Sparse reduced-rank regression selects variables and ranks via manifold optimization.
problem Traditional rank selection fails when true rank is high.
method Sparse regularization and manifold optimization for rank and variable selection.
result Accurate estimation of coefficient parameter with high true rank.
The paper introduces a framework to select efficient datasets for preserving model rankings.
problem Efficient evaluation of machine learning models on small, representative datasets.
method Bootstrap aggregation, clustering, design criteria, random baselines, and greedy farthest-first (FAFI).
result Several selection strategies improve rank preservation compared to random subsets, especially in time series classification.
Paper tackles fair low-rank approximation and column subset selection.
problem Minimize loss over sub-populations in machine learning.
method Developed algorithms for fair low-rank approximation and fair column subset selection.
result Achieved polynomial time algorithms for fair low-rank approximation.
Optimizes tensor rank selection for neural network compression.
problem Finding optimal tensor rank for regression models.
method Analyzes population expressions for training-testing discrepancy under Gaussian design.
result Optimal rank minimizes prediction error and aligns with cross-validation.
A new metric for stable model selection in CATE prediction.
problem Model selection in conditional average treatment effect (CATE) prediction.
method Analysis of model performance ranking and formulation of a novel metric.
result Our metric outperforms existing metrics in model selection and hyperparameter tuning.
New method predicts cancer drug rankings based on genomic data.
problem Selecting the right drugs for cancer patients.
method pLETORg method that predicts drug ranking structures using latent vectors.
result pLETORg significantly outperforms state-of-the-art methods in prioritizing new sensitive drugs.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
problem Determining optimal decomposition ranks in tensor decompositions.
method MARS uses binary masks to learn optimal tensor structure during training via relaxed MAP estimation.
result MARS achieves better results than previous methods in various tasks.
The paper tackles learning true rankings from noisy, incomplete data.
problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.
New algorithms select and rank features from MTS without feature extraction.
problem Feature extraction step for MTS classification.
method Directly computes similarity between time series and assesses cluster structure matching labels.
result Techniques match labels well without feature extraction.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
problem Understanding the effectiveness of tree-based methods in finite-sample settings, especially symbolic feature selection.
method Local ranking perspective, finite-sample analysis, oracle bounds, posterior contraction results, concordant divergence statistics.
result New insights and statistics for evaluating symbolic feature mappings.
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.
Bayesian framework for optimal sampling and selection in ranking problems.
problem Optimal sampling and selection in statistical ranking and selection.
method Formulated as a stochastic control problem, derived Bellman equation, value function approximation for optimal policy.
result Approximately optimal allocation policy with one-step-ahead and asymptotic optimality for independent normal distributions.
Truncated Singular Value Decomposition (SVD) calculates the closest rank-k approximation of a given input matrix. Selecting the appropriate rank k defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization prob…
New graph-based method selects outlier ensemble components.
problem Poor components negatively affect consensus results in outlier ensembles.
method Mapping rankings to graphs, mining to identify subsets.
result Our method outperforms state-of-the-art techniques.
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.
Unified framework for ranking-and-selection with multiple correct answers and non-answerable estimates
problem Fixed-precision ranking-and-selection in structured settings with non-unique answers and non-answerable estimates
method Unified framework based on answer-wise acceptance sets, restricted generalized likelihood ratio stopping, and answer-pitfall decomposition
result Unified recipe performs well across a broad range of pure-exploration problems
We develop an efficient algorithm for low-rank approximation with improved approximation guarantees.
problem Optimal low-rank approximation of matrices with ℓ1 norm constraints. method Polynomial time column subset selection-based algorithm achieving ildeO(k1/2)-approximation. result Improved approximation guarantees for ℓ1 low-rank approximation. Extends feature selection to GNNs, improving accuracy and feature ranking.
problem Improving feature selection in Graph Neural Networks (GNNs).
method Implemented a feature selection algorithm using Gumbel Softmax for ranking and selecting features in GNNs.
result Selected 225 features out of 1433 for the Cora dataset, improving classification accuracy.
This paper studies simultaneous feature selection and extraction in supervised and unsupervised learning. We propose and investigate selective reduced rank regression for constructing optimal explanatory factors from a parsimonious subset of input features. The proposed estimators enjoy sharp oracle inequalities, and w…
RI-based variable ranking and selection outperforms lasso in high-dimensional datasets.
problem Challenges in variable selection and model creation with correlated predictors.
method RI measures for feature ranking and selection, including CRI.Z.
result RI-based methods outperform lasso in high-dimensional datasets, especially with correlated predictors.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
problem Optimizing chemical compounds using traditional regression models.
method Introducing Rank-based Bayesian Optimization (RBO) using ranking models.
result RBO outperforms regression-based BO, especially for rough landscapes and activity cliffs.
Investigates portfolio selection for rank-dependent utilities in incomplete markets.
problem Portfolio selection for agents with rank-dependent utility in incomplete financial markets.
method Characterizes deterministic strict equilibrium strategies for constant-coefficient and time-invariant probability weighting functions. Addresses the issue of selecting an optimal strategy from multiple equilibrium strategies for time-variant probability weighting functions.
result Characterizes deterministic strict equilibrium strategies and identifies optimal strategies from multiple equilibrium strategies.
Proposes a novel feature selection method for hypergraphs.
problem The 'curse of dimensionality' problem in feature selection.
method Unsupervised hypergraph feature selection via point-weighting and low-rank representation.
result Significant improvement over state-of-the-art feature selection methods.
New unsupervised feature selection method for imbalanced datasets.
problem Feature selection challenges in imbalanced multi-class datasets.
method Distance Rank Score using Spearman's Rank Correlation.
result Outperforms existing methods on clustering problems.
Graph-based method ranks features using Eigenvector Centrality for feature selection.
problem Feature selection in high-dimensional data.
method Mapping features onto an affinity graph and ranking nodes based on Eigenvector Centrality.
result The method identifies effective features for classification, outperforming other methods in accuracy, stability, and speed.
This paper evaluates various loss functions for Transformer models in stock ranking.
problem Evaluating loss functions for Transformer models in stock ranking.
method Systematic evaluation of advanced loss functions (pointwise, pairwise, listwise) on S&P 500 data.
result Different loss functions impact a model's ability to discern profitable relative orderings among assets.
The paper improves PCS approximation for ranking and selection under limited simulation budgets.
problem Improving finite sample performance in Ranking and Selection.
method Develops a Bahadur-Rao type expansion for PCS, proposes a novel FCBA policy.
result FCBA policy achieves superior PCS performance compared to traditional methods.
Improves personalized treatment selection using covariates.
problem Ranking and selecting the best alternative based on covariates.
method Linear model for covariate effects, two-stage procedures for error types, generalized slippage configuration.
result Procedures provide statistical guarantees for correct selection.
Myopic procedures are shown to be asymptotically optimal in ranking and selection problems.
problem Selecting the best design from a set with unknown mean performance.
method Myopic procedures that iteratively improve an approximation of the objective measure.
result Myopic procedures satisfy optimality conditions of ranking and selection problems.
The study builds a customer selection model grouping and ranking customers based on multiple dimensions.
problem Traditional grouping methods based on assets are insufficient and ineffective.
method K-means unsupervised learning for grouping, weighted customer value calculation for ranking.
result Differentiates and ranks customers based on their values, not just assets.
Efficient tensor completion method using rank minimization on TR latent space.
problem High model sensitivity and exponential model possibilities in TR decomposition.
method Nuclear norm regularization on latent TR factors, ADMM scheme.
result Superior performance and efficiency compared to state-of-the-art algorithms.
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random…
Introduces greedy feature selection for classifier-dependent feature ranking.
problem Feature selection for classification tasks.
method Greedy feature selection, identifying the most important feature at each step based on the selected classifier.
result Theoretical and numerical benefits of greedy feature selection.
New method selects features via tensor decomposition and submodular optimization.
problem Feature selection for high-dimensional data.
method Low-rank tensor model, submodular optimization, greedy algorithm.
result Proposed method outperforms state-of-the-art feature selection.
Proposes new listwise learning-to-rank models to address rating ties and document relevance.
problem Rating ties and document relevance in existing listwise learning-to-rank models.
method Models ranking as selecting documents from a candidate set based on unique rating levels. Uses a new loss function and adapted RNN model for refining prediction scores.
result Models notably outperform state-of-the-art learning-to-rank models on four public datasets.
Null-Calibrated Conformal Selection via Target-Membership Scores
problem Identifying test candidates whose unknown responses fall in a target region while controlling the false discovery rate
method Membership-score-based conformal selection
result Finite-sample valid null p-values
New method compresses neural networks up to 14x with minimal performance loss.
problem Compressing neural networks for real-time applications.
method Post-training rank-selection method called Rank-Tuning.
result High compression rates with minimal performance degradation.
Optimal analysis of subset-selection based L_p low rank approximation.
problem Finding a rank-k matrix X to minimize the entry-wise L_p loss of matrix A.
method Column subset selection algorithm with improved approximation ratio using Riesz-Thorin interpolation theorem.
result Improved approximation ratio for subset selection based L_p low rank approximation.
Selective sampling improves matrix completion with known structure.
problem Reconstructing a low-rank matrix with incomplete data.
method Designing observation sets based on matrix structure and selective sampling.
result Improved reconstruction accuracy with selective sampling.
iSplit LBI predicts individualized partial rankings from ties, outperforming state-of-the-art methods.
problem Predicting partial rankings from pairwise comparisons with ties, considering individual preferences.
method Variable splitting-based algorithm (iSplit LBI) that generates a sequence of estimations with a regularization path, decomposing parameters into abnormal signals, personalized signals, and random noise.
result iSplit LBI significantly outperforms state-of-the-art alternatives in predicting individualized partial rankings.
Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.
problem Sequential budget allocation in ranking and selection
method Annealed weighted soft-min framework
result Surrogate converges uniformly to the hard minimum, soft-min weights concentrate on active challengers, and target allocation map is continuous.
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
New OCBA procedures minimize PICS in robust R&S.
problem Selecting the best alternative under input uncertainty.
method Developed new asymptotically optimal OCBA procedures.
result Procedures minimize probability of incorrect selection.
Physics-inspired methods optimize SVD compression of LLMs.
problem Efficiently compressing large language models (LLMs) using SVD.
method FermiGrad for globally optimal rank selection and PivGa for lossless compression.
result Global optimization of SVD ranks and lossless compression of low-rank factors.
Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for clas…
Develops efficient method for updating models with small data changes.
problem Efficiently updating models when data changes (e.g., adding/removing instances/features).
method Generalized Low-Rank Update (GLRU) for non-linear estimators.
result Provides updated solutions with computational complexity proportional to dataset changes.
This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of n objects can be identified by standard sorting methods using nlog2n pairwise comparisons. We are interested in natural situations in which relationships among the o…
Flexible ranking models from choice data.
problem Difficulties in modeling, learning from, and predicting rankings.
method Choice-based ranking models using repeated selection.
result Choice-based ranking models outperform existing models in various ranking tasks.