Koby Crammer
49 papers · 2001–2022 · 12 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 25 separate collaboration groups - the largest holds only 29% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Koby Crammer?
6 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Fernando Pereira, Mark Dredze, Alex Kulesza
14 papers
· 2005–2012
· NeurIPS, EMNLP, JMLR
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Confidence-Weighted Linear Classification for Text Categorization
JMLR 2012
Exploiting Feature Covariance in High-Dimensional Online Learning
AISTATS 2010
Adaptive Regularization of Weight Vectors
NeurIPS 2009
Multi-Class Confidence Weighted Algorithms
EMNLP 2009
Active Learning with Confidence
ACL 2008
Exact Convex Confidence-Weighted Learning
NeurIPS 2008
Learning from Multiple Sources
JMLR 2008
Online Methods for Multi-Domain Learning and Adaptation
EMNLP 2008
Learning Bounds for Domain Adaptation
NeurIPS 2007
Analysis of Representations for Domain Adaptation
NeurIPS 2006
Learning from Multiple Sources
NeurIPS 2006
Flexible Text Segmentation with Structured Multilabel Classification
EMNLP 2005
Gaussian Margin Machines
AISTATS 2005
Online Large-Margin Training of Dependency Parsers
ACL 2005
19 papers whose co-authors appear nowhere else under this name
Weighted Training for Cross-Task Learning
ICLR 2022
Convex Multi-Task Learning by Clustering
AISTATS 2015
Linear Multi-Resource Allocation with Semi-Bandit Feedback
NeurIPS 2015
Doubly Aggressive Selective Sampling Algorithms for Classification
AISTATS 2014
Learning Multiple Tasks in Parallel with a Shared Annotator
NeurIPS 2014
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions
AISTATS 2014
Hartigan’s K-Means Versus Lloyd’s K-Means — Is It Time for a Change?
IJCAI 2013
Multi Class Learning with Individual Sparsity
IJCAI 2013
A Simple Geometric Interpretation of SVM using Stochastic Adversaries
AISTATS 2012
Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
JMLR 2012
Learning Multiple Tasks using Shared Hypotheses
NeurIPS 2012
Metric Learning for Graph-Based Domain Adaptation
COLING 2012
Volume Regularization for Binary Classification
NeurIPS 2012
Learning Better Data Representation Using Inference-Driven Metric Learning
ACL 2010
Learning via Gaussian Herding
NeurIPS 2010
New Adaptive Algorithms for Online Classification
NeurIPS 2010
Loss-Sensitive Discriminative Training of Machine Transliteration Models
NAACL 2009
Advanced Online Learning for Natural Language Processing
ACL 2008
One-Class Clustering in the Text Domain
EMNLP 2008