Paper introduces stability in model averaging and proposes a L2-penalty method.
problem Theoretical properties of model averaging from stability perspective.
method Introduces stability, defines asymptotic empirical risk minimizer, and proposes L2-penalty model averaging method.
result Proposed L2-penalty method ensures stability and consistency under reasonable conditions.
A new method for sparse regression models using graph structure.
problem Sparse regression models for high-dimensional data.
method Decomposes coefficient vector into latent variables, performs regularization on latent variables, uses proximal projection.
result Stable performance compared to other models, especially for high-dimensional data.
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability pred…
Study compares dropout and l2 regularization in linear models.
problem Understanding the statistical behavior of dropout and l2 regularization in linear models.
method Derives non-asymptotic bounds for gradient descent iterates with dropout and compares them to l2 regularization.
result Indicates a more subtle relationship between dropout and l2 regularization, highlighting interactions between dynamics and randomness.
This paper analyzes Stochastic Depth regularization in ResNets.
problem Improving generalization in ResNets through regularization.
method Hybrid analysis combining perturbation and signal propagation.
result Principled guidelines for choosing survival rates in SD.
Regularization improves stability and consistency of sparse autoencoders.
problem Varying features across random seeds and training choices in SAEs.
method Added L1 or L2 penalties on encoder and decoder weights.
result L2 regularization increases cross-seed feature consistency.
Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the n…
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a deep neural net with skip-layer c…
Study ablated data augmentation techniques and their mathematical equivalence to penalties.
problem Lack of mathematical understanding of differences between ablated data augmentation techniques.
method Formal model of mean ablated data augmentation and inverted dropout for linear regression; empirical validation for deep networks.
result Ablated data augmentation and inverted dropout are mathematically equivalent to penalties in optimization.
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large body of work aiming to help humans understand these black box functions to varying levels of granularity -- for example, through distillati…
SP-SPCA improves sparse PCA by adaptively adjusting variable penalties, enhancing interpretability and stability.
problem Poor interpretability and variable redundancy in PCA for high-dimensional data.
method Introduces a single equilibrium parameter to adaptively adjust variable penalties in the L2 regularization framework.
result Consistently outperforms standard sparse PCA methods in identifying sparse loading patterns and preserving cumulative variance.
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very limited…
Improved neural networks by combining group DRO with regularization.
problem Overparameterized neural networks can fail on atypical groups due to spurious correlations.
method Coupling distributionally robust optimization (DRO) with increased regularization.
result Significant improvements in worst-case group accuracy, maintaining high average accuracy.
A parallel optimization method for convex functions using Hessian sketching and debiasing.
problem Massively parallel optimization of convex functions with limited communication.
method Newton method with Hessian sketching and debiasing by workers, server averages descent directions.
result Approximation of Newton step with low-complexity adaptive sketching scheme.
Multi-label text classification is a popular machine learning task where each document is assigned with multiple relevant labels. This task is challenging due to high dimensional features and correlated labels. Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high …
New method for sparse kernel selection improves prediction accuracy.
problem Sparse Multiple Kernel Learning for binary classification.
method Alternating best response algorithm with semidefinite relaxations.
result Method outperforms state-of-the-art MKL approaches in prediction accuracy.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
problem Analyzing multi-source, multi-way data from different high-throughput technologies.
method Multiple Linked Tensor Factorization (MULTIFAC) extending CP decomposition with L2 penalties and EM algorithm for incomplete data.
result MULTIFAC approximates underlying signal, identifies shared and unshared structures, and imputes missing data.
Estimates Gaussian location model with ridge regularization, comparing variational and spectral methods.
problem Estimating parameters in Gaussian location model with regularization.
method Ridge-regularized log-density-ratio estimation, variational and spectral approaches.
result Regularized variational estimator has lower risk with many observations, spectral estimator with fewer observations.
Spectral decoupling improves neural network generalization in medical imaging.
problem Poor generalization of neural networks trained on medical imaging data.
method Spectral decoupling, a regularization technique that encourages learning more features.
result Spectral decoupling increases network robustness and performance on external datasets.