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.
A method for logistic regression inference using both internal and external data.
Estimates non-parametric logistic model using case-control data and external summary info.
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a …
The study analyzes Bitcoin market volatility using GARCH models and external information.
Survey of methods to incorporate external knowledge into stock price prediction.
Study improves vehicle motion prediction by incorporating traffic density.
Develops a KL-divergence-based deep learning method for survival analysis with short data.
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
New algorithms improve privacy-preserving data release using external predictions.
We study the dynamics of the batch minority game, with random external information, using generating functional techniques a la De Dominicis. The relevant control parameter in this model is the ratio of the number of possible values for the external information over the number of trading agents. In the …
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…
Framework sharpens causal effect estimates without external assumptions.
Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing pop…
To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of operational risk involves the bank's internal data, expert opinions and relevant external data. In this paper we suggest a new …
Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.
Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process …
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal. In the natural world, intelligent organisms learn from internal drives, bypassing the need for external signals, which is beneficial for a wide range of tasks. Motivated by this observation, we propose to formulate…
Fuzzy eIX method evolves classifiers for online data streams.
engGNN combines external and generated graphs to improve disease classification and biomarker discovery.
Study confirms sparse coding in whole brain using MRI data.
KG-WDRO optimizes transfer learning with external knowledge.
We study a dynamical Ising model of agents' opinions (buy or sell) with coupling coefficients reassessed continuously in time according to how past external news (magnetic field) have explained realized market returns. By combining herding, the impact of external news and private information, we test within the same mo…
By treating the financial market as a thermodynamic system, we establish a one-to-one correspondence between thermodynamic variables and economic quantities. Measured by the expected loss under the worst-case scenario, financial risk caused by model uncertainty is regarded as a result of the interaction between financi…
In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional netw…
BED-LLM uses Bayesian experimental design to improve LLMs' information gathering.
Enhances image classification by integrating semantic hierarchy into CNN models.
In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in g…
In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and su…
Enhances weather detection by learning from auxiliary information.
Language Models (LMs) are important components in several Natural Language Processing systems. Recurrent Neural Network LMs composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results. However, these models still struggle to process long sequences which are more li…
A new measure normalizes clustering accuracy to evaluate algorithms better.
The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are cru…
The thesis introduces methods to use semantic hierarchy in image classification.
A transfer learning method builds high-dimensional models using disparate datasets.
The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their clas…
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electric…
Bayesian Tensor Network combines prior and data likelihood for efficient prediction and parameter estimation.
Paper proposes AI for stock market forecasting using external knowledge.
We study the dynamics of a version of the batch minority game, with random external information and with different types of inhomogeneous decision noise (additive and multiplicative), using generating functional techniques à la De Dominicis. The control parameters in this model are the ratio of the number o…
New estimator improves ATT estimation efficiency with external controls.
The constant growth of the e-commerce industry has rendered the problem of product retrieval particularly important. As more enterprises move their activities on the Web, the volume and the diversity of the product-related information increase quickly. These factors make it difficult for the users to identify and compa…
Method estimates model performance on external samples from limited statistical characteristics.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
EBMs improve continual learning without external memory or regularization.
Study long-term asset liquidation behavior with external flows.
A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading…
Proposes a dynamic model for urban traffic volume prediction.