Author pages are created from data sourced from our academic publisher partnerships and public sources.

- Publications
- Influence

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

- John C. Duchi, Elad Hazan, Y. Singer
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 1 February 2011

We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning.… Expand

On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines

- K. Crammer, Y. Singer
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 1 March 2002

In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion… Expand

An Efficient Boosting Algorithm for Combining Preferences

- Y. Freund, R. Iyer, R. Schapire, Y. Singer
- Computer Science
- J. Mach. Learn. Res.
- 24 July 1998

We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several… Expand

Improved Boosting Algorithms using Confidence-Rated Predictions

- R. Schapire, Y. Singer
- Computer Science
- COLT
- 24 July 1998

We describe several improvements to Freund and Schapire‘s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a… Expand

Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network

- Kristina Toutanova, D. Klein, Christopher D. Manning, Y. Singer
- Computer Science
- HLT-NAACL
- 27 May 2003

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of… Expand

Pegasos: primal estimated sub-gradient solver for SVM

- S. Shalev-Shwartz, Y. Singer, Nathan Srebro, Andrew Cotter
- Computer Science, Mathematics
- Math. Program.
- 1 March 2011

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of… Expand

Online Passive-Aggressive Algorithms

- K. Crammer, O. Dekel, Joseph Keshet, S. Shalev-Shwartz, Y. Singer
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 9 December 2003

We present a unified view for online classification, regression, and uni-class problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for… Expand

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

- Erin L. Allwein, R. Schapire, Y. Singer
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 29 June 2000

We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning… Expand

BoosTexter: A Boosting-based System for Text Categorization

- R. Schapire, Y. Singer
- Computer Science, Mathematics
- Machine Learning
- 1 May 2000

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We… Expand

Improved Boosting Algorithms Using Confidence-rated Predictions

- R. Schapire, Y. Singer
- Computer Science
- COLT' 98
- 1998

We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a… Expand