# Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries

One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010).

# Hot PATE: Private Aggregation of Distributions for Diverse Task

Moreover, the knowledge of models is often encapsulated in the response distribution itself and preserving this diversity is critical for fluid and effective knowledge transfer from teachers to student.

# Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization

The problem of learning threshold functions is a fundamental one in machine learning.

# Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs

When inputs are adaptive, however, an adversarial input can be constructed after $O(\ell)$ queries with the classic estimator and the best known robust estimator only supports $\tilde{O}(\ell^2)$ queries.

# On the Robustness of CountSketch to Adaptive Inputs

CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements.

# Differentially-Private Clustering of Easy Instances

Clustering is a fundamental problem in data analysis.

# FriendlyCore: Practical Differentially Private Aggregation

Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large number of data points that is required for accurate results.

# A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance.

# Differentially Private Weighted Sampling

A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics.

# WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement

We design novel composable sketches for WOR $\ell_p$ sampling, weighted sampling of keys according to a power $p\in[0, 2]$ of their frequency (or for signed data, sum of updates).

# Graph Learning with Loss-Guided Training

no code implementations31 May 2020,

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains {\em static} in the course of training.

# Sample Complexity Bounds for Influence Maximization

no code implementations31 Jul 2019, ,

Our main result is a surprising upper bound of $O( s \tau \epsilon^{-2} \ln \frac{n}{\delta})$ for a broad class of models that includes IC and LT models and their mixtures, where $n$ is the number of nodes and $\tau$ is the number of diffusion steps.

# LSH Microbatches for Stochastic Gradients: Value in Rearrangement

We make a principled argument for the properties of our arrangements that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal distribution of training examples.

# Self-Similar Epochs: Value in Arrangement

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples.

# Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps

no code implementations12 Jun 2017, ,

At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set $M$ of centroids and $\alpha \geq 1$, one2all efficiently assigns probabilities to points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha |M|\epsilon^{-2})$.

# Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity

no code implementations7 Mar 2017,

Classic methods capture the graph structure through some underlying diffusion process that propagates through the graph edges.

# Semi-Supervised Learning on Graphs through Reach and Distance Diffusion

no code implementations30 Mar 2016

Inspired by the success of social influence as an alternative to spectral centrality such as Page Rank, we explore SSL with our kernels and develop highly scalable algorithms for parameter setting, label learning, and sampling.

# Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees

no code implementations30 Mar 2015, ,

The estimate is based on a weighted sample of $O(\epsilon^{-2})$ pairs of points, which is computed using $O(n)$ distance computations.

# Sketch-based Influence Maximization and Computation: Scaling up with Guarantees

The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence.

Data Structures and Algorithms Social and Information Networks G.2.2; H.2.8

# All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis

no code implementations14 Jun 2013

We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of statistics.

Data Structures and Algorithms Social and Information Networks

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