Kaiyi Ji
30 papers · 2018–2025 · 8 venues · across top CS/AI venues
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Research topics
Machine Learning
(29)
Optimization & Theory Β· Machine Learning
(28)
Mathematics & Optimization
(17)
Optimization
(17)
Artificial Intelligence
(11)
Learning Paradigms Β· Artificial Intelligence
(10)
Learning Paradigms Β· Machine Learning
(10)
Deep Learning
(7)
Learning Types Β· Machine Learning
(7)
Learning Types Β· Deep Learning
(3)
Optimization & Theory Β· Deep Learning
(3)
Core Methods
(2)
Reinforcement Learning
(1)
Core AI
(1)
Mathematics
(1)
Keywords
bilevel optimization
(14)
convergence analysis
(7)
convergence rate
(6)
non-convex optimization
(6)
multi-task learning
(5)
stochastic optimization
(5)
federated learning
(4)
few-shot learning
(3)
stochastic gradient descent
(3)
neural network optimization
(3)
gradient descent
(3)
sample complexity
(3)
variance reduction
(3)
hyperparameter optimization
(3)
online learning
(2)
multi-objective optimization
(2)
model-agnostic meta-learning
(2)
implicit differentiation
(2)
communication efficiency
(2)
client sampling
(2)
Papers
Efficiently Escaping Saddle Points in Bilevel Optimization
JMLR 2025
First-Order Federated Bilevel Learning
AAAI 2025
MGDA Converges under Generalized Smoothness, Provably
ICLR 2025
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
ICCV 2025
Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis
ICLR 2025
AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning
ICLR 2024
Fair Resource Allocation in Multi-Task Learning
ICML 2024
First-Order Minimax Bilevel Optimization
NeurIPS 2024
Understanding Forgetting in Continual Learning with Linear Regression
ICML 2024
Achieving O(Ξ΅^-1.5) Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
NeurIPS 2023
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
ICML 2023
Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm
NeurIPS 2023
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
ICML 2023
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
NeurIPS 2023
Lower Bounds and Accelerated Algorithms for Bilevel Optimization
JMLR 2023
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
NeurIPS 2023
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
NeurIPS 2023
Data sampling affects the complexity of online SGD over dependent data
UAI 2022
On the Convergence Theory for Hessian-Free Bilevel Algorithms
NeurIPS 2022
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
JMLR 2022
Will Bilevel Optimizers Benefit from Loops
NeurIPS 2022
Bilevel Optimization: Convergence Analysis and Enhanced Design
ICML 2021
Provably Faster Algorithms for Bilevel Optimization
NeurIPS 2021
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
NeurIPS 2020
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
ICML 2020
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
IJCAI 2020
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack
AAAI 2020
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
ICML 2019
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
NeurIPS 2019
Minimax Estimation of Neural Net Distance
NeurIPS 2018