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.
The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.
Introduces new performance criteria for investment under distorted probabilities.
New algorithms minimize risk in MNL bandits, achieving near-optimal performance.
Extends ML fairness to handle minority groups over time.
New method for dynamic valuation in markets with random endowments.
This paper optimizes stock portfolios considering ESG criteria using Bayesian optimization.
The study examines Nash equilibria in utility maximization games with multiplicative performance criteria.
Improved recommendations using latent embeddings from user reviews.
Selective regression allows abstention to improve fairness criteria.
This work explores alternative learning criteria beyond traditional risk.
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms. However , in many situations, few or no data are labeled. This calls for alternative crite…
Efficiency criteria improve conformal predictors' performance.
In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for risk management, and are important in domains such as finance and process control. We propose both TD(0) and LSTD(lambda) variants with linear…
The sBIC outperforms other model selection criteria in LDA topic modeling.
SPX optimizes multiple graph drawing metrics for better readability.
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairne…
This work compares lattice-free and lattice-based training criteria for LVCSR.
In a Markovian stochastic volatility model, we consider financial agents whose investment criteria are modelled by forward exponential performance processes. The problem of contingent claim indifference valuation is first addressed and a number of properties are proved and discussed. Special attention is given to the c…
Proposes a multi-criteria AutoML system to optimize additional criteria like fairness and interpretability.
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…
A new method for multi-criteria recommender systems using graph attention networks.
We present turnpike-type results for the risk tolerance function in an incomplete market setting under time-monotone forward performance criteria. We show that, contrary to the classical case, the temporal and spatial limits do not coincide. We also show that they depend directly on the left- and right-end of the suppo…
New fairness criteria for algorithmic recourse actions that consider causal relationships.
In this paper we consider the problem of Gaussian process classifier (GPC) model selection with different Leave-One-Out (LOO) Cross Validation (CV) based optimization criteria and provide a practical algorithm using LOO predictive distributions with such criteria to select hyperparameters. Apart from the standard avera…
The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. ID3, C4.5 and CART are classical decision tree algorithms and the split criteria they…
The study evaluates different parameter selection methods for Gaussian process interpolation.
The paper introduces a new model selection criterion for various time series models.
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined with real-time fMRI to propose a novel framework which turns on its head the conventional functional neuroimaging approach. This closed-loop…
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amount of regularization, often determined by tuning parameters, is integral to achieving good performance. One can choose the …
Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning algorithm. Inspired by various philosophies about what constitutes a good criteria, different algorithms perform well on different datasets. Th…
TOPNet integrates task-based evaluation into machine learning models.
New method for sorting with interacting criteria using value functions and convex programming.
The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.
This paper compares two clustering evaluation metrics, revealing their differences and properties.
New pruning methods improve dynamic sparse training performance.
We analyze a family of portfolio management problems under relative performance criteria, for fund managers having CARA or CRRA utilities and trading in a common investment horizon in log-normal markets. We construct explicit constant equilibrium strategies for both the finite population games and the corresponding mea…
We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the…
Unified perspective unites Bayesian optimization and active learning for efficient goal-oriented optimization.
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input variables X. We consider Bayesian models for this task. In particular, we examine a special class of models, which we call Bayesian regression…
Develops fair clinical risk prediction models using counterfactual reasoning.
Automated model assesses online health info quality using machine learning.
We give some general criteria of being a homeomorphism for continuous mappings of topological manifolds, as well as criteria of being a diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we apply these criteria to the problems arising in two- and three-dimensional grid generation.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
New method finds optimal hyperparameters for multiple tasks and criteria.
We combine forward investment performance processes and ambiguity averse portfolio selection. We introduce the notion of robust forward criteria which addresses the issues of ambiguity in model specification and in preferences and investment horizon specification. It describes the evolution of time-consistent ambiguity…
New algorithms improve experimental design efficiency and approximation quality.
New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.