Fetching the paper…
Reading the bibliography…
Many machine learning applications involve jointly predicting multiple mutually dependent output variables.
Maximum entropy Markov models for information extraction and segmentation
Andrew McCallum, Dayne Freitag, and Fernando Pereira · 2000
Earlier work this paper cites.
PEGASUS: A policy search method for large MDPs and POMDPs
Andrew Ng and Michael Jordan · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando Pereira · 2001
Earlier work this paper cites.
Ibal: A probabilistic rational programming language
Avi Pfeffer · 2001
Earlier work this paper cites.
A machine learning approach to coreference resolution of noun phrases
Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim · 2001
Earlier work this paper cites.
Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms
Michael Collins · 2002
Earlier work this paper cites.
Fast decoding and optimal decoding for machine translation
Ulrich Germann, Mike Jahr, Kevin Knight, Daniel Marcu, and Kenji Yamada · 2003
Earlier work this paper cites.
An efficient algorithm for projective dependency parsing
Joakim Nivre · 2003
Earlier work this paper cites.
Max-margin Markov networks
Ben Taskar, Carlos Guestrin, and Daphne Koller · 2003
Earlier work this paper cites.
Incremental parsing with the perceptron algorithm
Michael Collins and Brian Roark · 2004
Earlier work this paper cites.
Support vector machine learning for interdependent and structured output spaces
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, and Yasmine Altun · 2004
Earlier work this paper cites.
Error limiting reductions between classification tasks
Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, and Bianca Zadrozny · 2005
Earlier work this paper cites.
Learning as search optimization: Approximate large margin methods for structured prediction
Hal Daumé III and Daniel Marcu · 2005
Earlier work this paper cites.
Compiling comp ling: Practical weighted dynamic programming and the dyna language
Jason Eisner, Eric Goldlust, and Noah A. Smith · 2005
Earlier work this paper cites.
CRF++ project, 2005
Taku Kudo · 2005
Earlier work this paper cites.
Markov logic networks
Matthew Richardson and Pedro Domingos · 2006
Earlier work this paper cites.
Vowpal wabbit, 2007
John Langford, Alex Strehl, and Lihong Li · 2007
Earlier work this paper cites.
BLOG: probabilistic models with unknown objects
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L Ong, and Andrey Kolobov · 2007
Cited alongside, same era.
Boosting structured prediction for imitation learning
Nathan Ratliff, David Bradley, J. Andrew Bagnell, and Joel Chestnutt · 2007
Cited alongside, same era.
Global inference for entity and relation identification via a linear programming formulation
Dan Roth and Scott Wen-Tau Yih · 2007
Cited alongside, same era.
On learning linear ranking functions for beam search
Yuehua Xu and Alan Fern · 2007
Cited alongside, same era.
Discriminative learning of beam-search heuristics for planning
Yuehua Xu, Alan Fern, and Sung Wook Yoon · 2007
Cited alongside, same era.
Church: a language for generative models
Noah Goodman, Vikash Mansinghka, Daniel Roy, Keith Bonawitz, and Josh Tenenbaum · 2008
A reduction from apprenticeship learning to classification
Umar Syed and Robert E. Schapire · 2011
Later among the works it cites.
Output space search for structured prediction
Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli · 2012
Later among the works it cites.
Structured perceptron with inexact search
Liang Huang, Suphan Fayong, and Yang Guo · 2012
Later among the works it cites.
A short introduction to probabilistic soft logic
Angelika Kimmig, Stephen Bach, Matthias Broecheler, Bert Huang, and Lise Getoor · 2012
Later among the works it cites.
Multi-core structural SVM training
Kai-Wei Chang, Vivek Srikumar, and Dan Roth · 2013
Later among the works it cites.
Training deterministic parsers with non-deterministic oracles
Yoav Goldberg and Joakim Nivre · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Search-based structured prediction
Hal Daumé III, John Langford, and Daniel Marcu · 2009
Cited alongside, same era.
Cutting-plane training of structural SVMs
Thorsten Joachims, Thomas Finley, and Chun-Nam Yu · 2009
Cited alongside, same era.
FACTORIE: probabilistic programming via imperatively defined factor graphs
Andrew McCallum, Karl Schultz, and Sameer Singh · 2009
Cited alongside, same era.
Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth · 2009
Cited alongside, same era.
Infer .net 2.4, 2010. microsoft research cambridge, 2010
Tom Minka, John Winn, John Guiver, and David Knowles · 2010
Cited alongside, same era.
A reliable effective terascale linear learning system
Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, and John Langford · 2011
Cited alongside, same era.
Normalized online learning
Stéphane Ross, Paul Mineiro, and John Langford · 2013
Later among the works it cites.
A fast and accurate dependency parser using neural networks
Danqi Chen and Christopher Manning · 2014
Closest in time.
HC-Search: A learning framework for search-based structured prediction
Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli · 2014
Closest in time.
Probabilistic programming
Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani · 2014
Closest in time.
Reinforcement and imitation learning via interactive no-regret learning
Stéphane Ross and J. Andrew Bagnell · 2014
Closest in time.
Learning to search better than your teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, and John Langford · 2015
Closest in time.
Illinoissl: A JAVA library for structured prediction
Kai-Wei Chang, Shyam Upadhyay, Ming-Wei Chang, Vivek Srikumar, and Dan Roth · 2015
Closest in time.
Transition-based dependency parsing with stack long short-term memory
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith · 2015
Closest in time.
Saul: Towards declarative learning based programming
Parisa Kordjamshidi, Dan Roth, and Hao Wu · 2015
Closest in time.
Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins · 2016
Closest in time.