Statistical Learning
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
However, it remains unclear if we can further improve the convergence rate when the assumptions for the function in the population level also hold for each random realization almost surely (e.g., Lipschitzness of each realization of the stochastic gradient).
A Batch-to-Online Transformation under Random-Order Model
In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.
A Batch-to-Online Transformation under Random-Order Model
In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems Benjamin Coleman
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
Individualized Dosing Dynamics via Neural Eigen Decomposition
Dosing models often use differential equations to model biological dynamics.
Coordinating Distributed Example Orders for Provably Accelerated Training
Whereas RR arbitrarily permutes training examples, GraB leverages stale gradients from prior epochs to order examples -- achieving a provably faster convergence rate than RR.
CD_GraB_camera_ready
Whereas RR arbitrarily permutes training examples, GraB leverages stale gradients from prior epochs to order examples -- achieving a provably faster convergence rate than RR.