91 citations · 255 across the 15 of their papers we have counts for
6 papers · 1 filter
Online semi-supervised perception: Real-time learning without explicit feedback
Branislav Kveton, Michal Valko, Matthai Phillipose +1
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In pa…
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Han Zhang, Wenhao Zheng, Charley Chen +4
Since the label collecting is prohibitive and time-consuming, unsupervised methods are preferred in applications such as fraud detection. Meanwhile, such applications usually requi…
Online Semi-Supervised Learning on Quantized Graphs
Michal Valko, Branislav Kveton, Ling Huang +1
In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL met…
Query Strategies for Evading Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +4
Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade…
Near-Optimal Evasion of Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +6
Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade det…
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang +1
Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for stati…