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.
Proposes second-order influence functions for identifying influential groups in test-time predictions.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
We investigated the network structures of the Japanese stock market through the minimum spanning tree. We defined grouping coefficient to test the validity of conventional grouping by industrial categories, and found a decreasing in trend for the coefficient. This phenomenon supports the increasing external influences …
Influence functions estimate the effect of removing a training point on a model without the need to retrain. They are based on a first-order Taylor approximation that is guaranteed to be accurate for sufficiently small changes to the model, and so are commonly used to study the effect of individual points in large data…
Optimizes choice sets to influence group decisions.
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal…
Influence functions are inaccurate in deep learning models, especially for deeper networks.
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to characterize social influence, and, in general, most data-science analyses focus on…
Survey of combination theorems in geometry and dynamics.
New method estimates data influence efficiently by leveraging test samples.
Predicting event attendance using social influence from social networks.
SBO uses dual voting to build consensus in noisy feedback settings.
CrossWalk enhances fairness in graph algorithms by biasing random walks.
Finance has benefited from the Wolfram's NKS approach but it can and will benefit even more in the future, and the gains from the influence may actually be concentrated among practitioners who unintentionally employ those principles as a group.
Using the United Nations COMTRADE database we apply the reduced Google matrix (REGOMAX) algorithm to analyze the multiproduct world trade in years 2004-2016. Our approach allows to determine the trade balance sensitivity of a group of countries to a specific product price increase from a specific exporting country taki…
GGDA simplifies DA for large models, speeding up attribution by up to 50x.
Study improves carbon price forecasting using quantile regression and feature selection.
This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at …
MRI image quality affects statistical and predictive analysis of brain morphology.
Establishes statistical and computational bounds for influence diagnostics.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
New model clusters cells and individuals, revealing genetic influences on cell types.
Dynamic Influence Tracker measures changing sample importance during model training.
Previous work has shown that popular trending events are important external factors which pose significant influence on user search behavior and also provided a way to computationally model this influence. However, their problem formulation was based on the strong assumption that each event poses its influence independ…
We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on the effectiveness o…
RelatIF selects more intuitive training examples for explaining model predictions.
Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can be direct (a direct influence on model outcomes) and indirect (model outcomes are influenced via proxy features). Feature influence can also …
Model learns collective and individual dynamics in time series data.
Complete criterion for VoI in multi-decision influence diagrams established.
The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the electi…
Aims to optimize influence spread in social networks using bandit algorithms.
In market modeling, one often treats buyers as a homogeneous group. In this paper we consider buyers with heterogeneous preferences and products available in many variants. Such a framework allows us to successfully model various market phenomena. In particular, we investigate how is the vendor's behavior influenced by…
Margulis wrote in the preface of his book Discrete subgroups of semisimple Lie groups that "A number of important topics have been omitted. The most significant of these is the theory of Kleinian groups and Thurston's theory of 3-dimensional manifolds: these two theories can be united under the common title of Theory o…
FAIRIF improves fairness in deep learning models without changing the model architecture.
Analytic realization of Thom-Smale complex for G-manifolds.
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying influential users and studying their impact on other users is an important problem because it provides…
The paper simplifies influence computations for large-scale machine learning models.
The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality bet…
Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multic…
New algorithm for competing influence spread in unknown networks.
GRANITE unifies feature-based explanation methods to reduce disagreement.
A study on the relation between the smooth structure of a symplectic homotopy K3 surface and its symplectic symmetries is initiated. A measurement of exoticness of a symplectic homotopy K3 surface is introduced, and the influence of an effective action of a K3 group via symplectic symmetries is investigated. It is show…
Better Hessian approximations improve influence function attributions in deep learning.
The paper extends influence functions to sequence tagging tasks for better model interpretability.
Extends Milnor's invariants to 3-manifolds, solving an open problem.
This study analyzes mutual influence on investment strategies of financial market agents.
Influence functions help study large language model generalization, revealing surprising decay patterns.
Recently, along with the emergence of food scandals, food supply chains have to face with ever-increasing pressure from compliance with food quality and safety regulations and standards. This paper aims to explore critical factors of compliance risk in food supply chain with an illustrated case in Vietnamese seafood in…