Bayesian methods improve k-NN classification performance.
problem Improving k-NN classification methods for mutual and symmetric neighborship.
method Bayesian mutual and symmetric k-NN regression methods.
result Bayesian methods outperform or match traditional k-NN methods.
Proposes a method to learn optimal neighbors and projection matrix in low-dimensional space.
problem Difficulty in precisely measuring similarity and selecting optimal neighbors in high-dimensional space.
method Models similarity and neighbors as variables, optimizing a unified objective function with nonnegative and sum-to-one constraints.
result Optimal similarity and projection matrix learned simultaneously, with adaptive regularization parameter.
The paper proves mutual information measurement is statistically limited.
problem Measuring mutual information from finite data is difficult.
method Proves statistical limitations on any method of measuring mutual information.
result Any distribution-free high-confidence lower bound on mutual information estimated from N samples cannot be larger than O(ln N ).
New measures quantify mutual dependence between multiple random vectors.
problem Measuring mutual dependence between multiple random vectors.
method Proposes three measures based on generalized distance covariance.
result Empirical and simplified empirical measures effectively test mutual independence.
New neural network approach using mutual information.
problem Training neural networks for imbalanced datasets.
method Converts neural network classifiers to mutual information evaluators.
result New form of softmax leads to better classification accuracy, especially for imbalanced datasets.
Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.
problem Estimating mutual information for complex distributions.
method Analytical description of profiles, introduction of Bend and Mix Models, Monte Carlo estimation.
result Bend and Mix Models accurately estimate mutual information profiles and provide Bayesian estimates.
Paper benchmarks mutual info estimators on diverse distributions.
problem Evaluating mutual information estimators on complex, real-world distributions.
method Constructs a diverse family of known-ground truth distributions, proposes a benchmark platform.
result Highlights differences in classical and neural estimators' performance across various conditions.
Neural network estimates mutual information for ICA.
problem Learning statistically independent features from noisy data.
method Minimizing mutual information estimated by a MINE network in a differentiable encoder network.
result Qualitatively equal solution to FastICA for blind-source-separation.
MIM learns useful representations with high mutual information.
problem Learning useful representations for downstream tasks.
method Symmetric Jensen-Shannon divergence and mutual information regularizer in an encoder/decoder framework.
result MIM learns high mutual information representations without posterior collapse.
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
Neural estimator improves mutual information estimation in high dimensions.
problem Estimating mutual information in high dimensions is challenging.
method Parametrizing conditional densities with normalizing flows and using block autoregressive structure.
result Improved mutual information estimation on benchmark tasks.
Improved bounds on learning algorithms' performance using conditional mutual information.
problem Bounding the generalization error of learning algorithms.
method Introducing conditional mutual information and disintegrated mutual information to tighten bounds.
result New bounds are tighter than previous ones, especially for noisy, iterative algorithms.
MINE uses neural networks to estimate mutual information efficiently.
problem Estimating mutual information between high-dimensional variables.
method Gradient descent over neural networks for mutual information estimation.
result MINE is linearly scalable in dimensionality and sample size, and is strongly consistent.
The paper argues that normalized mutual information is biased in clustering and community detection.
problem Bias in normalized mutual information for clustering and community detection.
method Introducing a modified version of mutual information to correct for information content and spurious dependence.
result The modified mutual information leads to different conclusions about which algorithms are best for community detection.
TCMI assesses mutual dependence of continuous variables without parametric assumptions.
problem Estimating mutual information from continuous distributions.
method TCMI extends mutual information to continuous variables using cumulative distributions.
result TCMI facilitates feature selection and ranking of variable sets.
Synthetic construction of 3D complex bases.
problem Creating a complete set of unbiased bases in 3D complex space.
method Synthetic construction using complex projective trigonometry.
result Synthetic construction of mutually unbiased bases in C^3.
This study analyzes mutual influence on investment strategies of financial market agents.
problem Mutual influence among agents in financial markets and its impact on investment strategies.
method Formulated optimal investment differential game problem, derived analytical solutions, proposed fast algorithm, and theoretically analyzed mutual influence.
result Agents' optimal strategies converge to the asymptotic strategy when mutual influence is strong and approaches infinity.
This paper studies mutual information in sequence models and finds Transformers excel in capturing long-range dependencies.
problem Understanding the expressive power of sequence models in capturing temporal dependencies.
method Theoretical and empirical analysis of linear and nonlinear RNNs, including Transformers.
result Transformers can capture long-range mutual information more efficiently than RNNs.
We find the maximum mutual information for neural networks and its key determinants.
problem Understanding the maximum mutual information in neural architectures.
method Derived closed-form expression for maximum mutual information across neural network families.
result Maximum mutual information stems from a generalized formula and is influenced by network width and statistical invariances.
New mutual information measure improves classification and community detection accuracy.
problem Standard mutual information measure can be inaccurate under real-world conditions.
method Corrected mutual information measure that accounts for all cases.
result Improved mutual information measure reduces errors in classification and community detection.
MIM learns joint distributions with mutual information and low divergence.
problem Learning joint distributions over observations and latent variables.
method Probabilistic auto-encoder with three design principles: low divergence, high mutual information, and low marginal entropy.
result MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and comparable data log likelihood to VAE.
Dynamic learning rate set using Mutual Information for neural networks.
problem Optimizing learning rate in deep neural networks.
method Mutual Information is used to dynamically adjust the learning rate during training cycles. Two approaches are tested: relative change and relative to a reference measure.
result Mutual Information can effectively set dynamic learning rates, leading to competitive performance.
Improves ICA via novel mutual dependence measures.
problem Improving Independent Component Analysis (ICA) for better component independence.
method Combines distance-based and kernel-based mutual dependence measures, introduces Latin hypercube sampling and Bayesian optimization for initialization.
result MDMICA outperforms other methods in terms of mutual independence of estimated components, especially when the ICA model is misspecified.
New method uses mutual info and network science to explain deep learning models.
problem Interpreting deep neural networks for understanding their decision-making process.
method Coupling mutual information with network science to quantify information flow in deep learning models.
result Proposed NIF technique for codifying information flow in deep learning models.
A new method estimates mutual information using neural classifiers.
problem Estimating mutual information for high-dimensional data is challenging.
method Trains a classifier to estimate joint distribution probability.
result Demonstrates high accuracy and reduces variance compared to variational methods.
Study examines Indian equity mutual funds' investment style and risk-shifting.
problem Understanding how Indian equity mutual funds' investment styles affect their returns.
method Estimating size and style beta coefficients, identifying breakpoints, analyzing investment styles, and assessing risk-shifting intensity.
result Funds can enhance returns by shifting to high-return styles like Small Value and Small Blend.
New estimator improves mutual information estimation.
problem Estimating mutual information in data science and machine learning.
method Proposes a new estimator that uses a preliminary estimate of the data distribution.
result A preliminary estimate helps in estimating mutual information more accurately.
Improved method for encoding contingency tables reduces mutual information bias.
problem Mutual information bias in measuring label similarity.
method Improved method for encoding contingency tables to reduce information cost.
result Better bound on reduced mutual information in typical use cases.
Proposes a framework to maximize mutual information in VAE models for better latent code representation.
problem Lack of explicit measurement of the quality of learned representations in VAE models.
method Variational Mutual Information Maximization Framework for VAE.
result Maximizes mutual information between latent codes and observations, improving latent code representation.
The paper proposes a method to learn structured representations from unlabeled data using mutual information maximization.
problem Learning structured representations from unlabeled data.
method Adversarial maximization of mutual information between a structured latent variable and a target variable.
result The proposed method outperforms current baselines in document hashing and yields highly compressed interpretable representations.
Softmax cross-entropy optimizes mutual information in neural networks.
problem Understanding the relationship between mutual information and classification neural networks.
method Demonstrated that optimizing softmax cross-entropy maximizes mutual information between inputs and labels.
result Softmax cross-entropy can approximate mutual information and highlight relevant image regions.
A new regularizer boosts long-range dependency in sequence data.
problem Improving long-range dependency in sequence data models.
method Developed a mutual information regularizer to enhance sequence learning.
result The approach increases mutual information and likelihood on holdout data.
Bayesian method finds patterns of mutual independence in data.
problem Investigating mutual independence in statistics.
method Bayesian model comparison using Markov chain Monte Carlo (MCMC).
result Automated search for patterns of mutual independence.
Proposes a new bound on generalization error using conditional mutual information.
problem Improving the generalization error bound in machine learning.
method Combines error decomposition and conditional mutual information techniques.
result New bound is order-wise better than previous ones in a simple Gaussian setting.
Novel approach checks mutual fund style consistency with actual composition.
problem Ensuring mutual fund style consistency with actual fund composition.
method Time series decomposition of stock prices to analyze mutual fund composition.
result Notable deviations found between proclaimed fund style and actual composition.
Machine learning categorizes mutual funds for better investment strategies.
problem Identifying similar mutual funds for diversified investment applications.
method Machine learning to learn and reproduce a well-regarded categorization system.
result Machine learning can learn and reproduce a categorization system that is widely regarded.
Paper proposes mutual information learning for deep learning classifiers.
problem Overfitting in deep learning models.
method Mutual information learning framework to train classifiers.
result MILCs achieve better generalization than conditional entropy classifiers.
Active feature selection uses mutual information to choose fewer labels for better feature selection.
problem Selecting features with limited labeled data.
method Uses active feature selection with mutual information criterion, optimizing label selection for better feature quality.
result Algorithm selects features with higher mutual information using fewer labels than the data set size.
Mutual info trees show higher risk in Brazilian equity network during transition.
problem Identifying nonlinear dependencies in Brazilian equity network.
method Used mutual information minimum spanning trees to compare with linear correlation.
result Mutual info trees indicate higher risk and power law tail in volatility transmission.
Paper addresses decontamination of mixed membership and partial label models.
problem Recovering base distributions from mutual contamination models.
method General setting with arbitrary probability spaces, algorithms for mixed membership and partial label models.
result Necessary and sufficient conditions for identifiability and algorithms for both models.
The paper introduces submodular information measures for machine learning applications.
problem Generalizing information-theoretic measures to non-random variables.
method Developing combinatorial information measures based on submodular functions.
result Submodular mutual information is submodular in one argument for certain submodular functions.
Estimates conditional mutual information with modified joint distribution for practical applications.
problem Estimating conditional mutual information with a modified joint distribution.
method Developed K nearest neighbor based estimators using importance sampling and coupling trick.
result Finite k consistency of the estimator demonstrated.
The paper proposes a model to learn disentangled representations using mutual information.
problem Learning disentangled representations from shared and exclusive attributes.
method Mutual information maximization for shared attributes and minimization for disentanglement.
result The proposed model outperforms state-of-the-art models in representation disentanglement.
In data science, it is often required to estimate dependencies between different data sources. These dependencies are typically calculated using Pearson's correlation, distance correlation, and/or mutual information. However, none of these measures satisfy all the Granger's axioms for an "ideal measure". One such ideal…
Paper proposes a novel method to reduce mutual information for missing data imputation.
problem Missing data imputation in datasets with missingness patterns.
method Iterative minimization of KL divergence between imputed data and missingness mask, using rectified flow training objective.
result The proposed method achieves superior imputation performance on synthetic and real-world datasets.
Wasserstein dependency measure improves unsupervised representation learning.
problem Incomplete representations from mutual information maximization.
method Wasserstein dependency measure using Wasserstein distance instead of KL divergence.
result Improved results on tasks with high mutual information.
New method estimates mutual information using normalizing flows.
problem Mutual information estimation in high-dimensional data.
method Normalizing flows to map data to target distributions with known MI.
result Theoretical guarantees and practical advantages demonstrated.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.