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 develops a theory for speculative decoding acceptance criteria.
New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.
Benchmarking deep time series models for equity portfolios
The principle of peer review is central to the evaluation of research, by ensuring that only high-quality items are funded or published. But peer review has also received criticism, as the selection of reviewers may introduce biases in the system. In 2014, the organizers of the ``Neural Information Processing Systems\r…
This work compares data reduction criteria for online Gaussian Processes.
We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general eligible assets, including defaultable bonds. Since the payoff of these assets is not…
The paper studies optimal investment using acceptability indices to maximize portfolio performance.
This paper examines fairness and arbitrariness in bias mitigation methods.
Proposes a new stability measure for model fitting on similar feature data sets.
Algorithmic fairness involves expressing notions such as equity, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise. Most work in the literature to date has focused on classification problems where the prediction is categorical, such as accepting or rejecting a loan applica…
We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu…
New star-shaped acceptability indexes generalize existing methods.
Studies acceptable bundles on a partially punctured polydisk.
Study on acceptable bundles on a punctured disk.
Simple conditions for comonotonic additive risk measures from acceptance sets.
Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econ…
In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …
The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimat…
Cactus improves auto-regressive decoding speed without sacrificing quality.
Review and compare sorting model selection methods for preference disaggregation.
New method combines neural networks with Monte Carlo for complex system reliability.
Proposes new deviation measures using Minkowski gauges.
Improves algorithmic recourse to guide towards both acceptance and improvement.
Paper predicts embryo implantation probability from IVF time-lapse imaging.
Estimates boundaries for acceptable bilateral gamma risk in financial markets.
Study reveals bias in machine learning conference reviews.
Introduces Star-Shaped deviation measures for risk analysis.
We consider a trader who wants to direct his portfolio towards a set of acceptable wealths given by a convex risk measure. We propose a black-box algorithm, whose inputs are the joint law of stock prices and the convex risk measure, and whose outputs are the numerical values of initial capital requirement and the funct…
Study financial contracts pricing in markets with nonproportional costs and constraints.
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
We establish dual representations for systemic risk measures based on acceptance sets in a general setting. We deal with systemic risk measures of both "first allocate, then aggregate" and "first aggregate, then allocate" type. In both cases, we provide a detailed analysis of the corresponding systemic acceptance sets …
INNs improve acceptance rates in electron spectra analysis.
The theory of acceptance sets and their associated risk measures plays a key role in the design of capital adequacy tests. The objective of this paper is to investigate, in the context of bounded financial positions, the class of surplus-invariant acceptance sets. These are characterized by the fact that acceptability …
New models improve machine learning accuracy and transparency in finance.
The most commonly accepted model for investors' preferences is expected utility theory. More recently, other theories have emerged and pose new challenges to mathematics. The present paper treats preferences of cumulative prospect theory (CPT), where an "S-shaped" utility function is considered (i.e. convex up to a cer…
Research examines motivations and factors influencing retailers' payment method choices.
Monetary risk measures are usually interpreted as the smallest amount of external capital that must be added to a financial position to make it acceptable. We propose a new concept: intrinsic risk measures and argue that this approach provides a direct path from unacceptable positions towards the acceptance set. Intrin…
Indices of acceptability are well suited to frame the axiomatic features of many performance measures, associated to terminal random cash flows.We extend this notion to classes of càdlàg processes modelling cash flows over a fixed investment horizon.We provide a representation result for bounded paths. We suggest an ac…
Generation of pseudorandom numbers from different probability distributions has been studied extensively in the Monte Carlo simulation literature. Two standard generation techniques are the acceptance-rejection and inverse transformation methods. An alternative approach to Monte Carlo simulation is the quasi-Monte Carl…
Paper extends ranking metrics theory for financial positions.
Newtonian dynamical systems which accept the normal shift on an arbitrary Riemannian manifold are considered. For them the determinating equations making the weak normality condition are derived. The expansion for the algebra of tensor fields is constructed.
Paper extends ranking metrics theory for financial positions.
Proposes a method to estimate acceptance regions for many classes, including new ones.
The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…
Improved sampling for Bayesian neural networks reduces vanishing acceptance rates and increases predictive accuracy.
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differ…