Transfer entropy analyzes interactions between network communities, including rare events.
problem Understanding information flows between network communities.
method Transfer entropy analysis, including Rényi transfer entropy for rare events.
result Transfer entropy provides a coherent description of community interactions, including non-linear interactions.
Transfer entropy shows abnormal brain connectivity in depression.
problem Abnormal brain connectivity in depression.
method Transfer entropy analysis on EEG recordings.
result Aberrated dynamics and direction of information between brain centers in depression.
New method speeds up lead-lag detection between asynchronous time series.
problem Slow inference of lead-lag networks between long time series.
method Derive asymptotic distribution of Transfer Entropy and introduce time-shifted time series.
result Statistically validated lead-lag networks between time series.
Paper proposes a method to estimate Transfer Entropy using Copula Entropy.
problem Estimating Transfer Entropy for causal discovery.
method Non-parametric method based on Copula Entropy.
result The proposed method effectively infers causality relationships from data.
In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analy…
Study examines entropy and transfer entropy in DJIA before 1997 Asian crisis.
problem Analyzing entropy and transfer entropy in financial markets.
method Measures entropy, Pearson correlation, and modified transfer entropy in DJIA.
result Different relationships emerge from conventional Pearson correlations.
The paper introduces tools to assess data transformation quality using information entropy.
problem Assessing the quality of data transformations in machine learning tasks.
method Introduces a decomposition of maximal potential entropy into non-transferable, transferable but not transferred, and transferred parts.
result Developed a visual exploratory tool called the Channel Multivariate Entropy Triangle to assess information transfer efficiency.
New framework uses entropy to improve robust color transfer.
problem Improving color transfer between images.
method Entropy regularisation in robust optimal transport.
result Shows Grogan et al's method is a robust optimal transport framework.
Develops variable-lag Granger causality and Transfer Entropy for time series analysis.
problem Fixed time delay assumption in Granger causality and Transfer Entropy does not hold in many applications.
method Variable-lag Granger causality and Transfer Entropy, using optimal warping path of Dynamic Time Warping (DTW).
result Proposed methods perform better than existing methods in both simulated and real-world datasets.
Detects non-linear causality between social media sentiment and cryptocurrency prices.
problem Detecting causality between non-linear time series data.
method Use of transfer entropy to measure information transfer, validating against synthetic data, and applying significance tests.
result Significant non-linear causality detected, orders of magnitude greater than linear causality.
This paper develops geometric tools for causal inference using information flow concepts.
problem Developing a geometric interpretation of causal inference from probabilistic measures.
method Introducing a new measure, GeoC_{y
ightarrow x}, based on fractal correlation dimension.
result Avoids boundedness issues in transfer entropy, providing a more robust measure of causal inference.
EPSTE: A geometric token and deep learning approach to estimating transfer entropy in neuroimaging time series
problem Inferring directed interactions between neural systems from EEG and MEG
method Reframing TE estimation as a learnable problem operating on structured symbolic representations
result EPSTE achieves near-perfect recovery of ground-truth directed structure and significantly lower absolute error than the baseline
Calculates local Granger causality for Gaussian and nonlinear systems.
problem Understanding causal influence in complex systems.
method Vector autoregression and information-theoretic approach.
result Local Granger causality offers a robust and fast method for time-directed information transfer.
The paper studies practical estimation and interpretation of Rényi transfer entropy.
problem Challenges in accurately estimating and interpreting Rényi transfer entropy.
method Systematic study of k-nearest neighbor estimator for Rényi entropy and transfer entropy.
result Effective estimates of effective Rényi transfer entropy can accurately capture directional information flow.
State entropy regularization improves robustness in reinforcement learning, especially under structured perturbations.
problem Structured and spatially correlated perturbations in reinforcement learning.
method State entropy regularization, compared to policy entropy.
result State entropy regularization provides better robustness to structured and spatially correlated perturbations.
Bayesian approach models policy distribution for faster exploration and transfer learning.
problem Challenges in exploration and adaptation to new tasks in transfer learning.
method Modeling a distribution over policies in a Bayesian deep reinforcement learning setup.
result Favorable experimental results on GridWorld and MiniGrid environments.
The paper introduces a new method to analyze market price changes using entropy transfer.
problem Lack of methods to observe the spreading dynamics of market prices.
method Combines entropy transfer with Granger causality to create a weighted directed graph.
result Demonstrates the effectiveness of the proposed method through comparison with correlation coefficients.
We analyze a simple asset transfer model in which the transfer amount is a fixed fraction f of the giver's wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values o…
Bitcoin's price direction is better predicted without additional drivers during high volatility.
problem Predicting Bitcoin's price direction using various determinants.
method Continuous local transfer entropy for feature selection and deep learning classification model.
result Bitcoin's price direction can be better predicted without additional drivers during high volatility.
Estimates task difficulty and transferability without trained models.
problem Estimating the difficulty and transferability of supervised classification tasks.
method Information theoretic approach using conditional entropy between training labels.
result Hardness and transferability estimates correlate with empirical values.
Entropy measures financial drawdowns, volatility, and volatility regimes.
problem Analyzing financial time series with entropy measures.
method Transfer entropy, state space models, entropy of realised volatility.
result Entropy reveals information flows and volatility regimes in financial markets.
While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge et al. (2012), it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of d…
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
Study improves deep reinforcement learning transfer by applying regularization techniques.
problem Improving generalization in deep reinforcement learning across tasks.
method Proposes regularization techniques including sample elimination, early stopping, and maximum entropy regularized adversarial learning.
result Enhanced robustness and generalization in deep transfer reinforcement learning.
In this paper, we quantify the statistical coherence between financial time series by means of the Renyi entropy. With the help of Campbell's coding theorem we show that the Renyi entropy selectively emphasizes only certain sectors of the underlying empirical distribution while strongly suppressing others. This accentu…
Simple regional perturbations maintain model transferability while reducing adversarial example distortion.
problem Comparing efficacy of regional adversarial attacks without complex methods.
method Developed a simple regional adversarial perturbation attack using cross-entropy sign.
result Localized adversarial examples require significantly less Lp norm distortion compared to non-local counterparts. LEEP measures transferability of learned representations efficiently.
problem Evaluating the transferability of learned representations in machine learning.
method LEEP: Log Expected Empirical Prediction, a simple measure requiring one pass through the target data set.
result LEEP predicts transfer and meta-transfer learning performance and convergence speed, outperforming existing measures.
This work uses transfer entropy to reconstruct influential network between global stock markets.
problem Noise in network structure due to mismatch of scale between single market and global markets.
method Reconstructing influential network between ten typical stock markets using transfer entropy.
result Before financial crises, connection strength between markets reaches a maximum, acting as an early warning signal.
Investor flows in Korean equity market transmit shared information, not private signals.
problem Whether investor flows transmit private information or only public signals.
method Transfer Entropy networks constructed from investor-type flows over
umNDates{} trading days.
result Investor flows transmit shared information, not private signals.
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
problem Understanding information dynamics in Bitcoin markets during the 2017 crash.
method Analysis of high-frequency market-microstructure observables using information theoretic measures.
result Temporal changes in information sharing across markets, including predictability, memory, and synchronous coupling.
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
problem Understanding information transmission and market dynamics in Chinese stock sectors.
method Daily closing price data of 28 sectors from 2000 to 2017, transfer entropy, maximum spanning arborescence (MSA).
result The composite sector is an information source, and the non-bank financial sector is an information sink.
The paper measures semantic information production in generative models using information theory.
problem Measuring when semantic decisions are made during generative model training.
method Using an online formula for the optimal Bayesian classifier, the paper estimates conditional entropy and determines time intervals for highest information transfer.
result Semantic information transfer is highest in intermediate stages of diffusion, with different classes making decisions at different times.
We investigate unification of two systems of identical elements having different dimensions which may be of interest for both physics and economics. Characteristic parameters as well as explicit formulae for the temperature (in economics - capital turnover) and dimension of the united system are obtained as functions o…
Deep learning speeds up protein mapping entropy calculation.
problem Efficiently calculating the mapping entropy of protein structures.
method Deep graph networks for accelerating mapping entropy computation.
result Deep graph networks achieve a speedup factor of up to 10^5.
New findings show Shannon information measures fail to accurately assess multivariate dependencies.
problem Accurately measuring information flow in complex systems.
method Demonstrated that Shannon information measures fail to distinguish between dyadic and polyadic relationships.
result Shannon information measures are inadequate for discovering meaningful dependency structures in joint probability distributions.
Model improves information transfer from visual streams.
problem Challenges in unsupervised learning from continuous visual data.
method Inspired by physics, maximizes mutual information through temporal process.
result Focus of attention enhances information transfer from input stream.
We develop SIN to analyze financial bubbles' sector influence.
problem Understanding causal relationships between sectors during financial bubbles.
method Developed Hidden Markov Model (HMM) and transfer entropy to quantify influence.
result SIN reveals sectors that influenced others during the bubble and crash.
Robotics learns new skills faster by reusing past movements.
problem Learning new motor skills is time-consuming and requires exploration of a large space of motor configurations.
method Combines probabilistic movement primitives with relative entropy policy search for skill initialization and adaptation.
result Quality of learned skills improves and the number of required iterations to learn a new task can be reduced by more than 60%.
Proposes a new method to better understand complex system interactions.
problem Current methods like Granger causality and transfer entropy fail to capture higher-order interactions.
method Introduces a generalized approach to capture multivariate causal interactions.
result The method can distinguish causal roles in synergetic interactions.
This study examines how the size and alignment of pretraining data affect the performance of large language models on downstream tasks.
problem Understanding how the size and alignment of pretraining data impact the performance of large language models on downstream tasks.
method Investigated the scaling behavior of large language models in a transfer learning setting, focusing on machine translation tasks.
result The size of the finetuning dataset and the distribution alignment between pretraining and downstream data significantly influence the scaling behavior of downstream performance.
Unified framework maps financial market dynamics using TE and KM, revealing directional information flow.
problem Challenges in traditional correlation analysis of financial markets, especially during crises.
method Combines Transfer Entropy (TE) and Kramers-Moyal (KM) expansion to analyze dynamic interactions among major indices.
result Increased directional information flow during crises, highlighting gold-dollar and oil-equity linkages.
Study shows regional banking uncertainty spillovers over 10 years.
problem Understanding uncertainty spillovers between regional banking systems.
method Improved Diebold-Yilmaz method using daily returns and ridge regularization.
result Regional uncertainty is significantly causally related, with North America influencing EU and ASEAN.
This note improves code quality for equientropic channels by maximizing marginal entropy.
problem Improving code quality for equientropic channels with finite bit usage.
method Characterizes code quality by an upper bound on expected minimum error probability for equientropic channels, showing random coding maximizes marginal entropy.
result For equientropic channels, random coding maximizes marginal entropy and achieves minimal upper bound on expected minimum error probability.
Novel method uses information theory to measure causal influences during transient neural events.
problem Characterizing network interactions during transient neural events.
method Structural Causal Models, Information Theory, Transfer Entropy, Dynamic Causal Strength, Relative Dynamic Causal Strength.
result Introduced a novel measure, relative Dynamic Causal Strength, with theoretical and empirical support.
Bayesian framework inserts prior knowledge in RL for faster task solving.
problem Faster transfer learning across reinforcement learning tasks.
method Bayesian posterior distribution combining task-specific and prior knowledge.
result Significant speed ups achieved in maze solving.
Algorithm learns Nash equilibria in stochastic games using entropy-regularized policies.
problem Learning Nash equilibria in zero-sum stochastic games is computationally expensive.
method Entropy-regularized soft policies for Q-function updates.
result Algorithm converges to Nash equilibrium under certain conditions.
Study compares information flow between Chinese and US stock sectors.
problem Analyzing how information flows between sectors in Chinese and US stock markets.
method Daily sector indices, transfer entropy of daily returns, comparing 2000-2017.
result Most active sectors in information exchange differ between China and US, reflecting market dynamics.
New method estimates traffic congestion delays using statistical causality.
problem Accurate estimation of traffic congestion delays during accidents.
method Proposes a novel time delay estimation method using lag-specific transfer entropy (TE) and Markov bootstrap techniques.
result Validated the method's efficacy using simulated and real data.