NGP selects N features from P using neural networks in a greedy, iterative process.
problem Feature selection for non-linear prediction problems.
method Neural Greedy Pursuit (NGP) algorithm, selecting features sequentially in an iterative loss minimization procedure.
result NGP provides better performance than DeepLIFT and Drop-one-out loss methods.
The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.
problem Ensuring reliability of feature selection after domain adaptation in high-dimensional regression.
method Proposes a novel method to test features selected by SeqFS-DA with controlled FPR.
result The proposed method controls FPR below a significance level α (e.g., 0.05) and enhances statistical power. Algorithm clusters items by sequentially selecting features, minimizing observations.
problem Clustering items based on bandit feedback with many features.
method Sequential Halving algorithm for feature selection.
result Accurate recovery of item partition with minimal observations.
A new feature selection method using attention for neural networks.
problem Feature selection for neural networks with a budget constraint.
method Sequential Attention: greedy forward selection with attention weights.
result Achieves state-of-the-art results for neural networks.
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.
This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between …
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets t…
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.
OOMP selects features online for sparse linear regression.
problem Feature selection in high-dimensional sparse linear models.
method Online algorithm that alternates between feature selection and coefficient estimation.
result Theoretical guarantees and computational complexity analysis of OOMP.
GIB improves neural network generalization by dynamically selecting task-relevant features across different sequential environments.
problem Poor generalization of deep neural networks to unseen environments.
method Proposes a gated information bottleneck (GIB) approach that dynamically drops spurious correlations and selects task-relevant features.
result GIB outperforms other IB approaches in adversarial robustness and OOD detection.
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.
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…
One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of …
Feature selection is frequently used as a pre-processing step to machine learning. It is a process of choosing a subset of original features so that the feature space is optimally reduced according to a certain evaluation criterion. The central objective of this paper is to reduce the dimension of the data by finding a…
Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when …
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.
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
This manuscript presents the following: (1) an improved version of the Binary Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature selection in machine learning (Aksakalli and Malekipirbazari, Pattern Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains computed via the Barz…
Hybrid model for online nonlinear prediction using LSTM and soft GBDT.
problem Online nonlinear prediction with manual feature selection and model selection issues.
method End-to-end architecture with LSTM for feature extraction and soft GBDT for regression, jointly optimized.
result Significant performance improvements over conventional methods on real datasets.
Feature selection (FS) has become an indispensable task in dealing with today's highly complex pattern recognition problems with massive number of features. In this study, we propose a new wrapper approach for FS based on binary simultaneous perturbation stochastic approximation (BSPSA). This pseudo-gradient descent st…
SFSVI uses Gaussian mixtures to approximate neural network outputs for continual learning.
problem Learning new tasks without forgetting old ones in neural networks.
method Sequential function-space variational inference with Gaussian mixture approximation.
result Gaussian mixture SFSVI outperforms other methods in continual learning.
New methods optimize experiment selection for sequential data, improving model accuracy.
problem Optimizing experiment selection for sequential data in multidimensional cases.
method Adopting greedy experiment selection methods for maximum likelihood estimation.
result Proposed methods produce consistent and asymptotically normal estimators.
Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an l0-constrained least-squares problem. The Orthogonal Least-Squares (OLS) algorithm sequentially selects the features (i.e., columns of the coefficient matrix) to greedil…
We identify and analyze selection structure in sequential data.
problem Selection biases in sequential data can distort analysis and hide underlying generation processes.
method Nonparametric identifiability of selection structure without interventional experiments.
result Selection structure is identifiable in sequential data without parametric assumptions.
In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are amenable to optimization via the standard greedy algorithm. Despite its desirab…
New method reduces memory usage for high-dimensional variable selection.
problem Scalability issues in high-dimensional variable selection, especially in genomics.
method Adaptive sampling of null features to eliminate dummy matrix materialization.
result Reduces memory and runtime by several orders of magnitude while preserving FDR control.
This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive po…
Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.
problem Learning optimal arm selection in unsupervised sequential selection with contextual information.
method Proposes an algorithm for the contextual USS problem under the CWD property, demonstrating sub-linear regret.
result Demonstrates sub-linear regret for the proposed algorithm.
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large poo…
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this lim…
PyHHMM is a Python library for HHMMs with advanced features.
problem Handling heterogeneous observation models and missing data in HMMs.
method Object-oriented Python implementation with advanced features.
result PyHHMM supports a heterogeneous observation model and missing data inference.
With the widespread use of the internet, the size of the text data increases day by day. Poems can be given as an example of the growing text. In this study, we aim to classify poetry according to poet. Firstly, data set consisting of three different poetry of poets written in English have been constructed. Then, text …
New algorithm for clustering data streams with no substitutions.
problem Clustering sequential data with no point substitution.
method Careful risk estimation, linear bin division, multiscale center selection.
result First constant approximation algorithm for random-order k-median clustering.
Unified statistical framework for LSTM model selection.
problem Model selection and hyperparameter tuning in LSTM networks is heuristic and computationally expensive.
method Proposes a statistical framework extending classical model selection ideas to LSTM networks.
result Improved performance of the proposed framework demonstrated on biomedical data.
Thompson Sampling tackles USS, a sequential selection problem without feedback.
problem Unsupervised Sequential Selection (USS) problem with fixed costs and ordered arms.
method Thompson Sampling algorithm for USS problem.
result Thompson Sampling achieves near optimal regret and better performance than existing algorithms.
CatNet controls FDR in LSTM models using SHAP feature importance and Gaussian mirrors.
problem Controlling False Discovery Rate (FDR) in LSTM models with feature selection.
method CatNet uses SHAP values for feature importance and Gaussian Mirror algorithm for FDR control. It introduces a kernel-based independence measure to handle feature correlations.
result CatNet reduces overfitting and improves model interpretability on simulated and real-world data.
Improved online penalty selection for time series models.
problem Efficiently selecting penalty parameters for lasso in time series models.
method Enhanced autoregressive model with online penalty selection.
result Significantly improved computational performance and forecast accuracy.
mNARX+ creates accurate surrogate models for complex systems without requiring domain expertise.
problem Creating accurate surrogate models for complex dynamical systems without extensive domain knowledge.
method Data-driven, recursive algorithm that automatically selects temporal features and their causal ordering.
result Automatically identifies critical auxiliary quantities and their order for accurate modeling.
EviTrack improves sequential prediction in delayed disambiguation scenarios.
problem Challenges in sequential prediction with delayed disambiguation where early observations are ambiguous.
method EviTrack operates over latent trajectories, applying evidence- and likelihood-ratio-based selection to delay commitment until supported by data.
result EviTrack outperforms sampling-based baselines in a controlled synthetic benchmark, achieving faster post-disambiguation recovery.
We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way. In this work, we formulate this active feature acquisition problem as a reinforcement learning problem, and provide a novel fr…
MP-Boost boosts accuracy faster and more interpretable than AdaBoost.
problem Developing a faster, more interpretable boosting method.
method Adaptive selection of minipatches (small subsets of instances and features) at each iteration.
result Achieves comparable accuracy to AdaBoost and gradient boosting but faster and more interpretable.
A framework for optimizing prompt selection in generative language models.
problem Efficiently selecting prompts for generative language models.
method Two-stage framework using simulation optimization to maximize a pre-defined score.
result Consistency of the sequential evaluation procedure in the proposed framework.
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.
Novel algorithm reduces feature inclusion in online decision-making.
problem Optimizing decision-making for personalized user experiences with fairness.
method Online Batched Sequential Inclusion (OBSI) algorithm for sequential feature inclusion.
result OBSI outperforms other algorithms in terms of regret, relevance of features, and compute.
We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening methods,…
Data-driven model selection reduces regret in sequential decisions.
problem Optimizing model selection in stochastic environments with bandit feedback.
method Data-driven regret balancing for model selection.
result Meta-learner selects the best base learner based on actual realized regret.
RAMPART ranks top-k features more accurately than existing methods.
problem Accurate ranking of important features in machine learning.
method Adaptive sequential halving strategy combined with ensembling techniques.
result RAMPART achieves the correct top-k ranking with high probability.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
problem Reduces cumulative regret in sequential query recommendations for closed loop interactive learning settings.
method Proposes a max-utility arm selection strategy based on the maximum utility of arms.
result Improves cumulative regret substantially compared to baseline algorithms and random selection.