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Found 10 papers, 0 papers with code
Adaptive Proximal Gradient Methods for Structured Neural Networks
We consider the training of structured neural networks where the regularizer can be non-smooth and possibly non-convex.
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
We propose a unified framework for stochastic proximal gradient descent, which we term ProxGen, that allows for arbitrary positive preconditioners and lower semi-continuous regularizers.
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Extensive experiments reveal that block-diagonal approaches achieve state-of-the-art results on several deep learning tasks, and can outperform adaptive diagonal methods, vanilla Sgd, as well as a modified version of full-matrix adaptation proposed very recently.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing.
Closed-form Estimators for High-dimensional Generalized Linear Models
We propose a class of closed-form estimators for GLMs under high-dimensional sampling regimes.
Elementary Estimators for Graphical Models
We propose a class of closed-form estimators for sparsity-structured graphical models, expressed as exponential family distributions, under high-dimensional settings.
Sparse Quantile Huber Regression for Efficient and Robust Estimation
We consider new formulations and methods for sparse quantile regression in the high-dimensional setting.
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
We consider regularized risk minimization in a large dictionary of Reproducing kernel Hilbert Spaces (RKHSs) over which the target function has a sparse representation.
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference
We consider multivariate regression problems involving high-dimensional predictor and response spaces.
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
We consider the problem of variable group selection for least squares regression, namely, that of selecting groups of variables for best regression performance, leveraging and adhering to a natural grouping structure within the explanatory variables.
