Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequence-based models often produce invalid sequences: sequences which do not represent a…
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48 results for “valid structures”
The paper shows cross-validation fails in learning Gaussian graphical model structures.
problem Cross-validation's failure in learning Gaussian graphical model structures.
method Finite-sample bounds on misidentification probability of Lasso estimator.
result Cross-validation is inconsistent for learning Gaussian graphical model structures.
CSTS benchmarks time series clustering by evaluating correlation structures.
problem Lack of validated ground truth for objectively assessing clustering quality.
method Synthetic benchmark CSTS for evaluating correlation structures in multivariate time series data.
result CSTS enables precise diagnosis of methodological limitations in correlation-based time series clustering.
CANs improve GANs by enforcing structured constraints during training.
problem Generating valid structured objects like molecules and game maps from examples alone.
method Constrained Adversarial Networks (CANs) embed constraints into the model during training, penalizing invalid structures.
result CANs efficiently generate high-quality and novel valid structures.
ACV improves structured data CV, handling dependence and noisy initial fits.
problem Structured data CV is slow due to expensive algorithm re-runs.
method Extend ACV to handle dependence and noisy initial fits.
result ACV provides accurate and efficient CV for structured data.
Unified approach to structured prediction combining entropy regularization and neuro-symbolic logic.
problem Structured prediction challenges due to large output spaces and insufficient labeled data.
method Neuro-symbolic entropy regularization loss that restricts entropy regularization to valid structures.
result Models predict more accurately and are more likely to be valid.
Knowing what you know: valid and validated confidence sets in multiclass and multilabel predictionstat.ML
Develops methods for valid and validated confidence sets in multiclass and multilabel prediction.
problem Challenges of typical conformal prediction methods in multiclass and multilabel problems, especially uneven coverage.
method Leverages quantile regression to build methods that always guarantee correct coverage and asymptotically optimal conditional coverage, addressing label interactions with tree-structured classifiers.
result Empirical evaluation suggests more robust coverage of confidence sets.
AI-assisted interviews allow respondents to describe experiences naturally, but mapping those accounts into structured survey variables is fallible.
problem Mapping AI-assisted interview responses into structured survey variables is fallible.
method Adaptive Matrix Validation (AMV) is proposed, which involves mapping responses into tabular data and using a small set of structured questions for statistical adjustment.
result The estimator calibrates mapped values using validation answers from other respondents and corrects remaining error with validation answers observed for the target respondent.
Paper offers anytime-valid inference for causal parameters using DML.
problem Classic DML is only valid asymptotically for a fixed sample size.
method Time-uniform DML results for anytime-valid inference.
result Valid inference at any arbitrary stopping time.
Paper addresses limitations of traditional hierarchical clustering methods.
problem Traditional hierarchical clustering methods face limitations in binary trees and ultrametrics.
method Introduces the notion of a valid hierarchy and a two-step algorithm to construct a binary tree and prune it to enforce validity.
result Proposes a method to recover the finest valid hierarchy, which is not constrained to binary structures.
Unified taxonomy for ML uncertainty in physics, validated.
problem Uncertainty quantification in machine learning for physics.
method Unified taxonomy, principled validation tools.
result Illustrated validation tools with examples.
New method infers causal effects without knowing control variables.
problem Inference errors when control variables are unknown.
method Proposes a method for inferring causal effects when control variables are unknown.
result Proves method yields asymptotically valid confidence intervals for average causal effects.
LOOCV is often useful for analyzing small, structured experimental designs.
problem The effectiveness of cross-validation in analyzing designed experiments.
method Empirical study comparing LOOCV and other model selection methods.
result LOOCV is often useful in the analysis of small, structured experimental designs.
New method uses weak labels to create valid confidence sets for predictions.
problem Lack of labeled data in machine learning models.
method Developed a conformal prediction framework to provide valid predictive confidence sets using weakly labeled data.
result New coverage definition allows for tighter and more informative (but valid) confidence sets.
Improved code translation by preserving structure with composed fine-tuning.
problem Improving code translation accuracy with unlabeled code outputs.
method Pre-trained denoiser to capture output structure, composed fine-tuning to fine-tune predictor.
result Composed fine-tuning significantly improves generalization over standard fine-tuning.
We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified larg…
Flow models recover causal transformations from observational data and a valid ordering.
problem Causal inference with only observational data and a valid causal ordering.
method Flow models that can recover component-wise, invertible transformations of exogenous variables.
result Flow models outperform previous methods and deliver consistent performance across various structural causal models.
This paper reviews and proposes a new approach for evaluating internal cluster validation indices.
problem Selecting the best-performing unsupervised classification algorithm without external information.
method Examines and proposes a new evaluation approach for internal validation indices.
result Suggests a new evaluation approach for internal validation indices.
Paper discusses challenges in deploying ML models for structural engineering.
problem Challenges in deploying machine learning models for structural engineering applications.
method Illustrates challenges through two examples, focusing on model overfitting, underspecification, training data representativeness, variable omission bias, and cross-validation.
result Highlights the importance of rigorous model validation techniques.
CARVE validates clustering results using resampling and stability analysis.
problem Inconsistent and unreliable clustering results due to algorithm, preprocessing, and sensitivity.
method CARVE uses resampling-based validation and stability analysis to evaluate multiple clustering algorithms and hyperparameters.
result CARVE consistently recovers near-optimal clusterings and finer biological structure.
Study uses topological signatures to quantify financial market complexity.
problem Capturing temporal organization beyond volatility measures.
method Null validated topological approach using norm of persistence landscapes.
result Persistence landscape norms reveal dynamical structure during market stress.
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy datacs.LG
This study validates BN structure learning algorithms under noisy data, revealing performance discrepancies.
problem Inconsistent performance claims across BN structure learning algorithms due to inconsistent evaluation methods.
method Applied 15 algorithms to noisy data in multiple studies, evaluating with various criteria.
result Traditional synthetic performance may overestimate real-world performance by 10-50%.
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluationstat.ML
Study examines how different time series cross-validation methods affect anomaly detection in multivariate time series.
problem Evaluating anomaly detection in multivariate time series requires preserving temporal dependencies, especially for subsequence anomalies.
method Systematically investigates walk-forward and sliding window methods across various validation configurations and classifier types.
result Sliding window method consistently yields higher precision-recall scores and reduced fold-to-fold performance variance, particularly for deep learning models.
Financial LLMs need explicit bias consideration to avoid invalid results.
problem Finance-specific biases inflate performance and contaminate backtests.
method Identified five recurring biases and proposed a Structural Validity Framework.
result Explicit bias consideration is necessary for valid deployment claims.
Generating point clouds, e.g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task. Meanwhile, neural networks utilizing symmetry invariant layers have been shown to be able to optimize their training objective in a data-efficient way. In this spirit, we present an ar…
CNN improves causal inference by controlling time-structured covariates.
problem Estimating the effect of early retirement on health outcomes while controlling for time-structured covariates.
method Used CNN to fit nuisance models explaining treatment and outcome, combining them into an augmented inverse probability weighting estimator.
result Uniformly valid inference achieved through CNN, providing rates of convergence and uniformly valid inference guarantees.
The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs. However, construction histories for these discrete objects are typically not unique and so generative models must reason about intractably large spaces in order…
A method for clustering small datasets in high dimensions using random projections.
problem Challenges in clustering small datasets in high-dimensional spaces.
method Random projection followed by binary clustering in one-dimensional space.
result Statistically significant clustering structures can be found with as few as 100-200 points.
New method for selecting clusters in residential electricity data.
problem Selecting useful clusters in electricity consumption data.
method Formalizing expert knowledge as external validation measures.
result Successfully reconstructed customer archetypes.
A method for efficient CV estimates in Bayesian hierarchical models.
problem Computational infeasibility of cross-validation in Bayesian hierarchical regression models.
method Conditioning on variance-covariance parameters to transform CV into an optimization problem.
result Equivalent or improved predictive estimates compared to full cross-validation.
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but is not directly applicable to networks since splitting network nodes into groups requires delet…
CRYSPNet: Crystal Structure Predictions via Neural Networkcond-mat.mtrl-sci
CRYSPNet predicts crystal structures from chemical compositions.
problem Predicting crystal structures of solids is challenging and computationally expensive.
method CRYSPNet uses a neural network to predict crystal properties from chemical composition.
result CRYSPNet outperforms alternative methods and is robustly validated.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
problem Endogeneity bias and invalid instrument validation in high-dimensional data.
method Merge variable selection algorithms and probabilistic graphs to estimate house prices and causal structure.
result Efficient data-driven instrument selection and invalid instrument purge in high-dimensional data.
Deep neural networks reveal a low-dimensional manifold structure in data.
problem Understanding the structure of data for better model performance.
method Model-centric analysis of the data manifold using the local data matrix and Fisher information matrix.
result The dataset lies on a data leaf with a dimension bounded by the number of labels.
Study reveals XAI methods fail in neuroimaging, suggesting domain-specific adaptation.
problem Systematic failures of XAI methods in neuroimaging applications.
method Systematic comparison of XAI methods on 45,000 structural brain MRIs using a novel validation framework.
result Two widely used XAI methods (GradCAM and Layer-wise Relevance Propagation) fail to accurately explain neuroimaging data.
Deep generative models have achieved remarkable success in various data domains, including images, time series, and natural languages. There remain, however, substantial challenges for combinatorial structures, including graphs. One of the key challenges lies in the difficulty of ensuring semantic validity in context. …
BRACE addresses noncompliance in bandits, offering methods for recommendation and treatment policies.
problem Noncompliance in bandit problems complicates learning objectives and treatment effects.
method BRACE formalizes objective-choice, identifies direct-control regimes, and proposes a phase-doubling algorithm for IV inversion.
result BRACE delivers valid policy values and structural uncertainty, even under weak identification and homogeneity failure.
Conditional Random Fields (CRF) are frequently applied for labeling and segmenting sequence data. Morency et al. (2007) introduced hidden state variables in a labeled CRF structure in order to model the latent dynamics within class labels, thus improving the labeling performance. Such a model is known as Latent-Dynamic…
FunCLBM clusters time series data for autonomous driving validation.
problem Validation of autonomous driving systems using large amounts of data.
method FunCLBM model for co-clustering high-dimensional time series data.
result FunCLBM provides structured partition and clustering views of datasets.
Validates network bootstraps for uncertainty quantification in network visualisation.
problem Quantifying uncertainty in network embeddings when only a single observation is available.
method Statistical indistinguishable embeddings using k-nearest neighbour smoothing, validated by an exchangeable network test.
result Proposes a principled, distribution-free network bootstrap that passes the exchangeable network test.
Researchers identify valid auxiliary functions for extreme value distributions and their max-domains of attraction.
problem Characterize valid auxiliary functions for extreme value distributions and their max-domains of attraction.
method Introduced 'universal' auxiliary functions valid for both VR and vMR representations, identified sets of valid auxiliary functions, and proposed a method for finding appropriate auxiliary functions.
result Characterized valid auxiliary functions for both VR and vMR representations for the entire MDA distribution families.
We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that…
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariancesstat.ME
We speed up Gaussian process cross-validation calculations and improve model diagnostics.
problem Efficiently calculating cross-validation residuals and their covariances in Gaussian processes.
method Generalized fast Gaussian process leave-one-out formulae to multiple-fold cross-validation, highlighting covariance structures.
result Correcting for residual covariances in cross-validation improves back to Maximum Likelihood Estimation.
New method for cross-validation in high-dimensional data with dependent or heavy-tailed covariates.
problem Inconsistent cross-validation in high-dimensional settings with dependent or heavy-tailed covariates.
method ROTI-GCV framework for cross-validation under proportional asymptotics regime.
result Demonstrated accuracy of ROTI-GCV in synthetic and semi-synthetic settings.
A new framework for time series forecasting that adapts to varying patterns.
problem Forecasting multivariate time series with predictive heterogeneity.
method Validation-driven clustering framework that applies specialization based on out-of-sample predictive performance.
result Improves robustness to heavy-tailed errors and local anomalies.
Cluster analysis is used to explore structure in unlabeled data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, thi…
New covariance estimator for financial portfolios.
problem Estimating large financial covariances in non-stationary environments.
method Exponentially weighted averages and cross-validation for nonlinearly shrinking sample eigenvalues.
result Our estimator performs well in large dimensions compared to existing estimators.
Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, includi…