4 citations · 9 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
Debiasing the Lasso under Weaker Tail Assumptions
Leonardo Voltarelli, Roberto Imbuzeiro Oliveira
We consider the problem of high-dimensional inference with the lasso estimator. Different methods including 'double selection' techniques and multiple versions of the 'debiased las…
Nonparametric inference on Fokker-Plank and McKean-Vlasov models
Adriana Laurindo Monteiro, Roberto Imbuzeiro Oliveira
We propose a kernel-based estimator of the velocity field governing the transport and diffusion of -dimensional interacting particles. Assuming the initial positions are i.i.d.…
Trimmed sample means for robust uniform mean estimation and regression
Roberto I. Oliveira, Lucas Resende
It is well-known that trimmed sample means are robust against heavy tails and data contamination. This paper analyzes the performance of trimmed means and related methods in two no…
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…
Improved covariance estimation: optimal robustness and sub-Gaussian guarantees under heavy tails
Roberto I. Oliveira, Zoraida F. Rico
We present an estimator of the covariance matrix of random -dimensional vector from an i.i.d. sample of size . Our sole assumption is that this vector satisfies a bounded…
Split Conformal Prediction and Non-Exchangeable Data
Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos +1
Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the origi…