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 a deep learning method for modeling dynamic individual-level latent trajectories with changing parameters.
Deep learning models can infer individual trajectories from sparse data.
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an…
LEGEND learns complex dynamics from aggregate data.
Proposes a model to handle mobile health data with irregular measurements.
Causal models help ensure fairness in systems with changing environments.
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
Model dynamic customer sensitivities across categories.
We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends…
New approach models individual vitality for better mortality predictions.
Method learns dynamics from aggregate data without full trajectories.
Model predicts depression and anxiety from social network dynamics.
Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.
New method for certified unlearning reduces noise injection.
Model learns collective and individual dynamics in time series data.
Model compares altruism and individualism in wealth dynamics.
Unified framework detects dynamic community structure in brain networks across individuals.
New model recommends stocks considering individual preferences and diversification.
A new method optimizes individual treatment effects in dynamic settings.
Method interprets LSTMs at the cell level for better understanding of their dynamics.
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actor…
WRSE predicts dynamic survival distributions in ICU patients.
New framework for forecasting psychological processes from ILD.
Network analysis improves stock return forecasting.
Enhances patient failure prediction using dynamic survival models.
Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on t…
The paper develops methods to estimate optimal treatment sequences under policy constraints.
We study the cross-correlation matrix of inventory variations of the most active individual and institutional investors in an emerging market to understand the dynamics of inventory variations. We find that the distribution of cross-correlation coefficient has a power-law form in the bulk followed by …
PASS model predicts disease progression with both accuracy and interpretability.
In the top-down approach to multi-name credit modeling, calculation of singe name sensitivities appears possible, at least in principle, within the so-called random thinning (RT) procedure which dissects the portfolio risk into individual contributions. We make an attempt to construct a practical RT framework that enab…
Model learns evolving network relationships over time.
We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and custo…
Proposes individual fairness for clustering, making data points prefer their own cluster.
Method learns optimal treatment sequences from observational data.
We investigate the dynamics of a trust game on a mixed population where individuals with the role of buyers are forced to play against a predetermined number of sellers, whom they choose dynamically. Agents with the role of sellers are also allowed to adapt the level of value for money of their products, based on payof…
Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
Tax dynamics affects wealth distribution in a linearly growing socio-economic model.
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi…
CLSB models system dynamics from cross-sectional data with population-level regularization.
For real hyperbolic spaces, the dynamics of individual isometries and the geometry of the limit set of nonelementary discrete isometry groups have been studied in great detail. Most of the results were generalised to discrete isometry groups of simply connected Riemannian manifolds of pinched negative curvature. For sy…
An algorithm is presented for momentum gradient descent optimization based on the first-order differential equation of the Newtonian dynamics. The fictitious mass is introduced to the dynamics of momentum for regularizing the adaptive stepsize of each individual parameter. The dynamic relaxation is adapted for stochast…
This paper presents a dynamic model to study the impact on the economic outcomes in different societies during the Malthusian Era of individualism (time spent working alone) and collectivism (complementary time spent working with others). The model is driven by opposing forces: a greater degree of collectivism provides…
We give a microscopic representation of the stock-market in which the microscopic agents are the individual traders and their capital. Their basic dynamics consists in the auto-catalysis of the individual capital and in the global competition/cooperation between the agents mediated by the total wealth invested in the s…
RL optimizes resource allocation in MG by balancing experience and exploration.
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
This paper addresses the problem of online learning in a dynamic setting. We consider a social network in which each individual observes a private signal about the underlying state of the world and communicates with her neighbors at each time period. Unlike many existing approaches, the underlying state is dynamic, and…
RL algorithms with medical integration improve personalized treatment recommendations.