no code implementations • 27 May 2022 • Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk
Is overparameterization a privacy liability?
1 code implementation • 2 Feb 2022 • Jasper Tan, Blake Mason, Hamid Javadi, Richard G. Baraniuk
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).
1 code implementation • NeurIPS 2021 • Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk
Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training.
1 code implementation • 10 Oct 2019 • Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk
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.
no code implementations • 28 May 2019 • Daniel LeJeune, Randall Balestriero, Hamid Javadi, Richard G. Baraniuk
Deep (neural) networks have been applied productively in a wide range of supervised and unsupervised learning tasks.
no code implementations • NeurIPS 2018 • Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Neural networks have been used prominently in several machine learning and statistics applications.
no code implementations • 12 Mar 2018 • Adel Javanmard, Hamid Javadi
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.
no code implementations • 2 Feb 2018 • Behrooz Ghorbani, Hamid Javadi, Andrea Montanari
Namely, for certain regimes of the model parameters, variational inference outputs a non-trivial decomposition into topics.
1 code implementation • NeurIPS 2017 • Soheil Feizi, Hamid Javadi, David Tse
Consider a dataset where data is collected on multiple features of multiple individuals over multiple times.
1 code implementation • 5 Oct 2017 • Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Neural networks have been used prominently in several machine learning and statistics applications.
no code implementations • 8 May 2017 • Hamid Javadi, Andrea Montanari
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.