6 citations · 18 across the 11 of their papers we have counts for
12 papers
Robust dimension-free estimation of simple random tensors: optimal guarantees under heavy tails and adversarial contamination
Roberto I. Oliveira, Zoraida F. Rico, Philip Thompson
We study robust estimation of simple random tensors of arbitrary order under finite-moment assumptions and adversarial contamination. We propose the first robust e…
Outlier-robust additive matrix decomposition
Philip Thompson
We study least-squares trace regression when the parameter is the sum of a -low-rank matrix and a -sparse matrix and a fraction of the labels is corrupted. For subgaussia…
A spectral least-squares-type method for heavy-tailed corrupted regression with unknown covariance \& heterogeneous noise
Roberto I. Oliveira, Zoraida F. Rico, Philip Thompson
We revisit heavy-tailed corrupted least-squares linear regression assuming to have a corrupted -sized label-feature sample of at most arbitrary outliers. We wish to estimat…
Outlier-robust sparse/low-rank least-squares regression and robust matrix completion
Philip Thompson
We study high-dimensional least-squares regression within a subgaussian statistical learning framework with heterogeneous noise. It includes -sparse and -low-rank least-squar…
Outlier-robust estimation of a sparse linear model using -penalized Huber's -estimator
Arnak S. Dalalyan, Philip Thompson
We study the problem of estimating a -dimensional -sparse vector in a linear model with Gaussian design and additive noise. In the case where the labels are contaminated by a…
Restricted eigenvalue property for corrupted Gaussian designs
Philip Thompson, Arnak S. Dalalyan
Motivated by the construction of tractable robust estimators via convex relaxations, we present conditions on the sample size which guarantee an augmented notion of Restricted Eige…
Sample average approximation with heavier tails II: localization in stochastic convex optimization and persistence results for the Lasso
Roberto I. Oliveira, Philip Thompson
``Localization'' has proven to be a valuable tool in the Statistical Learning literature as it allows sharp risk bounds in terms of the problem geometry. Localized bounds seem to b…
On variance reduction for stochastic smooth convex optimization with multiplicative noise
Alejandro Jofré, Philip Thompson
We propose dynamic sampled stochastic approximation (SA) methods for stochastic optimization with a heavy-tailed distribution (with finite 2nd moment). The objective is the sum of…
Sample average approximation with heavier tails I: non-asymptotic bounds with weak assumptions and stochastic constraints
Roberto I. Oliveira, Philip Thompson
We derive new and improved non-asymptotic deviation inequalities for the sample average approximation (SAA) of an optimization problem. Our results give strong error probability bo…
Incremental constraint projection methods for monotone stochastic variational inequalities
Alfredo Iusem, Alejandro Jofré, Philip Thompson
We consider stochastic variational inequalities with monotone operators defined as the expected value of a random operator. We assume the feasible set is the intersection of a larg…
Variance-based stochastic extragradient methods with line search for stochastic variational inequalities
Alfredo Iusem, Alejandro Jofré, Roberto I. Oliveira +1
A dynamic sampled stochastic approximated (DS-SA) extragradient method for stochastic variational inequalities (SVI) is proposed that is \emph{robust} with respect to an unknown Li…
Extragradient method with variance reduction for stochastic variational inequalities
Alfredo Iusem, Alejandro Jofré, Roberto I. Oliveira +1
We propose an extragradient method with stepsizes bounded away from zero for stochastic variational inequalities requiring only pseudo-monotonicity. We provide convergence and comp…