170 citations · 909 across the 66 of their papers we have counts for
6 papers · 1 filter
Recovery guarantees for polynomial approximation from dependent data with outliers
Lam Si Tung Ho, Hayden Schaeffer, Giang Tran +1
Learning non-linear systems from noisy, limited, and/or dependent data is an important task across various scientific fields including statistics, engineering, computer science, ma…
Compressed sensing with a jackknife and a bootstrap
Mark Tygert, Rachel Ward, Jure Zbontar
Compressed sensing proposes to reconstruct more degrees of freedom in a signal than the number of values actually measured. Compressed sensing therefore risks introducing errors --…
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
Rachel Ward, Xiaoxia Wu, Leon Bottou
Adaptive gradient methods such as AdaGrad and its variants update the stepsize in stochastic gradient descent on the fly according to the gradients received along the way; such met…
Extracting structured dynamical systems using sparse optimization with very few samples
Hayden Schaeffer, Giang Tran, Rachel Ward +1
Learning governing equations allows for deeper understanding of the structure and dynamics of data. We present a random sampling method for learning structured dynamical systems fr…
Greedy Variance Estimation for the LASSO
Christopher Kennedy, Rachel Ward
Recent results have proven the minimax optimality of LASSO and related algorithms for noisy linear regression. However, these results tend to rely on variance estimators that are i…
WNGrad: Learn the Learning Rate in Gradient Descent
Xiaoxia Wu, Rachel Ward, Léon Bottou
Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function…