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Found 12 papers, 0 papers with code
Metalearning with Very Few Samples Per Task
In multitask learning, we are given a fixed set of related learning tasks and need to output one accurate model per task, whereas in metalearning we are given tasks that are drawn i. i. d.
Differentially Private Medians and Interior Points for Non-Pathological Data
We construct differentially private estimators with low sample complexity that estimate the median of an arbitrary distribution over $\mathbb{R}$ satisfying very mild moment conditions.
Estimation of Entropy in Constant Space with Improved Sample Complexity
Recent work of Acharya et al. (NeurIPS 2019) showed how to estimate the entropy of a distribution $\mathcal D$ over an alphabet of size $k$ up to $\pm\epsilon$ additive error by streaming over $(k/\epsilon^3) \cdot \text{polylog}(1/\epsilon)$ i. i. d.
Local Differential Privacy Is Equivalent to Contraction of $E_γ$-Divergence
We investigate the local differential privacy (LDP) guarantees of a randomized privacy mechanism via its contraction properties.
Testing Tail Weight of a Distribution Via Hazard Rate
Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data.
Testing Determinantal Point Processes
Determinantal point processes (DPPs) are popular probabilistic models of diversity.
Private Testing of Distributions via Sample Permutations
In this paper, we use the framework of property testing to design algorithms to test the properties of the distribution that the data is drawn from with respect to differential privacy.
Testing Properties of Multiple Distributions with Few Samples
We propose a new setting for testing properties of distributions while receiving samples from several distributions, but few samples per distribution.
Testing Mixtures of Discrete Distributions
In our model, the noisy distribution is a mixture of the original distribution and noise, where the latter is known to the tester either explicitly or via sample access; the form of the noise is also known a priori.
Towards Testing Monotonicity of Distributions Over General Posets
We then build on these lower bounds to give $\Omega(n/\log{n})$ lower bounds for testing monotonicity over a matching poset of size $n$ and significantly improved lower bounds over the hypercube poset.
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Our theoretical results significantly improve over the best known algorithms for identity testing, and are the first results for private equivalence testing.
Differentially Private Identity and Closeness Testing of Discrete Distributions
We investigate the problems of identity and closeness testing over a discrete population from random samples.
