Haishan Ye
23 papers · 2017–2026 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+12 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (30) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π
Grand Slam
π¬
Deep Specialist
(17)
π§¬
Topic Evolution
π
Triple Crown
π
Keyword Champion
(3)
π€
Dynamic Duo
(10)
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
β
The Questioner
π
Century Club Β· Bronze
π
Trend Setter
ποΈ
Keyword Collector
(99)
Top co-authors
Research topics
Machine Learning
(21)
Optimization & Theory Β· Machine Learning
(20)
Mathematics & Optimization
(14)
Optimization
(14)
Application Areas
(3)
Artificial Intelligence
(2)
Deep Learning
(2)
Core AI
(2)
Mathematics
(2)
Optimization & Theory Β· Deep Learning
(2)
Computer Science
(1)
Natural Language Processing
(1)
Architectures
(1)
Core Methods
(1)
Foundations
(1)
Keywords
communication complexity
(6)
decentralized optimization
(5)
variance reduction
(4)
minimax optimization
(3)
stochastic optimization
(3)
distributed optimization
(3)
stochastic gradient
(3)
composite optimization
(3)
non-convex optimization
(3)
distributed learning
(3)
gradient descent
(3)
stochastic gradient descent
(2)
nesterov acceleration
(2)
bfgs method
(2)
quasi-newton method
(2)
convex optimization
(2)
convergence rate
(2)
zeroth-order optimization
(2)
superlinear convergence
(2)
sr1 method
(2)
Papers
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
AAAI 2026
Optimal Decentralized Composite Optimization for Strongly Convex Functions
JMLR 2025
ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs
ICLR 2025
Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer
ICLR 2025
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
AISTATS 2024
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
ICML 2024
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
ICLR 2024
Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods
ICML 2024
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
ICML 2024
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
NeurIPS 2024
Multi-Consensus Decentralized Accelerated Gradient Descent
JMLR 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
NeurIPS 2023
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
ICLR 2022
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods
JMLR 2022
Approximate Newton Methods
JMLR 2021
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
JMLR 2021
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence
NeurIPS 2021
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
AAAI 2021
Decentralized Accelerated Proximal Gradient Descent
NeurIPS 2020
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
CVPR 2020
Nesterov's Acceleration for Approximate Newton
JMLR 2020
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
NeurIPS 2020
Approximate Newton Methods and Their Local Convergence
ICML 2017