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Found 11 papers, 5 papers with code
A Blessing of Dimensionality in Membership Inference through Regularization
Is overparameterization a privacy liability?
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to memorize the training data (zero error on the training data).
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training.
The Implicit Regularization of Ordinary Least Squares Ensembles
Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the nature of the subsampling effect, particularly of the features, is not well understood.
Implicit Rugosity Regularization via Data Augmentation
Deep (neural) networks have been applied productively in a wide range of supervised and unsupervised learning tasks.
Porcupine Neural Networks: Approximating Neural Network Landscapes
Neural networks have been used prominently in several machine learning and statistics applications.
False Discovery Rate Control via Debiased Lasso
We consider the problem of variable selection in high-dimensional statistical models where the goal is to report a set of variables, out of many predictors $X_1, \dotsc, X_p$, that are relevant to a response of interest.
An Instability in Variational Inference for Topic Models
Namely, for certain regimes of the model parameters, variational inference outputs a non-trivial decomposition into topics.
Tensor Biclustering
Consider a dataset where data is collected on multiple features of multiple individuals over multiple times.
Porcupine Neural Networks: (Almost) All Local Optima are Global
Neural networks have been used prominently in several machine learning and statistics applications.
Non-negative Matrix Factorization via Archetypal Analysis
In this paper, we study an approach to NMF that can be traced back to the work of Cutler and Breiman (1994) and does not require the data to be separable, while providing a generally unique decomposition.
