
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
We show that aggregated model updates in federated learning may be insec...
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DRIVE: Onebit Distributed Mean Estimation
We consider the problem where n clients transmit ddimensional realvalu...
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SALSA: SelfAdjusting Lean Streaming Analytics
Counters are the fundamental building block of many data sketching schem...
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Dynamic Longest Increasing Subsequence and the ErdösSzekeres Partitioning Problem
In this paper, we provide new approximation algorithms for dynamic varia...
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ErdösSzekeres Partitioning Problem
In this note, we present a substantial improvement on the computational ...
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How to send a real number using a single bit (and some shared randomness)
We consider the fundamental problem of communicating an estimate of a re...
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PINT: Probabilistic Inband Network Telemetry
Commodity network devices support adding inband telemetry measurements ...
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Queues with Small Advice
Motivated by recent work on scheduling with predicted job sizes, we cons...
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Algorithms with Predictions
We introduce algorithms that use predictions from machine learning appli...
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Partitioned Learned Bloom Filter
Bloom filters are spaceefficient probabilistic data structures that are...
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Faster and More Accurate Measurement through AdditiveError Counters
Counters are a fundamental building block for networking applications su...
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Joint Alignment From Pairwise Differences with a Noisy Oracle
In this work we consider the problem of recovering n discrete random var...
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Prophets, Secretaries, and Maximizing the Probability of Choosing the Best
Suppose a customer is faced with a sequence of fluctuating prices, such ...
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Optimal Learning of Joint Alignments with a Faulty Oracle
We consider the following problem, which is useful in applications such ...
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The Supermarket Model with Known and Predicted Service Times
The supermarket model typically refers to a system with a large number o...
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Scheduling with Predictions and the Price of Misprediction
In many traditional job scheduling settings, it is assumed that one know...
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Online Pandora's Boxes and Bandits
We consider online variations of the Pandora's box problem (Weitzman. 19...
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A Model for Learned Bloom Filters, and Optimizing by Sandwiching
Recent work has suggested enhancing Bloom filters by using a prefilter,...
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Robust Set Reconciliation via Locality Sensitive Hashing
We consider variations of set reconciliation problems where two parties,...
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Directory Reconciliation
We initiate the theoretical study of directory reconciliation, a general...
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Metric Sublinear Algorithms via Linear Sampling
In this work we provide a new technique to design fast approximation alg...
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Optimizing Learned Bloom Filters by Sandwiching
We provide a simple method for improving the performance of the recently...
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A Model for Learned Bloom Filters and Related Structures
Recent work has suggested enhancing Bloom filters by using a prefilter,...
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Arithmetic Progression Hypergraphs: Examining the Second Moment Method
In many data structure settings, it has been shown that using "double ha...
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Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
Social networks and interactions in social media involve both positive a...
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Simulated Annealing for JPEG Quantization
JPEG is one of the most widely used image formats, but in some ways rema...
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2Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
The method of random projections has become a standard tool for machine ...
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Equitability Analysis of the Maximal Information Coefficient, with Comparisons
A measure of dependence is said to be equitable if it gives similar scor...
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Michael Mitzenmacher
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