Orchard View Room – Page 11

Towards Data Efficient Monte Carlo Estimates in Reinforcement Learning

Josiah Hanna
University of Wisconsin–Madison

Dictionary Learning from dependent data samples

Hanback Lyu
University of Wisconsin–Madison

Learning from preferences and labels

Aarti Singh
Carnegie Mellon University

Subspace cluster with missing data via integer programming

Jeff Linderoth
University of Wisconsin–Madison

New representer theorems for inverse problems and machine learning

Michael Unser
EPFL

Feedback Coding for Efficient Interactive Machine Learning

Greg Canal
University of Wisconsin–Madison

Phylogenomics: “Inverting” Random Trees

Sebastien Roch
University of Wisconsin–Madison

Multiclass and Multi-Label Learning with General Losses

Shivani Agarwal
University of Pennsylvania

Backward Feature Correction: How can Deep Learning performs Deep Learning

How does a 110-layer ResNet learn a high-complexity classifier using relatively few training examples and short training time? We present a theory towards explaining this learning process in terms of hierarchical learning. We refer to hierarchical learning as the learner learns to represent a complicated target function by decomposing it into a …

Why some robust estimators are efficiently computable?

Recent advances of computational robust statistics have produced efficient estimators with provable near-optimal statistical guarantees for a variety of problems. These estimators often involve non-convex optimization, and it is not clear why these non-convex problems are efficiently solvable, but many classical non-convex formulations are not. We make an attempt to …