Haishan Ye
23 papers · 2017–2026 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+14 more ↓ Show less ↑
🌍 Conference Polyglot (7) 🏃 Academic Marathon (8) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (10)
🐣
Hot Topic Early Bird
🧭
Keyword Pioneer
🌍
Conference Polyglot
(7)
🏆
Grand Slam
🏆
Keyword Champion
(2)
👑
Triple Crown
🔬
Deep Specialist
(12)
🤝
Dynamic Duo
(10)
🔥
Unstoppable
(6)
⚡
Prolific Year
(6)
💎
Century Club
(22)
❓
The Questioner
📈
Trend Setter
🗃️
Keyword Collector
(65)
Conferences
JMLR (6)
NIPS (5)
ICLR (4)
ICML (4)
AAAI (2)
AISTATS (1)
CVPR (1)
Top co-authors
Keywords
communication complexity
(5)
decentralized optimization
(4)
variance reduction
(3)
minimax optimization
(3)
distributed learning
(3)
stochastic gradient
(3)
stochastic optimization
(3)
second-order similarity
(2)
distributed optimization
(2)
convex optimization
(2)
nonconvex optimization
(2)
nesterov acceleration
(2)
quasi-newton method
(2)
sr1 method
(2)
gradient descent
(2)
superlinear convergence
(2)
bfgs method
(2)
composite optimization
(2)
low-rank approximation
(1)
convergence analysis
(1)
Papers
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
AAAI 2026
Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer
ICLR 2025
ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs
ICLR 2025
Optimal Decentralized Composite Optimization for Strongly Convex Functions
JMLR 2025
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
NIPS 2024
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
AISTATS 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
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
ICML 2024
Multi-Consensus Decentralized Accelerated Gradient Descent
JMLR 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
NIPS 2023
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods
JMLR 2022
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
ICLR 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
NIPS 2021
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
AAAI 2021
Nesterov's Acceleration for Approximate Newton
JMLR 2020
Decentralized Accelerated Proximal Gradient Descent
NIPS 2020
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
NIPS 2020
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
CVPR 2020
Approximate Newton Methods and Their Local Convergence
ICML 2017