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 recent measures of association, including a new coefficient.
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…
Survey of deep RL in intelligent transportation systems.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
PPI uses survey sampling methods for inference, bridging ML and statistics.
Survey of trainable activation functions in neural networks.
Survey on finite group actions on CW-complexes homotopy to spheres.
This survey outlines methods to ensure fairness in machine learning.
This paper reviews deep time-series forecasting focusing on autocorrelation modeling.
Survey of performative prediction, a machine learning setup causing distribution shifts.
The goal of this survey article is to explain and elucidate the affine structure of recent models appearing in the rough volatility literature, and show how it leads to exponential-affine transform formulas.
This paper surveys fairness notions in ML and recommends the most suitable one for real-world scenarios.
This is a short survey on finite-volume hyperbolic four-manifolds. We describe some general theorems and focus on the concrete examples that we found in the literature. The paper contains no new result.
Survey on Gaussian processes and their deep variants.
This paper surveys work on generalized Johnson homomorphisms and tools for studying them. The goal is to unite several related threads in the literature and to clarify existing results and relationships among them using Hodge theory. We survey the work of Alekseev, Kawazumi, Kuno and Naef on the Goldman--Turaev Lie bia…
Survey of self-supervised image representation learning methods.
Deep learning improves time series forecasting, outperforming other methods.
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now …
Survey on advanced gauge theory concepts.
Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this su…
Survey of yield farming protocols in DeFi.
Survey of reinforcement learning for sustainable energy challenges.
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution lear…
Survey on multiplayer bandits, highlighting theoretical gaps and future directions.
A reinforcement learning agent tries to maximize its cumulative payoff by interacting in an unknown environment. It is important for the agent to explore suboptimal actions as well as to pick actions with highest known rewards. Yet, in sensitive domains, collecting more data with exploration is not always possible, but…
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
Survey examines challenges of ML in avionic systems certification.
This paper surveys AUC maximization for big data and AI.
Survey various symmetry notions for toric varieties.
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reaso…
Survey categorizes time series anomaly detection methods.
Free actions of finite groups on spheres give rise to topological spherical space forms. The existence and classification problems for space forms have a long history in the geometry and topology of manifolds. In this article, we present a survey of some of the main results and a guide to the literature.
regvis.net offers a visual survey of regulatory visualization.
Survey on modeling event sequences through temporal processes.
Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researc…
Survey on privacy issues in deep learning and proposed solutions.
This work surveys algorithmic recourse, aiming to clarify definitions and solutions.
Survey on using large models to train smaller datasets in NLP.
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a -dimensional space into a set of blocks. In this way, the data points lie in the same block would share certain kinds of homogeneity. BNSP models can be applied to various areas, such as regression/classification …
This paper surveys recent theoretical advances in convex optimization approaches for community detection. We introduce some important theoretical techniques and results for establishing the consistency of convex community detection under various statistical models. In particular, we discuss the basic techniques based o…
This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literatur…
It is well known that certain combinations of configuration space integrals defined by Bott and Taubes produce cohomology classes of spaces of knots. The literature surrounding this important fact, however, is somewhat incomplete and lacking in detail. The aim of this paper is to fill in the gaps as well as summarize t…
The paper surveys mathematical results on filtration enlargement with financial examples.
Proposes a machine learning predictor for survey data.
Survey on statistical inference under memory constraints.
This survey aims to provide a guide to the literature on topological 4-manifolds. Foundational theorems on 4-manifolds are stated, especially in the topological category. Precise references are given, with indications of the strategies employed in the proofs. Where appropriate we give statements for manifolds of all di…
Survey of three geometric frameworks for action-dependent field theories.