A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.
RACER optimizes LLM-as-judge accuracy with dynamic reasoning selection.
The paper extends cost-efficiency analysis to incomplete markets.
Abstract and counterexamples show limitations of cost-efficiency in incomplete markets.
Intertemporal model for cost-efficient consumption using copulas.
CAPO optimizes LLM prompts more efficiently and cost-effectively.
A cost-efficient method for hyperparameter tuning using multi-fidelity Bayesian optimization.
EnEMF uses Epanechnikov kernel for high-dimensional filtering, improving accuracy and robustness.
New algorithm reduces online hyperparameter optimization costs.
Cer-Eval saves LLM evaluation costs while maintaining accuracy.
Cost-efficient distributed learning via combinatorial bandits.
Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment as…
We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates th…
Paper develops active learning for clustering unknown pairwise similarities.
Optimizes user marketing campaigns to balance cost and effectiveness.
Hybrid model uses GNNs and pathfinding to optimize portfolio rebalancing costs.
The pricing, hedging, optimal exercise and optimal cancellation of game or Israeli options are considered in a multi-currency model with proportional transaction costs. Efficient constructions for optimal hedging, cancellation and exercise strategies are presented, together with numerical examples, as well as probabili…
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection vi…
New optimal transport divergences derived from scoring functions.
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system provid…
Simplifies decision-making during medical exams with cost-efficient feature acquisition.
IPO Finance Agent extends Finance Agent v2 for SpaceX S-1 filings, improving accuracy and cost-efficiency.
Efficiently trains BERT on academic GPUs in 12 days.
Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant diagnostic method for malaria. However, it is time consuming and prone to human error…
Study cost-effective fairness audits with partial feedback, improving over random exploration.
AI threatens financial stability through misuse and stealth adoption.
Drug repositioning is an attractive cost-efficient strategy for the development of treatments for human diseases. Here, we propose an interpretable model that learns disease self-representations for drug repositioning. Our self-representation model represents each disease as a linear combination of a few other diseases…
Survey of universal portfolio techniques for minimizing investment regret.
Proper regularization is critical for speeding up training, improving generalization performance, and learning compact models that are cost efficient. We propose and analyze regularized gradient descent algorithms for learning shallow neural networks. Our framework is general and covers weight-sharing (convolutional ne…
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This p…
A new method improves super learner validation efficiency.
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic or simple empirical prediction models. In this paper, we evaluate…
We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we…
Riemannian stochastic gradient descent approximates a diffusion process called Riemannian stochastic modified flow.
IPO Finance Agent evaluates LLMs on SpaceX IPO due diligence, surpassing Finance Agent v2.
We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…
We detect lookahead bias in LLM forecasts using a novel statistical method.
State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has address…
Develops a framework for cost-efficient Bayesian optimization with constraints.
Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment.…
The problem-solving in automated theorem proving (ATP) can be interpreted as a search problem where the prover constructs a proof tree step by step. In this paper, we propose a deep reinforcement learning algorithm for proof search in intuitionistic propositional logic. The most significant challenge in the application…
Model for open, decentralized network with task load balancing.
Game theory models how agents trade in a risky asset considering price impact and a common signal.
New method optimizes costly evaluations in Bayesian optimization.
Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability. On par with data-intensive applications, the sheer size of linear regression problems creates an ever growing demand for quick and cost efficient solvers. Fortunately…
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking pur…