New method quantifies multivariate redundancy using maximum entropy decompositions.
problem Elusive multivariate measures of redundancy that comply with nonnegativity and axioms.
method Maximum entropy framework, rooted tree-based decompositions of mutual information.
result Quantifies different multivariate redundancy contributions.
New framework improves multivariate time series forecasting by minimizing redundant information.
problem Improving multivariate time series forecasting with deep learning techniques.
method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.
result Significantly surpasses existing models in comprehensive tests.
R package varrank ranks variables based on mutual information for multivariate data analysis.
problem Selecting and ranking variables for multivariate datasets.
method Minimum redundancy maximum relevance (mRMRe) model based on information theory.
result Flexible implementation for discrete and continuous data.
New metric for disentangling multivariate representations, accounting for more complex entanglements.
problem Current disentanglement metrics fail to detect entanglements involving more than two variables.
method Partial Information Decomposition framework to analyze information sharing and propose a new disentanglement metric.
result The proposed metric correctly identifies entanglements in high-dimensional spaces.
New method disentangles high-order effects in feature importance.
problem Quantifying cooperative effects in feature importance.
method Adaptive Leave One Covariate Out (LOCO) method to decompose LOCO into two-body and higher-order components.
result Decomposes LOCO into two-body and higher-order components, highlighting synergistic and redundant effects.
Proposes a new framework for decomposing information in multivariate settings.
problem Decomposing information from multiple sources about a target variable.
method Formal analogy with set theory and Blackwell order to define PID.
result Framework can be generalized to various information theories.
Feature selection is among the most important components because it not only helps enhance the classification accuracy, but also or even more important provides potential biomarker discovery. However, traditional multivariate methods is likely to obtain unstable and unreliable results in case of an extremely high dimen…
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
problem Understanding micro-trading behaviors before stock price jumps.
method Multivariate time series analysis considering temporal information.
result Identifies highly informative attributes for predicting price jumps.
The paper applies Gaussianization to analyze Earth data, simplifying complex multivariate distributions.
problem Challenges in accurately estimating information content in high-dimensional, heterogeneous Earth data.
method Multivariate Gaussianization for robust probability density estimation.
result Validates the method for estimating information-theoretic measures in Earth system data.
Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for uns…
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
The paper introduces a method to explain redundancy in deep CNNs using unit impulse response.
problem Redundancy in deep CNNs leads to unnecessary computations and increased cost.
method Empirical demonstration and unit impulse response analysis to identify and quantify redundancy across layers and depth.
result Identifies and quantifies redundancy in deep CNNs, providing better insights into their internal dynamics.
Redundancy improves learning stability and generalization in structured systems.
problem Understanding redundancy in structured systems for learning and generalization.
method Developed a theoretical framework that redefines redundancy as a geometric principle unifying various measures.
result Redundancy balances structure and coupling, leading to optimal stability and generalization.
New method quantifies redundant information using information bottleneck.
problem Quantifying redundant information among multiple sources.
method Formulated as an information bottleneck problem, termed redundancy bottleneck.
result Extracts information that best predicts the target without revealing source identity.
Transformers reduce redundancy by focusing on invariant relational quantities.
problem Substantial internal redundancy in Transformer models due to coordinate-dependent representations and continuous symmetries.
method Reformulate representations, attention mechanisms, and optimization dynamics in terms of invariant relational quantities, eliminating redundant degrees of freedom by construction.
result Architectures that operate directly on relational structures, providing a principled geometric framework for reducing parameter redundancy and analyzing optimization.
Proposes PEID for analyzing synergistic causation in complex systems.
problem Challenges in identifying and analyzing synergistic causation in complex systems.
method Partial Effective Information Decomposition (PEID) framework.
result Unified and computable characterization of synergistic causal relations.
Study shows DNNs often extract redundant features, influenced by network size and activation function.
problem Redundancy in deep neural network features.
method Hierarchical clustering of features based on cosine distances, varying network sizes and activation functions.
result Network size and activation function are key factors in DNN redundancy.
Paper proposes redundancy-free features for zero-shot object recognition.
problem Redundant visual features degrade zero-shot object recognition.
method Project original features into a new, statistically independent space.
result RFF-GZSL achieves competitive results on benchmark datasets.
This work explains scaling laws as redundancy laws in deep learning.
problem The mathematical origins of scaling laws in deep learning models remain unclear.
method Kernel regression and analysis of data covariance spectra.
result Scaling laws can be explained as redundancy laws, revealing the learning curve's slope depends on data redundancy.
New method uses information theory to uncover causal relationships in complex systems.
problem Discovering causal relationships in multivariate systems, especially in Bayesian networks and hypergraphs.
method Partial Information Decomposition (PID) to explicitly model higher-order interactions.
result PID components reveal direct causal neighbors and collider relationships in Bayesian networks and multi-tail hyperedges in causal hypergraphs.
Study on neural networks to identify redundancy issues in safe machine learning.
problem Identifying redundancy in neural network architectures for safe machine learning.
method Experiments with MNIST database using neural network classifiers.
result Underlines difficulties in using neural network classifiers for safe systems.
Study finds 10% redundant images in image classification datasets.
problem Redundancy in large image classification datasets.
method Analysis of CIFAR-10 and ImageNet datasets to identify redundant images.
result 10% of images are redundant and can be removed without significant loss of performance.
New entropy measures reveal information flow in CNNs without approximations.
problem Understanding information flow in convolutional neural networks (CNNs).
method Developed new entropy estimators based on Renyi's α-entropy and applied PID framework.
result Validated fundamental data processing inequalities and revealed properties of CNN training.
Clustering helps reduce spatial data size by identifying redundant points.
problem Too much spatial data makes it hard to answer research questions.
method Density-based clustering to compress data into representative features.
result Reduces spatial data set size by identifying and removing redundant points.
We simplify SSL by approximating redundant structural components with low-rank factorization.
problem Improving self-supervised learning performance with limited labeled data.
method Low-rank approximation of structural redundancy, introducing ε_s to measure approximation quality.
result The proposed method enhances SSL performance, as shown by theoretical and experimental validations.
This paper introduces a new measure to identify model redundancy in compressed CNNs.
problem Identifying remaining model redundancy in compressed CNNs.
method Developed a statistical formulation of CNNs and compressed CNNs via tensor decomposition, revealing discrepancies in sample complexity and model redundancy.
result Introduced a new model redundancy measure, the K/R ratio, for compressed CNNs. This paper explores how optimizing data access and reducing redundancy can improve machine learning algorithm performance.
problem Performance issues in machine learning algorithms due to data locality and redundancy.
method Analysis of data access patterns and computational redundancy in machine learning algorithms, identifying opportunities for reuse and experimentation.
result Initial indicative results show potential for improving performance through data access optimization and reuse of computation results.
New method calculates partial information for Gaussian systems based on dependency constraints.
problem Quantifying information sharing in multivariate Gaussian systems.
method Constructing maximum entropy models based on dependency constraints and deriving closed-form solutions.
result Closed-form solutions for Gaussian systems show differences in redundancy and synergy estimates compared to existing methods.
Optimized GPRNN reduces model complexity and overfitting, improving performance.
problem Overfitting in neural networks and high model complexity.
method Gaussian Process Regression - Neural Network hybrid with optimized redundant coordinates.
result Optimized GPRNN achieves lower test set error with fewer terms/neurons.
One of Powell's generators is not necessary.
problem Unresolved conjecture about generating Goeritz group.
method Short argument showing redundancy of one generator.
result One of Powell's generators is a consequence of others.
The study analyzes optimization trajectories in neural networks to reveal redundancy and redundancy-reducing strategies.
problem Understanding the directional structure and redundancy in neural network optimization.
method Introducing natural notions of complexity for optimization trajectories and analyzing their directional nature.
result Training only scalar batchnorm parameters can match the performance of training the entire network, indicating potential for hybrid optimization schemes.
This work introduces RISE to explain LLMs more reliably by distinguishing essential context.
problem Identifying which context elements influence LLM outputs reliably.
method RISE (Redundancy-Insensitive Scoring of Explanation) method.
result RISE provides more robust explanations than traditional methods.
This work identifies redundant tests in conditional-independence-based discovery that can improve graphical model accuracy.
problem Reliability and sensitivity of conditional-independence-based discovery algorithms.
method Analysis of redundant tests and their impact on error detection and correction.
result Redundant tests can improve graphical model accuracy but not all are beneficial.
Contrastive learning works well with redundant data views.
problem Improving unsupervised learning with redundant data views.
method Theoretical analysis of contrastive learning in a multi-view setting.
result Linear functions of learned representations are nearly optimal on prediction tasks when views are redundant.
Robust subgroup discovery finds non-redundant, statistically significant subgroups.
problem Finding interpretable, robust subgroups from data.
method Formulated subgroup lists for univariate and multivariate targets, used MDL principle and greedy heuristic SSD++.
result SSD++ outperforms previous methods in quality and size of subgroup lists.
DETOX improves distributed training resilience with redundancy and robust aggregation.
problem Byzantine node failures in distributed training.
method Combines redundancy and robust aggregation methods.
result Substantial increase in robustness with nearly linear runtime.
This paper explains adversarial examples as feature redundancy abuse.
problem Understanding and mitigating adversarial examples in machine learning.
method Information-theoretic model to explain adversarial attacks.
result Feature redundancy is necessary for adversarial examples.
New feature selection method for multi-labeled data.
problem Feature redundancy in multi-labeled data.
method Conditional information measure based on dependency maximization.
result Efficient and fast non-parametric feature selection.
Study on how deterministic dependencies affect information synergy and redundancy.
problem Understanding how deterministic dependencies impact information synergy and redundancy.
method Systematic analysis of deterministic dependencies on information decomposition.
result Identifies how negative terms can originate from deterministic dependencies and discusses implications for neural coding.
This paper compares feature selection methods for biomarker discovery in toxicant-treated fish.
problem Choosing the most suitable method for biomarker discovery in toxicant exposure studies.
method Three feature selection methods: SAM, mRMR, and GeoDE are compared.
result Different methods perform better in different cases, requiring dataset-specific decisions.
New method distinguishes feature relevance in non-linear contexts.
problem Finding relevant features with preserved redundancies.
method Random forest models and statistical methods.
result Distinguishes strong from weak feature relevance in non-linear problems.
Paper compresses deep neural networks by eliminating redundant neurons.
problem Challenges in deploying deep learning models due to high parameter count.
method Exploits non-linear redundancy to compress neural networks without loss.
result Reduces network size by up to 99% with minimal performance loss.
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
Wide neural networks' last hidden layers split into groups of redundant neurons.
problem Understanding why wide neural networks generalize well despite overfitting.
method Analyzed the last hidden layer representations of various convolutional neural networks.
result Wide hidden layers split into groups of redundant neurons, which help generalize.
BNs extract sparse, non-redundant features from climate data networks.
problem Redundant information in correlation networks limits physical feature extraction.
method Construct data-driven complex networks using Bayesian Networks.
result Sparse, non-redundant features reveal generalizable physical features.
Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization. However, despite their popularity, these algorithms suffer from a major limitation known as the "repeated Eigen-directions" phenomenon. That is, many of the embedding co…
mRMR method selects relevant features for marketing models without redundancy.
problem Selecting relevant features from large feature spaces in machine learning.
method Extends mRMR framework with non-linear redundancy and model-based relevance measures.
result Implemented mRMR method in production for Uber's marketing machine learning platform.
This paper enhances ML algorithms by improving data locality and reducing redundancy.
problem Improving performance of machine learning algorithms with complex data.
method Exploiting data locality and reuse in memory hierarchies of modern processors.
result Efficient implementation of machine learning algorithms can be achieved by reusing computation results.