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.
Paper improves compressed sensing with prior probability information.
In this paper, we introduce a numeraire-free and original probability based framework for financial markets. We reformulate or characterize fair markets, the optional decomposition theorem, superhedging, attainable claims and complete markets in terms of martingale deflators, present a recent result of Kramkov and Scha…
In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-prob…
This paper analyzes SHAP values using Fourier expansions for model interpretability.
For a given level of accuracy in option prices, the paper considers the problem of deciding when exactly, as one or more of the pricing parameters change, a barrier option degenerates into a simpler type of option. This problem is meaningful in the real world where option prices are always determined within a certain l…
Adaptive PINNs improve accuracy by adding points where solutions are uncertain.
In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric techniq…
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear pen…
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performan…
The study predicts pass completion probability in NFL games.
Adaptive learning rates improve FTPL's BOBW guarantees in bandit problems.
C-Mixup improves generalization in regression tasks by adjusting label similarity.
This paper deals with a high-order accurate implicit finite-difference approach to the pricing of barrier options. In this way various types of barrier options are priced, including barrier options paying rebates, and options on dividend-paying-stocks. Moreover, the barriers may be monitored either continuously or disc…
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an additional stopping probability based on the previously seen information. This mechanism is end-to-end trainable and derives its stopping decision s…
The study classifies policy announcements' impact on stock market volatility.
A method for efficient CV estimates in Bayesian hierarchical models.
In this paper, we propose a novel progressive parameter pruning method for Convolutional Neural Network acceleration, named Structured Probabilistic Pruning (SPP), which effectively prunes weights of convolutional layers in a probabilistic manner. Unlike existing deterministic pruning approaches, where unimportant weig…
There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distribu…
The MSPI predicts market stress with machine learning.
Cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of D binary variables are active. Maximum a posteriori (MAP) inference for cardinality potential models is well-understood, with efficient computations taking O(DlogD) time. Yet efficient marginalizat…
Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Th…
One of the primary concerns of product quality control in the automotive industry is an automated detection of defects of small sizes on specular car body surfaces. A new statistical learning approach is presented for surface finish defect detection based on spline smoothing method for feature extraction and -neares…
The paper proposes an efficient method for estimating ATEs using adaptive experiments.
Mitigates spurious correlations without bias labels.
New method calibrates uncertainty estimates for image classifiers without labeled data.
A new method for semi-supervised learning with missing data using GMM and margin confidence.
The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.
The article compares neural networks and logistic regression for credit scoring and introduces a new probability calibration technique.
A new neural network reduces high-dimensional time-series data for faster classification.
Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain. We cast domain adaptation as the problem of finding stable labels for target examples. A new definition of label stability is proposed, …
Discriminative neural networks address class imbalance in coronary heart disease risk analysis.
We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop thre…
New method reconstructs past foehn occurrences using unsupervised and supervised learning.
Persistent entropy detects phase transitions in complex systems.
In machine learning and data mining, Cluster analysis is one of the most widely used unsupervised learning technique. Philosophy of this algorithm is to find similar data items and group them together based on any distance function in multidimensional space. These methods are suitable for finding groups of data that be…
Framework prevents deep learning models from memorizing noisy labels.
TeLeS improves ASR confidence estimation by considering temporal alignment and lexical errors.
Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non-lineality. To tackle this problem, we investigate the expressive power of g…
This paper presents a method to efficiently estimate rare event probabilities using a combination of high and low-fidelity models.
Proposes a method for selecting important variables in high-dimensional data.
A new Weyl prior is proposed for Bayesian statistics, offering a more canonical choice for parameter α.
Improves AI-prior reliability for Bayesian inference.
Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors. However, objective priors such as the Jeffreys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques fo…
Improves GCNNs with node transition probabilities and DropNode regularization.
While Bayesian methods are praised for their ability to incorporate useful prior knowledge, in practice, convenient priors that allow for computationally cheap or tractable inference are commonly used. In this paper, we investigate the following question: for a given model, is it possible to compute an inference result…
Researchers derive exact priors for finite Bayesian neural networks.
PRCD-MAP learns to trust imperfect priors in causal discovery, improving accuracy and robustness.
Bayesian metalearning improves performance in linear bandits with misspecified priors.