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Found 7 papers, 3 papers with code
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition
Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcement learning.
On Penalty-based Bilevel Gradient Descent Method
Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization, meta-learning and reinforcement learning.
Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization
By leveraging the special structure of the equality constraints problem, the paper first presents an alternating implicit projected SGD approach and establishes the $\tilde{\cal O}(\epsilon^{-2})$ sample complexity that matches the state-of-the-art complexity of ALSET \citep{chen2021closing} for unconstrained bilevel problems.
Lazy Queries Can Reduce Variance in Zeroth-order Optimization
A major challenge of applying zeroth-order (ZO) methods is the high query complexity, especially when queries are costly.
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning.
A Single-Timescale Method for Stochastic Bilevel Optimization
This paper develops a new optimization method for a class of stochastic bilevel problems that we term Single-Timescale stochAstic BiLevEl optimization (STABLE) method.
Image denoising via K-SVD with primal-dual active set algorithm
K-SVD algorithm has been successfully applied to image denoising tasks dozens of years but the big bottleneck in speed and accuracy still needs attention to break.
