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Found 15 papers, 3 papers with code
How Reliable are Test Numbers for Revealing the COVID-19 Ground Truth and Applying Interventions?
We use the simulation framework to compare the performance of three testing policies: Random Symptomatic Testing (RST), Contact Tracing (CT), and a new Location Based Testing policy (LBT).
Fundamental limits of over-the-air optimization: Are analog schemes optimal?
We show that a simple scaled transmission analog coding scheme results in a slowdown in convergence rate by a factor of $\sqrt{d(1+1/\mathtt{SNR})}$.
Distributed Simulation and Distributed Inference
Nonetheless, we present a Las Vegas algorithm that simulates a single sample from the unknown distribution using $O(k/2^\ell)$ samples in expectation.
Estimating Renyi Entropy of Discrete Distributions
It was recently shown that estimating the Shannon entropy $H({\rm p})$ of a discrete $k$-symbol distribution ${\rm p}$ requires $\Theta(k/\log k)$ samples, a number that grows near-linearly in the support size.
Test without Trust: Optimal Locally Private Distribution Testing
We are concerned with two settings: First, when we insist on using an already deployed, general-purpose locally differentially private mechanism such as the popular RAPPOR or the recently introduced Hadamard Response for collecting data, and must build our tests based on the data collected via this mechanism; and second, when no such restriction is imposed, and we can design a bespoke mechanism specifically for testing.
Inference under Information Constraints I: Lower Bounds from Chi-Square Contraction
Underlying our bounds is a characterization of the contraction in chi-square distances between the observed distributions of the samples when information constraints are placed.
Inference under Information Constraints II: Communication Constraints and Shared Randomness
We propose a general-purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning.
Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit
We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information about their samples due to various information constraints.
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization
Finally, we propose an adaptive quantizer for gain which when used with RATQ for shape quantizer outperforms uniform gain quantization and is, in fact, close to optimal.
Interactive Inference under Information Constraints
We study the role of interactivity in distributed statistical inference under information constraints, e. g., communication constraints and local differential privacy.
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization
We propose \emph{Wyner-Ziv estimators}, which are communication and computationally efficient and near-optimal when an upper bound for the distance between the side information and the data is known.
Multiple Support Recovery Using Very Few Measurements Per Sample
For a given budget of $m$ measurements per sample, the goal is to recover the $\ell$ underlying supports, in the absence of the knowledge of group labels.
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
We obtain tight minimax rates for the problem of distributed estimation of discrete distributions under communication constraints, where $n$ users observing $m $ samples each can broadcast only $\ell$ bits.
The Role of Interactivity in Structured Estimation
Without sparsity assumptions, it has been established that interactivity cannot improve the minimax rates of estimation under these information constraints.
Continual Mean Estimation Under User-Level Privacy
We provide an algorithm that outputs a mean estimate at every time instant $t$ such that the overall release is user-level $\varepsilon$-DP and has the following error guarantee: Denoting by $M_t$ the maximum number of samples contributed by a user, as long as $\tilde{\Omega}(1/\varepsilon)$ users have $M_t/2$ samples each, the error at time $t$ is $\tilde{O}(1/\sqrt{t}+\sqrt{M}_t/t\varepsilon)$.
