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.
New method identifies common cause in causal insufficiency, revealing complex phase transitions.
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
NMF and PCC linked, improving data denoising and feature stability.
Optimizes portfolios by identifying causal drivers of diversification.
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been proposed. However, most of these deal only with purely directed causal relationships and cannot det…
New causal versions of MaxEnt and PIR avoid paradoxical probability updates.
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversa…
Extract common latent factors from graphs for better representation learning.
Bayesian optimization improves molecule design by addressing three pitfalls.
A new method identifies causal direction using dense functional classes.
Framework identifies causal direction from single data setting.
Paper simplifies calculating causation probabilities and ranks root causes.
Develops SCMs for latent selection to simplify causal analysis.
Jointly models cause-of-death mortality rates across multiple countries and genders.
New method falsifies causal graphs using outlier events.
CROC identifies the earliest-changing stream as the root cause in multi-stream data.
We consider linear models where potential causes are correlated with one target quantity and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression c…
New insights into how to inspect and learn from multi-stage processes and AI reasoning.
New method identifies causes in time series with latent variables.
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment and outcome cannot be blocked by other measurements, one possibility is to use an instrumental variable. In principle, it is possible under some…
Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary ha…
Spectral Independence Criterion helps infer cause-effect relationships in time series.
CLOUD method detects causal relationships in various data types without latent variable assumptions.
New method learns graph structure with hidden causes from observational data.
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression. Based on this theory, we develop Bi…
New formula for portfolio risk management using conditional PDEs.
Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may…
Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscri…
Operational risk is the risk relative to monetary losses caused by failures of bank internal processes due to heterogeneous causes. A dynamical model including both spontaneous generation of losses and generation via interactions between different processes is presented; the efforts made by the bank to avoid the occurr…
Proposes CSG model to separate semantic and variation factors for OOD prediction.
The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize the…
Reinterprets Granger causality with causal Bayesian networks and Reichenbach's principles.
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presu…
Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challeng…
New method improves cause-effect identification using neural networks.
Paper proposes methods to reduce bias and variance in recommender systems.
For common people, in contrast to brokers, bankers, and those who play on rising and falling prices of stocks, the stock market law is based on the simple fact that the depositors aim for financial profit at any given concrete stage. The common depositor cannot cause any significant variations in prices. This concept s…
A key problem in research on adversarial examples is that vulnerability to adversarial examples is usually measured by running attack algorithms. Because the attack algorithms are not optimal, the attack algorithms are prone to overestimating the size of perturbation needed to fool the target model. In other words, the…
Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is a mystery [#zhang2016understanding]. To find a potential mechanism, we focus on the study of implicit biases underlying the training process of DNNs. In this work, for both real and synthetic datasets, we empirically find that a…
The so called "globalization" process (i.e. the inexorable integration of markets, currencies, nation-states, technologies and the intensification of consciousness of the world as a whole) has a behavior exactly equivalent to a system that is tending to a maximum entropy state. This globalization process obeys a collec…
Generative models learn from unlabeled videos via object segmentation and scene modeling.
Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking …
This document provides a tutorial description of the use of the MDL principle in complex graph analysis. We give a brief summary of the preliminary subjects, and describe the basic principle, using the example of analysing the size of the largest clique in a graph. We also provide a discussion of how to interpret the r…
ECGDetect uses deep learning to detect ischemia in heart ECGs.
Local convolutions bias neural networks towards high-frequency adversarial examples.
A novel circuit motif uses sister cells for inference with correlated priors.
Mic2Mic reduces microphone variability for speech systems.
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variable . To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that firs…