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.
Survey of methods for learning from observation without requiring expert actions.
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
Survey of algorithms to correct past mistakes in prediction.
The paper provides an algorithm for the risk estimation when a company selects an outsourcing service provider for innovation product. Calculations are based on expert surveys conducted among customers and among providers of outsourcing. The surveys assessed the degree of materiality of species at risk.
We survey Mirzakhani's work relating to Riemann surfaces, which spans about 20 papers. We target the discussion at a broad audience of non-experts.
Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to…
This article is a survey article on geometric group theory from the point of view of a non-expert who likes geometric group theory and uses it in his own research. The sections are: classical examples, basics about quasiisometry,properties and invariants of groups invariant under quasiisometry, rigidity, hyperbolic spa…
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets…
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological…
This article is a survey on the braid groups, the Artin groups, and the Garside groups. It is a presentation, accessible to non-experts, of various topological and algebraic aspects of these groups. It is also a report on three points of the theory: the faithful linear representations, the cohomology, and the geometric…
RL improves combinatorial optimization by automating heuristic search.
Deep learning automates biofouling detection in ship hull images.
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
This is a survey paper focusing on the interplay between the curvature and topology of a Riemannian manifold. The first part of the paper provides a background discussion, aimed at non-experts, of Hopf's pinching problem and the Sphere Theorem. In the second part, we sketch the proof of the Differentiable Sphere Theore…
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheles…
This paper surveys imitation learning methods and challenges.
Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, whi…
Survey of neurosymbolic AI methods for reasoning over knowledge graphs.
We investigate a class of hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function. Suppose the true response y follows an exponential family regression model with mea…
The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of expert…
Sommelier recommends machine learning algorithms for datasets based on scholarly knowledge.
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite im…
Survey on making reinforcement learning models more understandable.
Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the label…
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance ratin…
Paper proposes government indemnification for AI risks to solve judgment-proof problem.
With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years…
Mirzakhani's thesis counts geodesics on hyperbolic surfaces, finding a specific asymptotic formula.
Multi-expert L2D underfits more severely, requiring new methods.
TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
Overview of integrable systems with symmetries, focusing on toric and semitoric systems.
The purpose of this paper is two-fold: we systematically introduce the notion of Cheeger deformations on fiber bundles with compact structure groups, and recover in a very simple and unified fashion several results that either already appear in the literature or are known by experts, though are not explicitly written e…
Tree-based synthesis improves forecast accuracy in GDP and inflation.
We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…
Improved time series forecasting with expert loss integration.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
Quantum computing poses a threat to Bitcoin and Ethereum, but only to spending and not mining.
NAMEx merges experts using Nash bargaining for improved performance.
Expert augmentation improves hybrid model generalization.
New method calibrates Gaussian product experts for better predictions.
Meta-algorithm optimizes nonstochastic bandits with infinitely many experts.
New algorithm reduces expert prediction regret for two experts.
The article improves prediction by aggregating Kalman recursions online.
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
Introduces data ethics for mathematicians, covering background, open data, and privacy.
Improved regret bounds for bandits with fixed expert advice using information theory.
Bayesian models combine experts with a flexible gating mechanism for complex data.