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.
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.
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…
Develops methods to select informative conformal prediction sets with FCR control.
problem Selecting informative prediction sets with FCR control in supervised learning.
method Unified framework for informative conformal prediction sets with FCR control.
result First procedures providing FCR control for informative prediction sets.
A new framework selects information sources to test hypotheses robustly, even with misclassifications.
problem Robust hypothesis testing with misclassification penalties.
method Introduces a misclassification penalty framework and an efficient greedy algorithm.
result Proposes a submodular surrogate metric for better selection.
Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximizing mutual information between subsets of features and class labels. Practical methods are forced to rely on approximations due to the diffi…
A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
problem Selecting models for ensemble learning to improve performance and reduce correlation issues.
method Formulate budgeted ensemble selection as maximizing mutual information, use Gaussian-copula to model correlated errors, propose a greedy mutual-information selection algorithm.
result Our method consistently outperforms strong baselines across multiple datasets.
Investigates the use of Information Coefficient as a stock selection model performance measure.
problem The adequacy and effectiveness of Information Coefficient (IC) for evaluating stock selection models is unclear.
method Simulation and simple statistical modeling to examine IC behavior statically and dynamically.
result Proposes two practical procedures for IC-based ongoing performance monitoring of stock selection models.
A new method for dynamic feature selection outperforms existing approaches.
problem Sequentially selecting features based on current information in machine learning.
method Greedy selection of features based on conditional mutual information, combined with a learning approach for optimization.
result The method outperforms existing feature selection methods in experiments.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
MICO uses mutual information co-training to improve selective search efficiency.
problem Efficiently search and route unseen queries in large-scale search systems.
method Mutual Information Co-training framework for selective search with minimal supervision.
result Significantly improves performance on multiple metrics of selective search.
Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied. On the other hand, model selection based on parameter shrinkage by ℓ1-type penalties is computationally efficient. In this paper we make an attempt to combine their st…
Optimal subset selection for hypothesis testing with penalties.
problem Optimal subset selection of information sources for hypothesis testing with misclassification penalties.
method Proposes a misclassification penalty framework and studies two variants of subset selection problems under centralized Bayesian learning.
result Proves the submodularity of the objective and constraints of the subset selection problems and establishes performance guarantees for greedy algorithms.
We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the…
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction. This allows the creation of compact models that are easier to interpret. Most of these techniques work over the whole dataset, b…
Feature selection aims to select the smallest feature subset that yields the minimum generalization error. In the rich literature in feature selection, information theory-based approaches seek a subset of features such that the mutual information between the selected features and the class labels is maximized. Despite …
A neural network approach for feature selection using mutual information.
problem Feature ranking and selection leading to sub-optimal solutions for class separability.
method Stochastic mutual information gradient estimation for dimensionality reduction.
result The network projects features onto an output space maximizing mutual information with class labels.
Proposes SNML for selecting word2vec Skip-gram dimensionality.
problem Selecting optimal dimensionality for word2vec Skip-gram models.
method Information criteria (AIC, BIC, SNML) applied to SG and SG Negative Sampling models.
result SNML outperforms AIC and BIC, selecting closer optimal dimensionality.
Mutual Information (MI) is often used for feature selection when developing classifier models. Estimating the MI for a subset of features is often intractable. We demonstrate, that under the assumptions of conditional independence, MI between a subset of features can be expressed as the Conditional Mutual Information (…
Value selection reduces model size while maintaining accuracy.
problem Space efficiency in model size reduction.
method Two probabilistic methods based on information theory's metric: PVS and P + VS.
result Value selection achieves balance between accuracy and model size reduction.
Top-two algorithm improved for best-k-arm selection.
problem Best-k-arm identification in multi-armed bandits.
method Information-directed selection based on dual variables.
result Top-two Thompson sampling with IDS is asymptotically optimal.
Paper designs a penalty for model order selection using information criteria.
problem Selecting the correct model order from a set of candidate models.
method Designs a penalty for the generalized information criterion (GIC) to minimize underestimation.
result Optimal penalty minimizes underestimation while keeping overestimation below a specified level.
Novel approach uses neural networks to enhance CI testing for feature selection.
problem Challenges in implementing Markov blanket feature selection due to CI testing limitations.
method Two-step approach: feature mapping followed by CI testing using k-NN. result The method boosts CI testing performance, leading to improved feature selection.
SIC detects elbows in error curves automatically.
problem Automatic elbow detection in error curves.
method Spectral information criterion (SIC) extracts geometric features of error curves.
result SIC provides a subset of models with smaller cardinality than total possible models.
Paper shows noisy labels can improve PLR variable selection.
problem Variable selection in PLR is challenging due to noisy labels.
method Proposes a novel ADMM-based algorithm to fuse noisy labels.
result Fused noisy labels improve PLR performance in estimation and classification.
In this paper, we present and illustrate some new tools for rigorously analyzing training data selection methods. These tools focus on the information theoretic losses that occur when sampling data. We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce…
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As a…
Combines historical and market data for better portfolio selection.
problem Improving portfolio selection through diverse information integration.
method Bayesian learning via Gaussian mixture model to harmonize historical and market data.
result The method enhances forecasting accuracy and robustness across various capital markets.
New causal measures improve feature selection in AI models.
problem Lack of causal interpretability in AI models.
method Introduces causal entropy and causal information gain to assess feature control.
result Demonstrates superiority of causal information gain in feature selection.
Accurate model selection is a fundamental requirement for statistical analysis. In many real-world applications of graphical modelling, correct model structure identification is the ultimate objective. Standard model validation procedures such as information theoretic scores and cross validation have demonstrated poor …
A new criterion selects models in overparameterized settings.
problem Model selection for overparameterized models with more parameters than data.
method Establishes Bayesian duality and introduces the Interpolating Information Criterion.
result The Interpolating Information Criterion selects models in overparameterized settings.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
problem Variable selection in high-dimensional data with high accuracy and stability.
method Subsample-ordered least-angle regression (solar) and its coordinate descent generalization (solar-cd) using L0 norm solution path averaging. result Solar selects variables with high accuracy and stability, reducing redundant variable selection.
Sparse GEMINI selects relevant features for clustering without assumptions.
problem Feature selection in clustering with relevant clusters and variables.
method Discriminative clustering model maximizing GEMINI with l1 penalty.
result Sparse GEMINI selects relevant subsets of variables without prior hypotheses.
Introduces top-k regularization for better feature selection in machine learning.
problem Limited ability of existing feature selection methods to reconcile feature representativeness and inter-correlations.
method Top-k regularization, which induces a sub-architecture on the model's architecture to select informative features and model complex relationships. result Uniform approximation error bound for top-k regularization approximating high-dimensional sparse functions. 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.
Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information los…
The paper solves portfolio selection using Rényi divergence and optimization.
problem Single-period portfolio selection under CRRA utility.
method Information-theoretic lens, Rényi divergence, Rényi entropy, Blahut-Arimoto-style alternating optimization.
result CRRA portfolio selection is equivalent to a Rényi information-projection problem.
The paper proposes selective forgetting for deep neural networks at a finer level than samples.
problem Selective forgetting of deep neural networks to handle outliers, poisoned data, or sensitive information.
method Formulated selective forgetting at a finer level than samples, introduced as an optimization problem on three criteria.
result Experimental results show the model can forget specific information for classification, improving accuracy in specific cases.
Cost-efficient feature selection for multi-label classification in medicine.
problem Feature selection in multi-label classification with cost constraints.
method Sequential feature selection maximizing conditional mutual information, followed by cost-free feature selection using shadow features.
result The method effectively reduces prediction costs in medical applications.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection
problem Estimating parameters in time series
method Composite likelihood method
result The method reduces computational cost
Feature selection has been proven a powerful preprocessing step for high-dimensional data analysis. However, most state-of-the-art methods tend to overlook the structural correlation information between pairwise samples, which may encapsulate useful information for refining the performance of feature selection. Moreove…
Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods. In this work, we develop a theoretical framework that allows obtaining the true feature ordering of two-dimensional sequential forward feat…
FisherSFT selects informative examples to fine-tune LLMs efficiently.
problem Adapting large language models to new domains efficiently.
method Selects examples maximizing information gain using Hessian of log-likelihood.
result Empirically demonstrates improved performance with reduced computational cost.
In this work we present a review of the state of the art of information theoretic feature selection methods. The concepts of feature relevance, redundance and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework i…
Proposes an active RBI framework using Rényi information measures for more informed decision-making.
problem Optimal latent variable estimates in real-time settings with streaming noisy observations.
method Unified inference and query selection steps through Rényi entropy and α-divergence; new objective called Momentum for exploration.
result Analytically demonstrates superior performance compared to conventional methods like mutual information.
Proposes a method to select features for subgroup datasets with systematic missing data.
problem Feature selection for datasets with subgroup structure and systematic missing data.
method Develops a heterogeneous graph neural network to propagate information between feature-subgroup-target variable connections.
result Demonstrates improved feature selection performance and scalability.
MIC improves VAR order selection accuracy.
problem Order selection in VAR models for accurate forecasting.
method MIC based on expected squared error loss.
result MIC consistently estimates true VAR order.