

New algorithm for multi-fidelity bandits reduces costs and improves regret.

Cost-aware SBI reduces expensive simulations in complex models.

Two major financial market complexities are transaction costs and uncertain volatility, and we analyze their joint impact on the problem of portfolio optimization. When volatility is constant, the transaction costs optimal investment problem has a long history, especially in the use of asymptotic approximations when th…

Optimal multi-fidelity best-arm identification reduces cost with better accuracy.

Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed. Our work is motivated by risk prediction problems in healthcare. Cost-structures in domains such as healthcare are quite…

Study reduces NAS search cost by generating multiple complex architectures in one shot.

Deep neural network predicts health costs better than traditional models.

Certified algorithms optimize functions with varying costs, providing error bounds.

Cer-Eval saves LLM evaluation costs while maintaining accuracy.

This survey is an introduction to asymptotic methods for portfolio-choice problems with small transaction costs. We outline how to derive the corresponding dynamic programming equations and simplify them in the small-cost limit. This allows to obtain explicit solutions in a wide range of settings, which we illustrate f…

We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gat…

New protocol reduces communication costs for heterogeneous bandits over complex networks.

Efficient algorithm for unknown linear systems with convex costs.

We present a dynamic model selection approach for resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and predict…

New algorithms reduce communication costs in collaborative learning.

Develops an actor-critic algorithm for risk-sensitive Markov decision processes.

Budgeted deferral framework reduces expert query costs in machine learning.

Proposes an angle-based framework for multicategory cost-sensitive classification.

We analyze the sample complexity of learning from multiple experiments where the experimenter has a total budget for obtaining samples. In this problem, the learner should choose a hypothesis that performs well with respect to multiple experiments, and their related data distributions. Each collected sample is associat…

New method optimizes costly evaluations in Bayesian optimization.

Improved Gaussian process inference for spatio-temporal data.

In this paper, we propose an effective THresholding method based on ORder Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure, uses order statistic to find an optimal threshold for…

We develop a fundamentally different stochastic dynamic programming model of trading costs. Built on a strong theoretical foundation, our model provides insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their …

New algorithm reduces RL complexity with low switching costs.

A deep reinforcement learning method for cost-sensitive portfolio selection.

SideNet adapts MainNet's complexity based on input, reducing compute cost.

New approach for uninformed investors to optimize execution costs.

Study shows hard sample complexity for learning optimal policies in stochastic shortest path problems.

Generative models speed up complex system simulations.

In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complex…

Sparsity helps reduce diffusion model costs.

A new algorithm reduces memory and computational needs for reinforcement learning.

This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.

Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.

Adaptive Multilevel Monte Carlo improves probability estimation for complex random variables.

The paper analyzes the statistical cost of tuning kernel hyperparameters in robust regression.

This paper applies quantum theory to cost accounting, focusing on WIP valuation.

Optimized portfolio turnover strategies enhance wealth and reduce costs.

Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these analyses help policy analysts and economists determine coverage, inform policy, and guide resource allocation, they are statistically challenging for several reasons. Cost and effectiveness are correlated and follow com…

Research funding agencies routinely use a proportion of their total revenues to support internal administration and marketing costs. The ratio of administration to total costs, referred to as the administration ratio, is highly variable and within any single fund depends on many factors including the number and average…

FAB-COST improves cold-start recommendation accuracy with less data.

Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper we use a genetic programming algorithm to derive exp…

Investigates how rebalancing frequency and transaction costs affect log-optimal portfolios.

The paper analyzes the efficiency of unlearning methods and establishes bounds for minimax computation times.

A new method reduces the complexity of decentralized optimization.

Deep architecture such as hierarchical semi-Markov models is an important class of models for nested sequential data. Current exact inference schemes either cost cubic time in sequence length, or exponential time in model depth. These costs are prohibitive for large-scale problems with arbitrary length and depth. In th…

Survey on AL strategies for cost-effective annotation in classification.

LMM predicts healthcare costs and risks with improved accuracy.