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Found 15 papers, 0 papers with code
The Sample Complexity of Simple Binary Hypothesis Testing
In this paper, we derive a formula that characterizes the sample complexity (up to multiplicative constants that are independent of $p$, $q$, and all error parameters) for: (i) all $0 \le \alpha, \beta \le 1/8$ in the prior-free setting; and (ii) all $\delta \le \alpha/4$ in the Bayesian setting.
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints
For the sample complexity of simple hypothesis testing under pure LDP constraints, we establish instance-optimal bounds for distributions with binary support; minimax-optimal bounds for general distributions; and (approximately) instance-optimal, computationally efficient algorithms for general distributions.
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities
We show that the sample complexity of simple binary hypothesis testing under communication constraints is at most a logarithmic factor larger than in the unconstrained setting and this bound is tight.
The Many Faces of Adversarial Risk
Adversarial risk quantifies the performance of classifiers on adversarially perturbed data.
Robust regression with covariate filtering: Heavy tails and adversarial contamination
We study the problem of linear regression where both covariates and responses are potentially (i) heavy-tailed and (ii) adversarially contaminated.
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications
The $r$-parallel set of a measurable set $A \subseteq \mathbb R^d$ is the set of all points whose distance from $A$ is at most $r$.
Adversarial Risk via Optimal Transport and Optimal Couplings
We show that the optimal adversarial risk for binary classification with 0-1 loss is determined by an optimal transport cost between the probability distributions of the two classes.
Extracting robust and accurate features via a robust information bottleneck
We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space.
Robustifying deep networks for image segmentation
Materials and Methods: In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas.
Estimating location parameters in entangled single-sample distributions
In the multivariate setting, we generalize our theory to mean estimation for mixtures of radially symmetric distributions, and derive minimax lower bounds on the expected error of any estimator that is agnostic to the scales of individual data points.
Graph-Based Ascent Algorithms for Function Maximization
We also provide simulations showing the relative convergence rates of our algorithms in comparison to an unbiased random walk, as a function of the smoothness of the graph function.
Generalization Error Bounds for Noisy, Iterative Algorithms
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data.
Computing and maximizing influence in linear threshold and triggering models
We quantify the gap between our upper and lower bounds in the case of the linear threshold model and illustrate the gains of our upper bounds for independent cascade models in relation to existing results.
Adversarial Influence Maximization
We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting.
On model misspecification and KL separation for Gaussian graphical models
We establish bounds on the KL divergence between two multivariate Gaussian distributions in terms of the Hamming distance between the edge sets of the corresponding graphical models.
