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.
Bayesian neural networks show good correlation between out-of-sample performance and Bayesian evidence.
Evidence Networks simplify Bayesian model comparison for complex models.
We consider the game-theoretic scenario of testing the performance of Forecaster by Sceptic who gambles against the forecasts. Sceptic's current capital is interpreted as the amount of evidence he has found against Forecaster. Reporting the maximum of Sceptic's capital so far exaggerates the evidence. We characterize t…
Paper uses evidence theory to improve stock price forecasting accuracy.
OPAA estimates probability densities using functional analysis.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
We provide evidence that cumulative distributions of absolute normalized returns for the American companies with the highest market capitalization, uncover a critical behavior for different time scales . Such cumulative distributions, in accordance with a variety of complex --and financial-- systems, can be m…
Stochastic Bayesian Neural Network improves scalability and performance.
We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination. The probabilistic output…
fiBAG integrates multiplatform genomic data to identify disease markers.
Martingale Doppelgänger-Eval benchmarks VLMs on candlestick evidence vs. trend extrapolation
Empirical study finds variance swap rate is affine in spot variance for S&P500 data.
This paper introduces a method to estimate log-likelihood in VAE models.
The paper explores how to handle uncertain evidence in probabilistic models.
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract info…
Evidence acquisition costs influence disclosure behavior and preference.
The natural gradient of ELBO vanishes in unconstrained optimization, simplifying learning.
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical ou…
Formula for arborescent link tails using theta functions.
This article introduces a framework to estimate the value of evidence-based decision making.
The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectifier and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discu…
Loosely speaking, the Volume Conjecture states that the limit of the n-th colored Jones polynomial of a hyperbolic knot, evaluated at the primitive complex n-th root of unity is a sequence of complex numbers that grows exponentially. Moreover, the exponential growth rate is proportional to the hyperbolic volume of the …
Two sets of high quality income data are analysed in detail, one set from the UK, one from the USA. It is firstly demonstrated that both a log-normal distribution and a Boltzmann distribution can give very accurate fits to both these data sets. The absence of a power tail in the US data set is then discussed. Taken in …
We prove that square integrable holomorphic functions (with respect to a plurisubharmonic weight) can be extended in a square integrable manner from certain singular hypersurfaces (which include uniformly flat, normal crossing divisors) to entire functions in affine space. This provides evidence for a conjecture regard…
We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optim…
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samp…
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
Method estimates Bayesian evidence from posterior samples using normalizing flows.
Bayesian linear networks reveal optimal depth and width trade-offs.
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.
In the modern era, abundant information is easily accessible from various sources, however only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting…
Study assesses neural nets for optimization problems, highlighting SiLU's effectiveness.
The ropelength of a knot is the quotient of its length by its thickness. We consider a family of energy functions for knots, depending on a power p, which approach ropelength as p increases. We describe a numerically computed trefoil knot which seems to be a local minimum for ropelength; there are nearby critical point…
By studying all the trades and best bids/asks of ultra high frequency snapshots recorded from the order books of a basket of 10 futures assets, we bring qualitative empirical evidence that the impact of a single trade depends on the intertrade time lags. We find that when the trading rate becomes faster, the return var…
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…
We conjecture a formula for the refined Vafa-Witten invariants of any smooth surface satisfying and . The unrefined formula corrects a proposal by Labastida-Lozano and involves unexpected algebraic expressions in modular functions. We prove that our formula satisfi…
We use Morse theory of the Yang-Mills functional to compute the Betti numbers of the moduli stack of flat U(3)-bundles over a compact nonorientable surface. Our result establishes the antiperfection conjecture of Ho-Liu, and provides evidence for the equivariant formality conjecture of the author.
MINIMALIST maximizes mutual information for likelihood estimation from simulated data.
LPF provides formal guarantees for aggregating multi-evidence in probabilistic tasks.
We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The…
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
It is well known that there exist statistical and structural differences between the stock markets of developed and emerging countries. In this work, we present an analysis of the variations and autocorrelations of the Mexican Stock Market index (IPC) for different periods of its historical daily data, showing evidence…
Neural networks with learned biases can approximate any function.
Deep Evidence Regression improves credit risk prediction uncertainty.
The study proposes a conjecture about the monodromy group of singular hyperbolic metrics and provides evidence and confirmations.
Study aggregation of statistical evidence under unknown dependence using group-invariance.
Study on colored Jones polynomial and link complements.