A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper introduces stability in model averaging and proposes a L2-penalty method.
A new method for sparse regression models using graph structure.
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.
This paper analyzes Stochastic Depth regularization in ResNets.
Regularization improves stability and consistency of sparse autoencoders.
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…
Overparameterized neural networks can be highly accurate on average on an i.i.d. test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimize…
Study ablated data augmentation techniques and their mathematical equivalence to penalties.
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.
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…
A parallel optimization method for convex functions using Hessian sketching and debiasing.
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.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
Estimates Gaussian location model with ridge regularization, comparing variational and spectral methods.
Spectral decoupling improves neural network generalization in medical imaging.