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NIPS
2008
15 years 2 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
85
Voted
CCS
2009
ACM
15 years 7 months ago
A framework for quantitative security analysis of machine learning
We propose a framework for quantitative security analysis of machine learning methods. Key issus of this framework are a formal specification of the deployed learning model and a...
Pavel Laskov, Marius Kloft
99
Voted
ICML
2004
IEEE
16 years 1 months ago
Large margin hierarchical classification
We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
Ofer Dekel, Joseph Keshet, Yoram Singer
121
Voted
APPINF
2003
15 years 1 months ago
Evolving High-Dimensional, Adaptive Camera-based Speed Sensors
This paper reviews some attempts that exploit a phenomenon, also known as motion parallax, to estimate the distance of closest approach of a moving object. Despite their success, ...
Ralf Salomon
ICML
2004
IEEE
16 years 1 months ago
Unifying collaborative and content-based filtering
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a nove...
Justin Basilico, Thomas Hofmann