arXiv research

CARROT optimizes LLM routing by choosing the cheapest and most accurate model.

CLQT benchmarks LLM portfolio managers by evaluating their decision-making process, not just returns.

Optimizes function networks by selectively evaluating parts of the network, reducing costs.

The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …

CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.

CAGES optimizes expensive RL problems by efficiently learning gradients from multiple sources.

Bayesian optimization with cost-awareness using Gittins index.

This work improves cost-aware Bayesian optimization by introducing Pareto-efficient acquisition functions.

Develops a framework for cost-efficient Bayesian optimization with constraints.

Cost-aware BO minimizes function evaluations with varying costs.

A new BO framework reduces costs by using low-fidelity data.

Cost-aware SBI reduces expensive simulations in complex models.

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

CAPO optimizes LLM prompts more efficiently and cost-effectively.

New model considers varying costs in learning, outperforming existing methods.

Paper tackles best arm identification with cost consideration.

In this paper, we propose a cost-aware cascading bandits model, a new variant of multi-armed ban- dits with cascading feedback, by considering the random cost of pulling arms. In each step, the learning agent chooses an ordered list of items and examines them sequentially, until certain stopping condition is satisfied.…

Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy …

TRASHFIRE improves model robustness by analyzing training rates and costs.

In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features …

Adaptive portfolio outperforms static alternatives by 120% over 5 years.

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

CADO optimizes heatmap-based solvers for cost minimization, overcoming performance limitations.

Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.

The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optim…

Proposes a new framework for investing that adapts to market regimes.

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost…

Resource-efficient oblique trees reduce neural signal classification costs.

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the …

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…

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…

SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.

Neural Lévy model improves risk and density forecasting for financial returns.

A RL framework for hedging equity index options with realistic costs.

Adaptive replication improves stochastic function optimization.

The paper optimizes portfolios with transaction costs in a large asset universe.

Improved MCMC sampling for expensive, irregular likelihoods.

Unified framework for ranking-and-selection with multiple correct answers and non-answerable estimates

Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature acquisition usually involves special devices or complex process, it is expensive to acquire all feature values for the whole dataset. On the…

This paper proposes CSADA to make DNNs cost-sensitive.

Accelerates Bayesian optimization of function networks with partial evaluations.

Paper uses SciPhyRL for optimizing large institutional portfolios.

Machine learning predicts Bitcoin returns but trading performance drops with costs.

New algorithm reduces decision switching in dynamic environments.

FR-LUX optimizes portfolio management by learning cost-aware policies robust to market conditions.

PEMC uses ML to enhance Monte Carlo simulations, reducing variance and runtime.

An active learner chooses which data points to label to minimize cost and error in streaming data.

YAHPO Gym introduces a new benchmark for evaluating hyperparameter optimization methods.