Michael Mitzenmacher
18 papers · 2014–2025 · 7 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (35) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Half 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Grand Slam
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Triple Crown
🚀
Conference Pioneer
🔥
Unstoppable · Bronze
💎
Century Club · Bronze
🌈
Renaissance Researcher
(6)
📈
Trend Setter
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Keyword Collector
(93)
Top co-authors
Research topics
Machine Learning
(16)
Optimization & Theory · Machine Learning
(15)
Application Areas
(8)
Artificial Intelligence
(5)
Mathematics & Optimization
(4)
Optimization
(4)
Computer Science
(3)
Deep Learning
(3)
Learning Paradigms · Artificial Intelligence
(3)
Core Methods
(2)
Foundations
(2)
Learning Paradigms · Machine Learning
(2)
Learning Types
(2)
Optimization & Theory · Deep Learning
(2)
Systems
(2)
Keywords
federated learning
(4)
distributed mean estimation
(3)
gradient compression
(3)
model compression
(2)
sequential decision
(2)
communication efficiency
(2)
random projection
(2)
online algorithm
(2)
count-min sketch
(2)
dirichlet process
(1)
online decision making
(1)
bayesian inference
(1)
posterior distribution
(1)
mutual information
(1)
neural network compression
(1)
quantization
(1)
variational inference
(1)
independence testing
(1)
binary classification
(1)
statistical dependence
(1)
Papers
DON’T STOP ME NOW: EMBEDDING BASED SCHEDULING FOR LLMS
ICLR 2025
Accelerating Federated Learning with Quick Distributed Mean Estimation
ICML 2024
Optimal and Approximate Adaptive Stochastic Quantization
NeurIPS 2024
SkipPredict: When to Invest in Predictions for Scheduling
NeurIPS 2024
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
NSDI 2024
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
ICML 2022
DRIVE: One-bit Distributed Mean Estimation
NeurIPS 2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
ICML 2021
Partitioned Learned Bloom Filters
ICLR 2021
Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation
ICML 2021
Prophets, Secretaries, and Maximizing the Probability of Choosing the Best
AISTATS 2020
Online Pandora’s Boxes and Bandits
AAAI 2019
A Bayesian Nonparametric View on Count-Min Sketch
NeurIPS 2018
A Model for Learned Bloom Filters and Optimizing by Sandwiching
NeurIPS 2018
Weightless: Lossy weight encoding for deep neural network compression
ICML 2018
Measuring Dependence Powerfully and Equitably
JMLR 2016
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity
NeurIPS 2016
Coding for Random Projections
ICML 2014