Search Results for author:
Found 11 papers, 4 papers with code
Orthogonally weighted $\ell_{2,1}$ regularization for rank-aware joint sparse recovery: algorithm and analysis
We propose and analyze an efficient algorithm for solving the joint sparse recovery problem using a new regularization-based method, named orthogonally weighted $\ell_{2, 1}$ ($\mathit{ow}\ell_{2, 1}$), which is specifically designed to take into account the rank of the solution matrix.
Point Cloud Data Simulation and Modelling with Aize Workspace
This work takes a look at data models often used in digital twins and presents preliminary results specifically from surface reconstruction and semantic segmentation models trained using simulated data.
Momentum Aggregation for Private Non-convex ERM
We introduce new algorithms and convergence guarantees for privacy-preserving non-convex Empirical Risk Minimization (ERM) on smooth $d$-dimensional objectives.
Differentially Private Online-to-Batch for Smooth Losses
We develop a new reduction that converts any online convex optimization algorithm suffering $O(\sqrt{T})$ regret into an $\epsilon$-differentially private stochastic convex optimization algorithm with the optimal convergence rate $\tilde O(1/\sqrt{T} + \sqrt{d}/\epsilon T)$ on smooth losses in linear time, forming a direct analogy to the classical non-private "online-to-batch" conversion.
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
Given that some of the calibrated parameters that show a good agreement with the experimental data can be nonphysical mercury properties, we need a more advanced two-phase flow model to capture bubble dynamics and mercury cavitation.
Better SGD using Second-order Momentum
We develop a new algorithm for non-convex stochastic optimization that finds an $\epsilon$-critical point in the optimal $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector product computations.
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
The local gradient points to the direction of the steepest slope in an infinitesimal neighborhood.
Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing
We first propose a novel criterion that guarantees that an $s$-sparse signal is the local minimizer of the $\ell_1/\ell_2$ objective; our criterion is interpretable and useful in practice.
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods.
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Standard ES methods with $d$-dimensional Gaussian smoothing suffer from the curse of dimensionality due to the high variance of Monte Carlo (MC) based gradient estimators.
On Cross-validation for Sparse Reduced Rank Regression
In high-dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received a lot of attention recently.
