551 citations · 1.1k across the 33 of their papers we have counts for
6 papers · 1 filter
On the identifiability of mixtures of ranking models
Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh +1
Mixtures of ranking models are standard tools for ranking problems. However, even the fundamental question of parameter identifiability is not fully understood: the identifiability…
Provable Training Set Debugging for Linear Regression
Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
We investigate problems in penalized -estimation, inspired by applications in machine learning debugging. Data are collected from two pools, one containing data with possibly co…
Extracting robust and accurate features via a robust information bottleneck
Ankit Pensia, Varun Jog, Po-Ling Loh
We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space…
Does Data Augmentation Lead to Positive Margin?
Shashank Rajput, Zhili Feng, Zachary Charles +2
Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness. DA artificially expands the training set by applying ra…
Online learning with graph-structured feedback against adaptive adversaries
Zhili Feng, Po-Ling Loh
We derive upper and lower bounds for the policy regret of -round online learning problems with graph-structured feedback, where the adversary is nonoblivious but assumed to have…
Generalization Error Bounds for Noisy, Iterative Algorithms
Ankit Pensia, Varun Jog, Po-Ling Loh
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. Recent work [Xu a…