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Michael Mitzenmacher

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titleconferencedate
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24)2024-04-16
Proposes Tensor Homomorphic Compression that makes compressed gradients directly summable (homomorphic property) so parameter servers or programmable switches…
Talk on
Uniform Bounds for Scheduling with Job Size EstimatesITCS 20222022-01-31
Single-server queuing with Poisson arrivals and multiplicative beta-alpha bounded noise on job size estimates gets a new rank-based scheduler RaDiCal that is…
Talk
Peeling AlgorithmsParallel and Distributed Algorithms for Inference and Optimization (Simons Institute, Oct. 21–24, 2013)2013-10-21
Survey of peeling algorithms links Karp‑Sipser and pure literal ideas to practical tools like invertible Bloom lookup tables that store key/value XOR sums and…
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