Junhong Lin
13 papers · 2015–2025 · 5 venues · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Silver π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (19)
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Keyword Champion
(4)
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Keyword Pioneer
ποΈ
Keyword Collector
(52)
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Century Club Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(12)
Optimization & Theory Β· Machine Learning
(12)
Mathematics & Optimization
(5)
Core Methods
(5)
Optimization
(5)
Deep Learning
(3)
Optimization & Theory Β· Deep Learning
(3)
Artificial Intelligence
(2)
Natural Language Processing
(2)
Core AI
(2)
Applications
(1)
Learning Paradigms
(1)
Resources & Methods
(1)
Keywords
stochastic gradient descent
(6)
reproducing kernel hilbert space
(5)
convergence rate
(5)
stochastic gradient method
(4)
least squares regression
(3)
early stopping
(3)
regularization parameter
(2)
learning rate
(2)
non-convex optimization
(2)
regularized algorithm
(2)
distributed learning
(2)
sample complexity
(1)
convergence analysis
(1)
hyperparameter tuning
(1)
kernel ridge regression
(1)
convex loss
(1)
question answering
(1)
square loss
(1)
knowledge graph
(1)
generalization error bound
(1)
Papers
LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
ICML 2025
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
ICLR 2025
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
NeurIPS 2024
Revisiting Convergence of AdaGrad with Relaxed Assumptions
UAI 2024
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
JMLR 2020
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
JMLR 2020
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
ICML 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
ICML 2018
Optimal Rates for Multi-pass Stochastic Gradient Methods
JMLR 2017
Generalization Properties and Implicit Regularization for Multiple Passes SGM
ICML 2016
Iterative Regularization for Learning with Convex Loss Functions
JMLR 2016
Optimal Learning for Multi-pass Stochastic Gradient Methods
NeurIPS 2016
Learning Theory of Randomized Kaczmarz Algorithm
JMLR 2015