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Found 9 papers, 2 papers with code
AdaBoost and robust one-bit compressed sensing
This paper studies binary classification in robust one-bit compressed sensing with adversarial errors.
On the robustness of minimum norm interpolators and regularized empirical risk minimizers
This article develops a general theory for minimum norm interpolating estimators and regularized empirical risk minimizers (RERM) in linear models in the presence of additive, potentially adversarial, errors.
Deep ReLU Programming
Feed-forward ReLU neural networks partition their input domain into finitely many "affine regions" of constant neuron activation pattern and affine behaviour.
Optimization and Control
Adaptive Rates for Total Variation Image Denoising
We study the theoretical properties of image denoising via total variation penalized least-squares.
Prediction bounds for higher order total variation regularized least squares
We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of $(k-1)^{\rm th}$ order differences.
Oracle inequalities for square root analysis estimators with application to total variation penalties
Through the direct study of the analysis estimator we derive oracle inequalities with fast and slow rates by adapting the arguments involving projections by Dalalyan, Hebiri and Lederer (2017).
A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks
More precisely, the information about the number regions per dimensionality is pushed through the layers starting with one region of the input dimension of the neural network and using a recursion based on an analysis of how many regions per output dimensionality a subsequent layer with a certain width can induce on an input region with a given dimensionality.
On the total variation regularized estimator over a class of tree graphs
We generalize to tree graphs obtained by connecting path graphs an oracle result obtained for the Fused Lasso over the path graph.
Asymptotic Confidence Regions for High-dimensional Structured Sparsity
In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators which incorporate prior knowledge about the organization of the non-zero coefficients.
