New method for matrix completion with row and column similarities.
problem Matrix completion with row and column similarities.
method Iterative model selection with Hutchinson estimator.
result Effective model selection for optimal smoothing parameters.
iRDM selects unlabeled samples for regression without labels, improving model accuracy.
problem Selecting unlabeled samples for regression without label information.
method Iterative representativeness-diversity maximization (iRDM).
result iRDM significantly outperforms supervised ALR, especially with limited labeled samples.
A new method selects features for better model performance.
problem Improving model performance by selecting effective features.
method Supervised orthogonal least square regression with feature weighting.
result The method reduces feature dimensionality and improves classification results.
Paper uses RL to optimize multi-asset portfolios in fluctuating markets.
problem Optimizing multi-asset portfolios in time-varying financial markets.
method Soft Actor-Critic (SAC) algorithm for policy learning, policy iteration process.
result SAC algorithm outperforms in various criteria in simulated and real financial markets.
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model…
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
problem Recovering causal graphs in the presence of latent confounders and selection bias.
method Iterative causal discovery (ICD) algorithm that relies on causal Markov and faithfulness assumptions.
result Sound and complete algorithm that recovers the equivalence class of the underlying causal graph.
Unified dynamic approach for sparse model selection improves efficiency and accuracy.
problem Sparse model selection challenges in various fields.
method Iterative regularization path using Mirror Descent or Linearized Bregman Iterations.
result Path consistency theory with no false positives and minimax optimal error rate.
HyperImpute improves iterative imputation by automatically selecting models and hyperparameters.
problem Imputing missing values in datasets with variable model specifications.
method Generalized iterative imputation framework that adapts and configures models and hyperparameters automatically.
result Demonstrates superior imputation accuracy compared to benchmarks.
Optimizes subset selection in multiple linear regression models.
problem Choosing a subset of variables for regression models to balance fit and complexity.
method Developed mathematical programming models and algorithms for subset selection, tested with branch-and-bound and iterative heuristic approaches.
result Proposed models and algorithms efficiently find optimal or near-optimal solutions.
Solves complex machine learning problems with IRW method.
problem Problems with intractable sparsity-inducing norms in machine learning.
method Iteratively Re-Weighted (IRW) method with convergence guarantee.
result IRW method significantly outperforms alternative methods in robust feature selection.
Petridish efficiently searches neural architectures by iteratively adding shortcut connections.
problem Finding efficient neural architectures for various tasks.
method Iteratively adds shortcut connections to existing network layers, motivated by feature selection.
result Petridish efficiently finds competitive models with few GPU days.
Efficiently compress neural networks with MUSCO method.
problem Compression of deep neural networks.
method Iterative approach alternating low-rank factorization with rank selection and fine-tuning.
result Improves compression rate while maintaining accuracy.
ILTS iteratively refines linear regression models on subsets of data.
problem Linear regression with corruptions and outliers.
method Iteratively selects and refits the subset of samples with lowest loss.
result ILTS converges linearly to the closest mixture component under certain conditions.
New algorithm selects best preconditioner for iterative methods.
problem Choosing optimal preconditioner for solving linear systems.
method Sketching methods to estimate preconditioner stability.
result Provable method selects minimal stability preconditioner.
Self-training in linear models shows a U-shaped test-risk curve due to signal forgetting and denoising.
problem Understanding the dynamics of iterative self-training in high-dimensional linear regression.
method Derivation of deterministic-equivalent recursions for prediction risk and effective noise, analysis of signal forgetting and denoising effects.
result An optimal early-stopping time is determined, and a U-shaped test-risk curve is observed.
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property that is closed under conditioning and marginalization. By design, ancestral graphs encode precisely the conditional independence structures t…
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.
Greedy selection works well in a toy model of independent increments.
problem Iterative selection of maximum-value processes from i.i.d. stochastic processes.
method Fixed greedy selection at each stage.
result Optimal strategy is greedy selection under independent increments.
Self-paced learning selects tasks in a human-like progression for better multitask machine learning.
problem Improving multitask machine learning performance through effective task selection.
method Iterative selection of most appropriate tasks, learning task parameters, and updating shared knowledge using a bi-convex loss function.
result Self-paced task selection outperforms baseline methods in various multitask learning scenarios.
Proposes a privacy-preserving sign selection method for distributed systems.
problem Sign selection in distributed differentially private settings.
method Iterative peeling of stability function combined with exponential mechanism.
result Recovery of support and signs with optimal signal-to-noise ratio.
A reinforcement learning method predicts medical outcomes from masked feature vectors.
problem Predicting medical outcomes from limited feature data.
method Reinforcement learning with iterative feature selection and a guesser network.
result The method outperforms baselines and is highly interpretable.
A novel kernel approach for model selection in simulator-based models.
problem Model selection for simulator-based statistical models with limited prior knowledge.
method Iteratively updates model weights and parameters using Bayes' rule and kernel recursive ABC algorithm.
result Demonstrates effectiveness on dynamical systems in ecology and epidemiology.
Paper analyzes iterates in high-dimensional linear models and proposes estimators for their generalization error.
problem Analyzing iterates in high-dimensional linear models with comparable feature and sample sizes.
method Novel estimators for generalization error, debiasing corrections, and valid confidence intervals.
result Estimators are n \sqrt{n} n -consistent and can be used for early stopping. Improved SPSA-FSR method for feature selection and ranking in machine learning.
problem Feature selection and ranking in machine learning.
method Improved Simultaneous Perturbation Stochastic Approximation (SPSA) method with Barzilai and Borwein (BB) method for non-monotone iteration gains.
result Dramatically reduces the number of iterations required for convergence without impacting solution quality.
Adaptive l1-regularization controls short-selling in portfolio selection.
problem Financial markets' restrictions on short-selling and sparsity in portfolio solutions.
method Updating rule for l1-penalty parameter in Bregman iteration.
result Approach preserves properties of original l1-regularization and controls both sparsity and short positions.
Kernel Multigrid accelerates Back-fitting for additive Gaussian Processes.
problem Slow convergence of Back-fitting in training additive Gaussian Processes.
method Kernel Packets (KP) and Sparse Gaussian Process Regression (GPR) to enhance Back-fitting.
result Kernel Multigrid reduces the required iterations to O ( log n ) \mathcal{O}(\log n) O ( log n ) . RaSE ensemble framework improves sparse classification accuracy.
problem Sparse classification challenges in high-dimensional data.
method Random Subspace Ensemble (RaSE) framework with subspace selection via RIC.
result RaSE achieves low misclassification rates and accurate feature ranking.
Study portfolio selection with exogenous and endogenous transaction costs using deep learning.
problem Portfolio selection with both exogenous and endogenous transaction costs.
method Deep learning-driven policy iteration scheme for high-dimensional HJB equations.
result Proposes a scheme to address the curse of dimensionality and adapt to high-dimensional control spaces.
We propose a framework to perform streaming covariance selection. Our approach employs regularization constraints where a time-varying sparsity parameter is iteratively estimated via stochastic gradient descent. This allows for the regularization parameter to be efficiently learnt in an online manner. The proposed fram…
Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Many frequently-used techniques are based on independence screening; examples include correlation ranking (Fan and Lv, 2008) or feature selection using a two-sample t-test in high-dimension…
Pruning method removes less important features in linear models.
problem Removing less important features in linear models trained by gradient flow.
method Iterative Magnitude Pruning (IMP) applied to linear models trained by gradient flow.
result IMP prunes features with smallest projection onto the data.
Investor selects portfolios based on news attention in a hidden Markov model.
problem Mean-variance portfolio selection in a dynamic attention context.
method Closed-loop equilibrium strategies via extended HJB equation and Markov chain approximation.
result Equilibrium strategies found through iterative algorithm and numerical examples.
In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handle…
New sampler reduces MCMC complexity for Bayesian variable selection.
problem High-dimensional Bayesian variable selection with high computation complexity.
method Variable-complexity subset weighted-Tempered Gibbs Sampler (wTGS) with Rao-Blackwellized estimator.
result Variances of Rao-Blackwellized estimator are smaller than those of subset wTGS.
A stability metric compares feature selection algorithms in machine learning.
problem Stability of feature selection algorithms in machine learning.
method Rank-based instability index to compare MDA, LIME, and SHAP algorithms.
result LIME and SHAP are more stable than MDA, with LIME being best for human interpretability.
Two algorithms approximate CMI for feature selection, improving classifier model development.
problem Estimating MI for feature subsets is hard and combinatorial.
method Truncated Power Method (TPower) and Low Rank Bilinear Approximation (LowRank).
result Approximations to NP-hard CMI feature selection are very effective.
Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while ob…
FWSR selects k exemplars from data sets efficiently.
problem Selecting representative data points for learning tasks.
method Kernelizable Frank-Wolfe Sparse Representation (FWSR) algorithm.
result FWSR achieves linear convergence and outperforms other methods in speed and accuracy.
FLOP algorithm speeds up causal structure learning for linear models.
problem Efficiently learning causal structures from discrete data.
method FLOP algorithm combines fast parent selection and iterative score updates.
result FLOP finds highly accurate causal structures with near-perfect recovery.
Bayesian approach to selecting data for machine learning.
problem Iterative data selection in machine learning algorithms.
method Embedding data selection into decision theory and deriving Bayes-optimal criteria.
result Mitigates confirmation bias in data selection.
A new variable selection method using model-based boosting and random permutations.
problem Sparse and fast variable selection in high-dimensional data.
method Model-based gradient boosting with randomly permuted variables to stop early.
result Competes with state-of-the-art methods in high-dimensional classification.
Algorithm recovers causal graphs from data with fewer tests.
problem Recovering causal graphs from observational data with latent confounders and selection bias.
method Iterative CI tests with increasing condition set sizes.
result Significantly fewer CI tests and condition sets compared to FCI.
PFBP algorithm speeds up feature selection in big data.
problem Feature selection in high-dimensional and/or large sample size data.
method PFBP algorithm partitions data and uses local computations with early decisions.
result Asymptotic optimality for causal networks, super-linear speedup, linear scalability.
A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a χ 2 χ^2 χ 2 test and a regularization. After a relatively s…
Sparser Random Feature Models via IMP (ShRIMP) efficiently learns sparse models for high-dimensional data.
problem Learning sparse models for high-dimensional data with sparse variable dependencies.
method Iterative Magnitude Pruning applied to Random Feature Models.
result ShRIMP achieves better or competitive test accuracy compared to state-of-the-art methods.
Optimal Biweight kernel and computationally efficient Epanechnikov kernel for modal linear regression.
problem Finding the best kernel for modal linear regression.
method Refined analysis of asymptotic statistical behavior and IRLS algorithm convergence.
result Biweight kernel minimizes asymptotic mean squared error, Epanechnikov kernel guarantees IRLS convergence.
Dash selects dynamic pseudo labels from unlabeled data for semi-supervised learning.
problem Efficiently using unlabeled data in semi-supervised learning while avoiding incorrect pseudo labels.
method Dynamic thresholding to select a subset of unlabeled examples for training.
result Dash achieves theoretical convergence and outperforms state-of-the-art methods empirically.
Deep RL solves combinatorial selection problems with large item spaces.
problem Solving MDPs with large state and action spaces, especially for combinatorial selection.
method Convert S-MDP to IS-MDP, use weight-shared Q-networks to manage state space explosion.
result Our approach effectively handles large item spaces and scales to diverse environments.