8 papers
Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics
Han Bao
Gradient descent has been of particular interest in modern machine learning beyond sole focus on optimization. Implicit bias emerging from optimization, though not being encoded by…
Exploring coupled tropical Pacific variability within a Multi-branch -Variational Autoencoder
Emily F. Wisinski, Maria J. Molina, Kyle J. C. Hall +4
This study explores what is encoded in the latent space of a multi-branch -variational autoencoder (-VAE) trained on coupled tropical Pacific climate fields. We assess the re…
Non-Stationary Online Structured Prediction with Surrogate Losses
Shinsaku Sakaue, Han Bao, Yuzhou Cao
Online structured prediction, including online classification as a special case, is the task of sequentially predicting labels from input features. In this setting, the surrogate r…
Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses
Yuzhou Cao, Han Bao, Lei Feng +1
Surrogate regret bounds, also known as excess risk bounds, bridge the gap between the convergence rates of surrogate and target losses. The regret transfer is lossless if the surro…
Revisiting Online Learning Approach to Inverse Linear Optimization: A FenchelYoung Loss Perspective and Gap-Dependent Regret Analysis
Shinsaku Sakaue, Han Bao, Taira Tsuchiya
This paper revisits the online learning approach to inverse linear optimization studied by Bärmann et al. (2017), where the goal is to infer an unknown linear objective function of…
Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses
Han Bao, Shinsaku Sakaue, Yuki Takezawa
The gradient descent (GD) has been one of the most common optimizer in machine learning. In particular, the loss landscape of a neural network is typically sharpened during the ini…
Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
Shinsaku Sakaue, Taira Tsuchiya, Han Bao +1
In online inverse linear optimization, a learner observes time-varying sets of feasible actions and an agent's optimal actions, selected by solving linear optimization over the fea…
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
Shinsaku Sakaue, Han Bao, Taira Tsuchiya +1
This paper studies online structured prediction with full-information feedback. For online multiclass classification, Van der Hoeven (2020) established \emph{finite} surrogate regr…