This paper uses information theory to improve risk modeling in big data.
problem Insufficient application of information theory in actuarial science.
method Explores information theory to uncover performance limits of insurance big data systems.
result Guidance for risk modeling and actuarial pricing systems.
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.
New framework extracts useful information from tensor data with structural properties.
problem Extract useful information from tensor data with structural properties.
method Proposed an additive tensor decomposition (ATD) framework and an ADMM algorithm to solve the high dimensional optimization problem.
result Versatile and effective framework demonstrated in simulations and real medical image analysis.
Extends machine learning model training to include validation data.
problem Machine learning validation data usage in training.
method Extends predictive information bottleneck to include validation data.
result Validation data improves model training efficiency.
New concept of epiplexity quantifies useful information from data.
problem Understanding useful information content from data without unlimited computational capacity.
method Introducing epiplexity, a measure of information computationally bounded observers can learn.
result Epiplexity captures useful information content, not just randomness.
EvoRate metric assesses learnability of sequential data by measuring predictive information.
problem Model misspecification due to misinterpreting patterns in sequential data.
method Predictive information framework based on mutual information between past and future.
result Temporal patterns fundamentally constrain learnability; optimal predictors cannot outperform intrinsic information limit.
We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate …
MFNets constructs efficient multifidelity surrogates from diverse information sources.
problem Creating accurate surrogates from multiple, potentially costly or inaccurate data sources.
method Directed acyclic graph of connections, gradient-based minimization of least squares objective, flexible information source structure.
result Error reduction by orders-of-magnitude, especially in low-data scenarios.
CVIB uses information theory to learn counterfactuals from MNAR data without RCTs.
problem Debiasing learning from missing-not-at-random (MNAR) data in recommendation systems.
method CVIB, a variational information bottleneck, separates task-aware mutual information into factual and counterfactual parts.
result CVIB significantly enhances both shallow and deep models in recommendation systems.
Generative AI amplifies data without increasing information, with a mathematical limit.
problem Data amplification without information gain.
method Information theoretic concepts applied to GAN-generated data.
result A mathematical bound on gain is established, showing data can be amplified without increasing information content.
Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA is how to pre…
A new framework for information theory considers computational constraints.
problem Understanding information in complex systems with computational limitations.
method Variational extension of Shannon's information theory with computational constraints.
result Predictive V-information can be created through computation and reliably estimated from data. We introduce a new framework for unsupervised learning of representations based on a novel hierarchical decomposition of information. Intuitively, data is passed through a series of progressively fine-grained sieves. Each layer of the sieve recovers a single latent factor that is maximally informative about multivariat…
We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-con…
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
The study uses a ReLU network to discern geometric structure in data via the Data Information Matrix.
problem Understanding the geometric structure of real data in high-dimensional spaces.
method Employing a ReLU neural network trained as a classifier and the Data Information Matrix (DIM) to discern a singular foliation structure.
result The singular points of the foliation are measure zero, and a local regular foliation exists almost everywhere.
Bayesian method mitigates negative transfer in unknown source data.
problem Negative transfer in transfer learning where target performance worsens after source data consideration.
method Proxy-informed robust method for probabilistic transfer learning (PROMPT).
result Negative transfer can be mitigated without prior knowledge of source data.
Paper analyzes trade-offs between fairness, privacy, and accuracy using Chernoff Information.
problem The relationship between fairness and privacy in machine learning.
method Utilizes Chernoff Information to characterize trade-offs, proposes Chernoff Difference and Noisy Chernoff Difference, develops CINE for neural estimation.
result Shows three distinct behaviors of Noisy Chernoff Difference based on data distribution.
New method handles missing data in multimodal brain imaging.
problem Missing data in multimodal brain imaging.
method Full Information Linked ICA (FI-LICA) algorithm.
result FI-LICA outperforms current practices in classification and prediction.
Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advantages of information planning for text data by focusing on three supervised models: Naive Bayes, supervised LDA and deep neural networks. We s…
This work tackles online memory selection in continual learning using information theory.
problem Online selection of a representative replay memory from data streams.
method Information-theoretic criteria (surprise, learnability) and Bayesian model for efficient computation.
result InfoRS improves robustness against data imbalance compared to reservoir sampling.
TIPRDC anonymizes data features to protect privacy while retaining useful information.
problem Privacy concerns from crowdsourced data hinder deep learning applications.
method Hybrid training method combining adversarial and mutual information estimation.
result Feature extractor hides private information while preserving original data features.
Model selection in clustering requires (i) to specify a suitable clustering principle and (ii) to control the model order complexity by choosing an appropriate number of clusters depending on the noise level in the data. We advocate an information theoretic perspective where the uncertainty in the measurements quantize…
User releases data to service provider while balancing privacy and utility.
problem Balancing user privacy and service utility in data release.
method Formulated as a Markov decision process (MDP) and solved using deep reinforcement learning (RL).
result Achieved a trade-off between revealing useful information and protecting sensitive data.
Embeddings leak sensitive information about input data, which can be recovered or inferred.
problem Information leakage in embedding models.
method Developed three classes of attacks to study information leakage.
result Embeddings leak sensitive information about input data, which can be partially recovered or inferred.
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…
Improved bounds for SGLD via data-dependent estimates.
problem Improving generalization bounds for noisy iterative learning algorithms.
method Variational characterization of mutual information and data-dependent priors.
result Significantly improved mutual information bounds for SGLD.
The paper uses geometric methods to classify medical data histograms.
problem Classifying medical data histograms for disease diagnosis.
method Information geometry of beta distributions for comparing and classifying histograms.
result Geometric tools, particularly negatively curved Fisher information, enable unique mean calculation and K-means classification.
LASLA improves multiple testing accuracy with network-structured data.
problem Efficiently incorporating complex side information into multiple testing.
method Locally adaptive structure learning algorithm (LASLA) integrating auxiliary information.
result LASLA asymptotically controls FDR and enhances power with side information.
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined…
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.
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.
Extends information bottleneck to multi-view unsupervised learning.
problem Identifying superfluous information in unlabeled multi-view data.
method Multi-view information bottleneck model, leveraging data augmentation.
result State-of-the-art results on Sketchy and MIR-Flickr datasets.
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
problem Informative labels can bias semi-supervised learning models, especially when some classes are more likely to be labeled.
method Estimates missing-data mechanism and uses inverse propensity weighting to debias SSL algorithms.
result Proposed methods improve SSL performance, demonstrated on various datasets including medical ones.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
problem Challenges in learning graph representations due to structure and feature information.
method GIB is an information-theoretic principle that balances expressiveness and robustness by maximizing mutual information between representation and target, while constraining mutual information with input data.
result GIB-based models are more robust to adversarial attacks, achieving up to 31% improvement.
Improves inference from sparse data with hybrid summary statistics.
problem Robust simulation-based inference from limited data.
method Augment traditional summary statistics with neural network outputs to maximize mutual information.
result Improves information extraction and makes inference robust in low-data settings.
Whatever information a deep neural network has gleaned from training data is encoded in its weights. How this information affects the response of the network to future data remains largely an open question. Indeed, even defining and measuring information entails some subtleties, since a trained network is a determinist…
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
problem Missing labels in semi-supervised learning can be informative, improving classifier performance.
method Formulates missingness as a mixture model problem and uses EM algorithm for fitting.
result Modelling informative missingness can yield a classifier with smaller expected error than a completely labelled sample.
Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, d…
Proposes dynamic borrowing method for historical data in clinical trials.
problem Insufficient statistical power in rare and pediatric disease clinical trials.
method Dynamic borrowing method based on frequentist approach using similarity measures.
result Demonstrates usefulness of dynamic borrowing in reanalyzing clinical trial data.
Proposes a method to compute information theory measures via Gaussianization.
problem Challenges of computing information from multidimensional data.
method Indirect computation using a multivariate Gaussianization transform.
result Proposed methods outperform existing estimators, especially in high dimensions.
Feature selection, which searches for the most representative features in observed data, is critical for health data analysis. Unlike feature extraction, such as PCA and autoencoder based methods, feature selection preserves interpretability, meaning that the selected features provide direct information about certain h…
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.
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
problem Detecting and filtering noisy or mislabeled data in deep learning models.
method A mutual information-based framework quantifying statistical dependencies between inputs and labels.
result The method effectively filters low-quality samples, improving classification accuracy by up to 15%.
This work develops a model to distinguish network and covariate information.
problem Identifying unique network and covariate information.
method Low-rank model with two-step estimation: spectral method followed by refinement.
result The method accurately recovers joint and individual components.
Effectively modelling hidden structures in a network is very practical but theoretically challenging. Existing relational models only involve very limited information, namely the binary directional link data, embedded in a network to learn hidden networking structures. There is other rich and meaningful information (e.…
New framework ties learning algorithms to data recognition using CMI.
problem Understanding how well machine learning models generalize from training data.
method Information-theoretic framework using Conditional Mutual Information (CMI).
result Bounds on CMI imply various forms of generalization.
ALINE optimizes Bayesian inference and data acquisition by strategically querying informative data.
problem Strategic acquisition of informative data for Bayesian inference in challenging tasks.
method Unified framework combining amortized Bayesian inference and active data acquisition using a transformer architecture trained via reinforcement learning.
result ALINE delivers both instant and accurate inference along with efficient selection of informative points.