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.
Modeling horse race betting odds with Ornstein-Uhlenbeck process.
We find a remarkable agreement between the statistics of a randomly divided interval and the observed statistical patterns and distributions found in horse racing betting markets. We compare the distribution of implied winning odds, the average true winning probabilities, the implied odds conditional on a win, and the …
Investigates sports betting strategies using modern portfolio theory and Kelly criterion.
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high rel…
A new ranking model with dynamic covariates improves statistical analysis.
This paper is part of an ongoing investigation of "pragmatic information", defined in Weinberger (2002) as "the amount of information actually used in making a decision". Because a study of information rates led to the Noiseless and Noisy Coding Theorems, two of the most important results of Shannon's theory, we begin …
Study on weekly momentum strategies in Chinese stocks, comparing various risk metrics.
BBE simulates sports betting exchanges for data generation.
China and EU race to develop hydrogen for energy transition.
BBE simulates betting exchanges to generate synthetic data for AI research.
We deal with the \textit{selective classification} problem (supervised-learning problem with a rejection option), where we want to achieve the best performance at a certain level of coverage of the data. We transform the original -class classification problem to -class where the -th class represents th…
Identifying homogeneous subgroups of variables can be challenging in high dimensional data analysis with highly correlated predictors. We propose a new method called Hexagonal Operator for Regression with Shrinkage and Equality Selection, HORSES for short, that simultaneously selects positively correlated variables and…
Study improves forecasting in betting markets using novel neural networks.
Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that r…
Develops a method to quantify racial bias in law enforcement systems.
Reduces gender classification bias by learning race-invariant face representations.
Develops a learning model predictive controller for competitive racing.
Deep RL drone trained to compete against classical path planning in drone racing.
Quantum kernels show no advantage in stock return prediction, but differ in stability metrics.
Study shows data breaches cause significant financial losses for firms, especially in health sector.
DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.
To answer questions about racial inequality and fairness, we often need a way to infer race and ethnicity from names. One way to infer race and ethnicity from names is by relying on the Census Bureau's list of popular last names. The list, however, suffers from at least three limitations: 1. it only contains last names…
Black women and white men have the highest income disparity in the U.S.
Improved race prediction model outperforms existing methods.
RankNet forecasts car racing positions with improved accuracy and stability.
Improved surname geocoding and name supplements enhance race imputation accuracy.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
Lognormal distribution used for predicting team rankings in an orienteering relay race.
Over the last couple of years, deep learning and especially convolutional neural networks have become one of the work horses of computer vision. One limiting factor for the applicability of supervised deep learning to more areas is the need for large, manually labeled datasets. In this paper we propose an easy to imple…
A new model predicts race places using changeover-times and log-normal distributions.
We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event. LDR explains non-monotonic covariate effects by racing a potentially infinite number of sub-risks, and consequently relaxes the ubiqu…
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
This work improves autonomous racing by creating diverse opponents and adapting risk.
Study compares SPG and PPO for racing games, finding SPG more stable with weighted actions.
This short note is intended as a "Letter to the Editor" Perspective in order that it serves as a contribution, in view of reaching the physics community caring about rare events and scaling laws and unexpected findings, on a domain of wide interest: sport and money. It is apparent from the data reported and discussed b…
In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would…
Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to …
Study removes bias from chest X-ray embeddings using orthogonalization.
New approach to counterfactual reasoning avoids demographic interventions.
Two of the most popular modelling paradigms in computer vision are feed-forward neural networks (FFNs) and probabilistic graphical models (GMs). Various connections between the two have been studied in recent works, such as e.g. expressing mean-field based inference in a GM as an FFN. This paper establishes a new conne…
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framew…
In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategies to optimise control policies for individual stages of the process might not be applicable, and in…
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued…
"Overlearning" means that a model trained for a seemingly simple objective implicitly learns to recognize attributes and concepts that are (1) not part of the learning objective, and (2) sensitive from a privacy or bias perspective. For example, a binary gender classifier of facial images also learns to recognize races…
Paper debiases multiple word embedding biases simultaneously.
Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs---imperceptibly but adversarially perturbed versions of natural inputs. We have witnessed an arms race between defenders who attempt to train robust networks and attackers who try to constr…
fairadapt uses causal inference to mitigate algorithmic bias in data pre-processing.
Study analyzes financial distributions and inequality in professional cycling teams.