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.
Quriosity analyzes curiosity-driven questions from diverse sources.
Framework identifies causal factors of climate change using correlations and machine learning.
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed a…
TCR simplifies complex models into interpretable causal factors.
Proposes PEID for analyzing synergistic causation in complex systems.
New causal analysis reconciles predictive and statistical fairness.
New pipeline for causal research in psychology and social sciences.
While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence. Designing an efficient causality test, that may be carried out in the absence of restricti…
New benchmarks show LLMs struggle with causal discovery.
This report reviews the Edinburgh tram project's risk management. Projects frequently overrun their cost and timelines and fall short on intended benefits. Cost, schedule, and benefit risk of projects need to be carefully considered to avoid this. The report describes and evaluates risk assessment and management for th…
The paper addresses bias amplification in prediction and decision-making using causal analysis.
Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modelling routes on maps to approving loan predictions. In the former, we may require the prediction model to respect the presence of phy…
The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves …
This report was commissioned by the Commission of Inquiry Respecting the Muskrat Falls Project to provide the national and international context in which the Muskrat Falls Project took place. The Commission asked for the report to cover three specific topics of questions: (1) What is the national and international cont…
We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit.…
We describe the use of Non-Negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) algorithms to perform topic mining and labelling applied to retail customer communications in attempt to characterize the subject of customers inquiries. In this paper we compare both algorithms in the topic mining perf…
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation. Such modification provides the fundamental interventional operation that allows us to obtain answers to counterfactual inquiries, i.e.,…
Statistical analysis of financial data most focused on testing the validity of Brownian motion (Bm). Analysis performed on several time series have shown deviation from the Bm hypothesis, that is at the base of the evaluation of many financial derivatives. We inquiry in the behavior of measures of performance based on …
Let be a subgroup of generated by three parabolic transformations. The main goal of this paper is to present an algorithm to determine whether or not is discrete. Historically discreteness algorithms have been considered within several broader mathematical paradigms: the discreteness problem, the con…
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…
This paper clarifies deep learning for IS scholars.
This paper describes the algorithms, features and implementation of PyDEC, a Python library for computations related to the discretization of exterior calculus. PyDEC facilitates inquiry into both physical problems on manifolds as well as purely topological problems on abstract complexes. We describe efficient algorith…
The study of a machine learning problem is in many ways is difficult to separate from the study of the loss function being used. One avenue of inquiry has been to look at these loss functions in terms of their properties as scoring rules via the proper-composite representation, in which predictions are mapped to probab…
With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software de…
Simulation-based inference methods can produce unreliable posterior approximations.
We propose a stochastic map model of economic dynamics. In the last decade, an array of observations in economics has been investigated in the econophysics literature, a major example being the universal features of inequality in terms of income and wealth. Another area of inquiry is the formation of opinion in a socie…
New framework learns disentangled causal representations from observed labels.
The purpose of this book is to give an exposition of geometry, from a point of view which complements Klein's Erlangen program. The emphasis is on extending the classical Euclidean geometry to the finite case, but it goes beyond that. After a brief introduction, which gives the main theme, I present the main results, a…
Reinterprets Granger causality with causal Bayesian networks and Reichenbach's principles.
New measures for causal entropy and information gain studied.
We quantify causal bias in continuous treatment settings.
Paper develops a model for verifying facts in tables without pre-retrieved evidence.
Improved Granger causality method for dynamic time series data.
The study examines causal razors and their logical relations, highlighting a dilemma in causal discovery.
CIB compresses variables causally, preserving key causal interactions.
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
A new method clusters heterogeneous subgroups for accurate causal learning.
Framework for Granger causality in extreme events.
Proposes DCNAR for dynamic causal inference from neural time series.
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the…
New algorithms for causal bandits without knowing the graph structure.
This review explores causal decision-making to improve decision quality.
New model improves data augmentation for causal tasks.
DoWhy-GCM extends causal inference in graphical models for diverse queries.
ABCI infers causal models and queries simultaneously using Bayesian active learning.
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
Proposes Causal Loss to improve machine learning models' causal inference.
iCITRIS learns causal variables from interactive systems with instantaneous effects.