Andi Han
18 papers · 2021–2026 · 7 venues · across top CS/AI venues
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Research topics
Machine Learning
(14)
Optimization & Theory Β· Machine Learning
(11)
Deep Learning
(10)
Architectures
(7)
Mathematics & Optimization
(6)
Optimization
(6)
Artificial Intelligence
(5)
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Models
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Optimization & Theory Β· Deep Learning
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Mathematics
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Learning Types Β· Machine Learning
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Learning Types Β· Deep Learning
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Computer Vision
(1)
Keywords
riemannian optimization
(3)
graph neural network
(3)
riemannian manifold
(3)
diffusion model
(3)
stochastic optimization
(2)
in-context learning
(2)
representation learning
(2)
feature learning
(2)
node classification
(2)
label noise
(2)
gradient descent
(2)
attention mechanism
(2)
link prediction
(2)
model architecture
(1)
sparse coding
(1)
stochastic gradient
(1)
non-convex optimization
(1)
transformer architecture
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parameter efficient
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neural network optimization
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Papers
On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
AAAI 2026
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
ICML 2025
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
ICLR 2025
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
ICML 2025
On the Feature Learning in Diffusion Models
ICLR 2025
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
ICLR 2025
On the Role of Label Noise in the Feature Learning Process
ICML 2025
Provable In-Context Vector Arithmetic via Retrieving Task Concepts
ICML 2025
When Graph Neural Networks Meet Dynamic Mode Decomposition
ICLR 2025
A Framework for Bilevel Optimization on Riemannian Manifolds
NeurIPS 2024
On the Comparison between Multi-modal and Single-modal Contrastive Learning
NeurIPS 2024
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
NeurIPS 2024
Riemannian coordinate descent algorithms on matrix manifolds
ICML 2024
SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining
NeurIPS 2024
A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models
ACML 2023
Riemannian Accelerated Gradient Methods via Extrapolation
AISTATS 2023
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
NeurIPS 2021
Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization
IJCAI 2021