Kevin Scaman
23 papers · 2014–2025 · 7 conferences · across top CS/AI conferences
Achievements
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๐งญ Keyword Pioneer ๐ Interdisciplinary Bridge ๐ Renaissance Researcher (5) ๐บ๏ธ Taxonomy Completionist (16) ๐ Conference Polyglot (7)
๐บ๏ธ
Taxonomy Completionist
(16)
๐งญ
Keyword Pioneer
๐
Renaissance Researcher
(5)
๐งฌ
Topic Evolution
๐
Keyword Champion
(2)
๐
Grand Slam
๐๏ธ
Keyword Collector
(103)
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Century Club
(23)
๐ฅ
Unstoppable
(6)
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Trend Setter
โ
The Questioner
Conferences
NIPS (11)
ICML (6)
AISTATS (2)
AAAI (1)
ICLR (1)
IJCAI (1)
JMLR (1)
Top co-authors
Keywords
convergence rate
(5)
non-convex optimization
(5)
network optimization
(3)
federated learning
(3)
distributed optimization
(3)
neural network optimization
(2)
graph neural network
(2)
gradient descent
(2)
convex optimization
(2)
stochastic gradient descent
(2)
spectral radius
(2)
randomized smoothing
(2)
primal-dual algorithm
(2)
lipschitz continuity
(2)
diffusion network
(2)
privacy preservation
(1)
sample complexity
(1)
adversarial robustness
(1)
convergence analysis
(1)
graph classification
(1)
Papers
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
AAAI 2025
When to Forget? Complexity Trade-offs in Machine Unlearning
ICML 2025
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
AISTATS 2024
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
ICML 2024
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
ICLR 2024
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
AISTATS 2024
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
ICML 2022
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
ICML 2022
On Sample Optimality in Personalized Collaborative and Federated Learning
NIPS 2022
Convergence beyond the over-parameterized regime using Rayleigh quotients
NIPS 2022
Lipschitz normalization for self-attention layers with application to graph neural networks
ICML 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
NIPS 2021
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations
NIPS 2020
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration
NIPS 2020
Coloring Graph Neural Networks for Node Disambiguation
IJCAI 2020
Optimal Convergence Rates for Convex Distributed Optimization in Networks
JMLR 2019
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
NIPS 2019
Lipschitz regularity of deep neural networks: analysis and efficient estimation
NIPS 2018
KONG: Kernels for ordered-neighborhood graphs
NIPS 2018
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
NIPS 2018
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks
ICML 2017
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks
NIPS 2015
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
NIPS 2014