Michael Mahoney
36 papers · 2013–2025 · 7 conferences · across top CS/AI conferences
Achievements
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๐ Conference Polyglot (7) ๐งญ Keyword Pioneer ๐ Interdisciplinary Bridge ๐บ๏ธ Taxonomy Completionist (22) ๐ Academic Marathon (12)
๐บ๏ธ
Taxonomy Completionist
(22)
๐งญ
Keyword Pioneer
๐ฃ
Hot Topic Early Bird
๐
Conference Loyalist
(22)
๐
Keyword Champion
๐งฌ
Topic Evolution
๐ฌ
Deep Specialist
(10)
๐ฅ
Unstoppable
(5)
๐๏ธ
Keyword Collector
(61)
๐
Conference Pioneer
โก
Prolific Year
(8)
โ
The Questioner
๐
Trend Setter
๐
Century Club
(36)
Conferences
ICML (22)
AISTATS (7)
AAAI (2)
EMNLP (2)
ACL (1)
COLT (1)
JMLR (1)
Top co-authors
Research topics
Keywords
neural network optimization
(6)
stochastic optimization
(4)
randomized algorithm
(4)
least square
(4)
convex optimization
(3)
leverage score
(3)
matrix approximation
(3)
randomized sketching
(2)
implicit regularization
(2)
error estimation
(2)
model compression
(2)
non-convex optimization
(2)
stochastic gradient descent
(2)
numerical linear algebra
(2)
data augmentation
(2)
low-rank approximation
(2)
variational inference
(2)
combinatorial optimization
(2)
bootstrap method
(2)
neural network
(2)
Papers
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
JMLR 2025
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
ACL 2024
NoisyMix: Boosting Model Robustness to Common Corruptions
AISTATS 2024
GACT: Activation Compressed Training for Generic Network Architectures
ICML 2022
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
ICML 2022
Neurotoxin: Durable Backdoors in Federated Learning
ICML 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
ICML 2022
FatโTailed Variational Inference with Anisotropic Tail Adaptive Flows
ICML 2022
Good Classifiers are Abundant in the Interpolating Regime
AISTATS 2021
HAWQ-V3: Dyadic Neural Network Quantization
ICML 2021
Sparse sketches with small inversion bias
COLT 2021
Whatโs Hidden in a One-layer Randomly Weighted Transformer?
EMNLP 2021
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
AAAI 2021
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
ICML 2021
Multiplicative Noise and Heavy Tails in Stochastic Optimization
ICML 2021
Inefficiency of K-FAC for Large Batch Size Training
AAAI 2020
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms
AISTATS 2020
Statistical guarantees for local graph clustering
AISTATS 2020
Bayesian experimental design using regularized determinantal point processes
AISTATS 2020
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
EMNLP 2020
Forecasting Sequential Data Using Consistent Koopman Autoencoders
ICML 2020
Error Estimation for Sketched SVD via the Bootstrap
ICML 2020
PowerNorm: Rethinking Batch Normalization in Transformers
ICML 2020
Traditional and Heavy Tailed Self Regularization in Neural Network Models
ICML 2019
FLAG nโ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
AISTATS 2018
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
ICML 2018
Out-of-sample extension of graph adjacency spectral embedding
ICML 2018
A Simple and Strongly-Local Flow-Based Method for Cut Improvement
ICML 2016
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
ICML 2015
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrรถm Method
AISTATS 2015
A Statistical Perspective on Algorithmic Leveraging
ICML 2014
Anti-differentiating approximation algorithms:A case study with min-cuts, spectral, and flow
ICML 2014
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
ICML 2014
Robust Regression on MapReduce
ICML 2013
Quantile Regression for Large-scale Applications
ICML 2013
Revisiting the Nystrom method for improved large-scale machine learning
ICML 2013