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ORCIDJohn D. Lafferty
John Lafferty
Person information
- affiliation: Yale University
- affiliation: University of Chicago
- affiliation: Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
[c85]Awni Altabaa, Taylor Whittington Webb, Jonathan D. Cohen, John Lafferty:
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers. ICLR 2024- [i32]Awni Altabaa, John Lafferty:
Approximation of relation functions and attention mechanisms. CoRR abs/2402.08856 (2024) - [i31]Awni Altabaa, John Lafferty:
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures. CoRR abs/2405.16727 (2024) - 2023
- [i30]Qi Lin, Zifan Li, John Lafferty, Ilker Yildirim:
From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces. CoRR abs/2302.10392 (2023) - [i29]Awni Altabaa, Taylor W. Webb, Jonathan D. Cohen, John Lafferty:
Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning. CoRR abs/2304.00195 (2023) - [i28]Taylor W. Webb, Steven M. Frankland, Awni Altabaa, Kamesh Krishnamurthy, Declan Campbell, Jacob L. Russin
, Randall C. O'Reilly, John Lafferty, Jonathan D. Cohen:
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction. CoRR abs/2309.06629 (2023) - [i27]Awni Altabaa, John Lafferty:
Relational Convolutional Networks: A framework for learning representations of hierarchical relations. CoRR abs/2310.03240 (2023) - 2022
- [i26]Leon Lufkin, Ashish Puri, Ganlin Song, Xinyi Zhong, John Lafferty:
Emergent organization of receptive fields in networks of excitatory and inhibitory neurons. CoRR abs/2205.13614 (2022) - 2021
- [c84]Ganlin Song, Ruitu Xu, John Lafferty:
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights. NeurIPS 2021: 19888-19898 - [i25]Ganlin Song, Ruitu Xu, John Lafferty:
Convergence and Alignment of Gradient Descentwith Random Back propagation Weights. CoRR abs/2106.06044 (2021) - 2020
- [i24]Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. CoRR abs/2006.14781 (2020)
2010 – 2019
- 2019
- [c83]Michihiro Yasunaga, John D. Lafferty:
TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts. AAAI 2019: 7394-7401 - [c82]Ganlin Song, Zhou Fan, John Lafferty:
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks. NeurIPS 2019: 15008-15017 - [i23]Michihiro Yasunaga, John Lafferty:
TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts. CoRR abs/1902.06034 (2019) - [i22]Dana Yang, John Lafferty, David Pollard:
Fair quantile regression. CoRR abs/1907.08646 (2019) - [i21]Ganlin Song, Zhou Fan, John Lafferty:
Surfing: Iterative optimization over incrementally trained deep networks. CoRR abs/1907.08653 (2019) - 2018
- [j22]Sabyasachi Chatterjee, John D. Lafferty:
Denoising Flows on Trees. IEEE Trans. Inf. Theory 64(3): 1767-1783 (2018) - [c81]Nikita Mishra, Connor Imes, John D. Lafferty, Henry Hoffmann:
CALOREE: Learning Control for Predictable Latency and Low Energy. ASPLOS 2018: 184-198 - [c80]Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty:
Prediction Rule Reshaping. ICML 2018: 629-637 - [c79]Yuancheng Zhu, John Lafferty:
Distributed Nonparametric Regression under Communication Constraints. ICML 2018: 6004-6012 - [i20]Yuancheng Zhu, John D. Lafferty:
Distributed Nonparametric Regression under Communication Constraints. CoRR abs/1803.01302 (2018) - [i19]Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John D. Lafferty:
Prediction Rule Reshaping. CoRR abs/1805.06439 (2018) - 2017
- [j21]Adam L. Berger, John D. Lafferty:
Information Retrieval as Statistical Translation. SIGIR Forum 51(2): 219-226 (2017) - [j20]John D. Lafferty, Chengxiang Zhai:
Document Language Models, Query Models, and Risk Minimization for Information Retrieval. SIGIR Forum 51(2): 251-259 (2017) - [j19]Chengxiang Zhai, John D. Lafferty:
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR Forum 51(2): 268-276 (2017) - [c78]Nikita Mishra, John D. Lafferty, Henry Hoffmann:
ESP: A Machine Learning Approach to Predicting Application Interference. ICAC 2017: 125-134 - [i18]Chao Gao, John Lafferty:
Testing Network Structure Using Relations Between Small Subgraph Probabilities. CoRR abs/1704.06742 (2017) - [i17]Chao Gao, John Lafferty:
Testing for Global Network Structure Using Small Subgraph Statistics. CoRR abs/1710.00862 (2017) - 2016
- [c77]Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty:
Selective inference for group-sparse linear models. NIPS 2016: 2469-2477 - [c76]Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu:
Local Minimax Complexity of Stochastic Convex Optimization. NIPS 2016: 3423-3431 - [i16]Qinqing Zheng, John D. Lafferty:
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent. CoRR abs/1605.07051 (2016) - 2015
- [j18]ChengXiang Zhai, William W. Cohen, John D. Lafferty:
Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. SIGIR Forum 49(1): 2-9 (2015) - [c75]Nikita Mishra, Huazhe Zhang, John D. Lafferty, Henry Hoffmann:
A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints. ASPLOS 2015: 267-281 - [c74]Qinqing Zheng, John D. Lafferty:
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. NIPS 2015: 109-117 - [i15]Qinqing Zheng, John D. Lafferty:
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. CoRR abs/1506.06081 (2015) - 2014
- [c73]Zhe Liu, John D. Lafferty:
Blossom Tree Graphical Models. NIPS 2014: 1458-1465 - [c72]Yuancheng Zhu, John D. Lafferty:
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls. NIPS 2014: 3662-3670 - 2013
- [c71]Dinah Shender, John D. Lafferty:
Computation-Risk Tradeoffs for Covariance-Thresholded Regression. ICML (3) 2013: 756-764 - [c70]Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou:
The Bigraphical Lasso. ICML (3) 2013: 1229-1237 - [c69]Minhua Chen, John D. Lafferty:
Mismatched estimation and relative entropy in vector Gaussian channels. ISIT 2013: 2845-2849 - [i14]Thomas P. Minka, John D. Lafferty:
Expectation-Propogation for the Generative Aspect Model. CoRR abs/1301.0588 (2013) - [i13]John D. Lafferty, Larry A. Wasserman:
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. CoRR abs/1301.2286 (2013) - 2012
- [j17]Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. J. Mach. Learn. Res. 13: 1059-1062 (2012) - [c68]Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty:
Sparse Additive Functional and Kernel CCA. ICML 2012 - [c67]Haijie Gu, John D. Lafferty:
Sequential Nonparametric Regression. ICML 2012 - [c66]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML 2012 - [c65]Min Xu, John D. Lafferty:
Conditional Sparse Coding and Grouped Multivariate Regression. ICML 2012 - [c64]Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty:
Nonparametric Reduced Rank Regression. NIPS 2012: 1637-1645 - [c63]Han Liu, John D. Lafferty, Larry A. Wasserman:
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS 2012: 2546-2554 - [i12]John D. Lafferty, Han Liu, Larry A. Wasserman:
Sparse Nonparametric Graphical Models. CoRR abs/1201.0794 (2012) - [i11]Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty:
Sparse Additive Functional and Kernel CCA. CoRR abs/1206.4669 (2012) - [i10]Haijie Gu, John D. Lafferty:
Sequential Nonparametric Regression. CoRR abs/1206.6408 (2012) - [i9]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal SKEPTIC. CoRR abs/1206.6488 (2012) - [i8]Pradeep Ravikumar, John D. Lafferty:
Variational Chernoff Bounds for Graphical Models. CoRR abs/1207.4172 (2012) - 2011
- [j16]Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman:
Forest Density Estimation. J. Mach. Learn. Res. 12: 907-951 (2011) - [j15]Mladen Kolar, John D. Lafferty, Larry A. Wasserman:
Union Support Recovery in Multi-task Learning. J. Mach. Learn. Res. 12: 2415-2435 (2011) - [c62]Kai Yu, Yuanqing Lin, John D. Lafferty:
Learning image representations from the pixel level via hierarchical sparse coding. CVPR 2011: 1713-1720 - 2010
- [j14]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Time varying undirected graphs. Mach. Learn. 80(2-3): 295-319 (2010) - [c61]Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu:
Forest Density Estimation. COLT 2010: 394-406 - [c60]Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman:
Graph-Valued Regression. NIPS 2010: 1423-1431 - [e2]John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2010 [contents]
2000 – 2009
- 2009
- [j13]Han Liu, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. J. Mach. Learn. Res. 10: 2295-2328 (2009) - [j12]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed and Privacy-Sensitive Sparse Regression. IEEE Trans. Inf. Theory 55(2): 846-866 (2009) - [c59]Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong:
Large-scale collaborative prediction using a nonparametric random effects model. ICML 2009: 1185-1192 - [c58]Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gong:
Fast nonparametric matrix factorization for large-scale collaborative filtering. SIGIR 2009: 211-218 - [e1]Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta:
Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, ISBN 9781615679119 [contents] - 2008
- [c57]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Time Varying Undirected Graphs. COLT 2008: 455-466 - [c56]Han Liu, John D. Lafferty, Larry A. Wasserman:
Nonparametric regression and classification with joint sparsity constraints. NIPS 2008: 969-976 - 2007
- [c55]Noah A. Smith, Douglas L. Vail, John D. Lafferty:
Computationally Efficient M-Estimation of Log-Linear Structure Models. ACL 2007 - [c54]Douglas L. Vail, Manuela M. Veloso, John D. Lafferty:
Conditional random fields for activity recognition. AAMAS 2007: 235 - [c53]Matus Telgarsky, John D. Lafferty:
Signal Decomposition using Multiscale Admixture Models. ICASSP (2) 2007: 449-452 - [c52]Ramesh Nallapati, William W. Cohen, John D. Lafferty:
Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability. ICDM Workshops 2007: 349-354 - [c51]Douglas L. Vail, John D. Lafferty, Manuela M. Veloso:
Feature selection in conditional random fields for activity recognition. IROS 2007: 3379-3384 - [c50]Ramesh Nallapati, Susan Ditmore, John D. Lafferty, Kin Ung:
Multiscale topic tomography. KDD 2007: 520-529 - [c49]John D. Lafferty, Larry A. Wasserman:
Statistical Analysis of Semi-Supervised Regression. NIPS 2007: 801-808 - [c48]Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. NIPS 2007: 1201-1208 - [c47]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression. NIPS 2007: 1713-1720 - [c46]Han Liu, John D. Lafferty, Larry A. Wasserman:
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. AISTATS 2007: 283-290 - [i7]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression. CoRR abs/0706.0534 (2007) - 2006
- [j11]ChengXiang Zhai, John D. Lafferty:
A risk minimization framework for information retrieval. Inf. Process. Manag. 42(1): 31-55 (2006) - [c45]David M. Blei, John D. Lafferty:
Dynamic topic models. ICML 2006: 113-120 - [c44]Pradeep Ravikumar, John D. Lafferty:
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. ICML 2006: 737-744 - [c43]Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty:
High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression. NIPS 2006: 1465-1472 - [p1]Xiaojin Zhu, Jaz S. Kandola, John Lafferty, Zoubin Ghahramani:
Graph Kernels by Spectral Transforms. Semi-Supervised Learning 2006: 276-291 - 2005
- [j10]John D. Lafferty, Guy Lebanon:
Diffusion Kernels on Statistical Manifolds. J. Mach. Learn. Res. 6: 129-163 (2005) - [c42]Xiaojin Zhu, John D. Lafferty:
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. ICML 2005: 1052-1059 - [c41]David M. Blei, John D. Lafferty:
Correlated Topic Models. NIPS 2005: 147-154 - [c40]John D. Lafferty, Larry A. Wasserman:
Rodeo: Sparse Nonparametric Regression in High Dimensions. NIPS 2005: 707-714 - [c39]Pradeep Ravikumar, John D. Lafferty:
Preconditioner Approximations for Probabilistic Graphical Models. NIPS 2005: 1113-1120 - 2004
- [j9]ChengXiang Zhai, John D. Lafferty:
A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2): 179-214 (2004) - [c38]Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy:
Semi-supervised learning using randomized mincuts. ICML 2004 - [c37]John D. Lafferty, Xiaojin Zhu, Yan Liu:
Kernel conditional random fields: representation and clique selection. ICML 2004 - [c36]Guy Lebanon, John D. Lafferty:
Hyperplane margin classifiers on the multinomial manifold. ICML 2004 - [c35]Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty:
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning. NIPS 2004: 1641-1648 - [c34]Pradeep Ravikumar, John D. Lafferty:
Variational Chernoff Bounds for Graphical Models. UAI 2004: 462-469 - 2003
- [j8]James Allan, Jay Aslam, Nicholas J. Belkin, Chris Buckley, James P. Callan, W. Bruce Croft, Susan T. Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard H. Hovy, Wessel Kraaij, John D. Lafferty, Victor Lavrenko, David D. Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay M. Ponte, John M. Prager, Dragomir R. Radev, Philip Resnik, Stephen E. Robertson, Ronald Rosenfeld, Salim Roukos, Mark Sanderson, Richard M. Schwartz, Amit Singhal, Alan F. Smeaton, Howard R. Turtle, Ellen M. Voorhees, Ralph M. Weischedel, Jinxi Xu, ChengXiang Zhai:
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37(1): 31-47 (2003) - [c33]Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty:
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML 2003: 912-919 - [c32]ChengXiang Zhai, William W. Cohen, John D. Lafferty:
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. SIGIR 2003: 10-17 - 2002
- [c31]Shyjan Mahamud, Martial Hebert, John D. Lafferty:
Combining Simple Discriminators for Object Discrimination. ECCV (3) 2002: 776-790 - [c30]Risi Kondor, John D. Lafferty:
Diffusion Kernels on Graphs and Other Discrete Input Spaces. ICML 2002: 315-322 - [c29]Guy Lebanon, John D. Lafferty:
Cranking: Combining Rankings Using Conditional Probability Models on Permutations. ICML 2002: 363-370 - [c28]John D. Lafferty, Guy Lebanon:
Information Diffusion Kernels. NIPS 2002: 375-382 - [c27]Guy Lebanon, John D. Lafferty:
Conditional Models on the Ranking Poset. NIPS 2002: 415-422 - [c26]ChengXiang Zhai, John D. Lafferty:
Two-stage language models for information retrieval. SIGIR 2002: 49-56 - [c25]Thomas P. Minka, John D. Lafferty:
Expectation-Propogation for the Generative Aspect Model. UAI 2002: 352-359 - 2001
- [j7]W. Bruce Croft, James P. Callan, John D. Lafferty:
Workshop on language modeling and information retrieval. SIGIR Forum 35(1): 4-6 (2001) - [c24]ChengXiang Zhai, John D. Lafferty:
Model-based Feedback in the Language Modeling Approach to Information Retrieval. CIKM 2001: 403-410 - [c23]John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira:
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML 2001: 282-289 - [c22]Guy Lebanon, John D. Lafferty:
Boosting and Maximum Likelihood for Exponential Models. NIPS 2001: 447-454 - [c21]John D. Lafferty, ChengXiang Zhai:
Document Language Models, Query Models, and Risk Minimization for Information Retrieval. SIGIR 2001: 111-119 - [c20]ChengXiang Zhai, John D. Lafferty:
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR 2001: 334-342 - [c19]John D. Lafferty, Larry A. Wasserman:
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. UAI 2001: 293-300
1990 – 1999
- 1999
- [j6]Doug Beeferman, Adam L. Berger, John D. Lafferty:
Statistical Models for Text Segmentation. Mach. Learn. 34(1-3): 177-210 (1999) - [j5]John D. Lafferty, Alexander Vardy:
Ordered Binary Decision Diagrams and Minimal Trellises. IEEE Trans. Computers 48(9): 971-987 (1999) - [c18]John D. Lafferty:
Additive Models, Boosting, and Inference for Generalized Divergences. COLT 1999: 125-133 - [c17]Adam L. Berger, John D. Lafferty:
Information Retrieval as Statistical Translation. SIGIR 1999: 222-229 - [c16]Adam L. Berger, John D. Lafferty:
The Weaver System for Document Retrieval. TREC 1999 - 1998
- [c15]Doug Beeferman, Adam L. Berger, John D. Lafferty:
Cyberpunc: a lightweight punctuation annotation system for speech. ICASSP 1998: 689-692 - 1997
- [j4]Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty:
Inducing Features of Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 19(4): 380-393 (1997) - [c14]Doug Beeferman, Adam L. Berger, John D. Lafferty:
A Model of Lexical Attraction and Repulsion. ACL 1997: 373-380 - [c13]Doug Beeferman, Adam L. Berger, John D. Lafferty:
Text Segmentation Using Exponential Models. EMNLP 1997 - [c12]John D. Lafferty, Daniel N. Rockmore:
Spectral Techniques for Expander Codes. STOC 1997: 160-167 - [i6]Doug Beeferman, Adam L. Berger, John D. Lafferty:
Text Segmentation Using Exponential Models. CoRR cmp-lg/9706016 (1997) - [i5]Doug Beeferman, Adam L. Berger, John D. Lafferty:
A Model of Lexical Attraction and Repulsion. CoRR cmp-lg/9706018 (1997) - 1996
- [c11]Paul Placeway, John D. Lafferty:
Cheating with imperfect transcripts. ICSLP 1996: 2115-2118 - [c10]Ye-Yi Wang, John D. Lafferty, Alex Waibel:
Word clustering with parallel spoken language corpora. ICSLP 1996: 2364-2367 - 1995
- [c9]Dennis Grinberg, John Lafferty, Daniel Dominic Sleator:
A Robust Parsing Algorithm for Link Grammars. IWPT 1995: 111-125 - [i4]Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty:
Inducing Features of Random Fields. CoRR abs/cmp-lg/9506014 (1995) - [i3]Dennis Grinberg, John D. Lafferty, Daniel Dominic Sleator:
A Robust Parsing Algorithm For Link Grammars. CoRR abs/cmp-lg/9508003 (1995) - [i2]John D. Lafferty, Bernhard Suhm:
Cluster Expansions and Iterative Scaling for Maximum Entropy Language Models. CoRR abs/cmp-lg/9509003 (1995) - 1994
- [c8]Stephen Della Pietra, Vincent J. Della Pietra, John R. Gillett, John D. Lafferty, Harry Printz, Lubos Ures:
Inference and Estimation of a Long-Range Trigram Model. ICGI 1994: 78-92 - [c7]Adam L. Berger, Peter F. Brown, Stephen Della Pietra, Vincent J. Della Pietra, John R. Gillett, John D. Lafferty, Robert L. Mercer, Harry Printz, Lubos Ures:
The Candide System for Machine Translation. HLT 1994 - [c6]Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Adwait Ratnaparkhi, Salim Roukos:
Decision Tree Parsing using a Hidden Derivation Model. HLT 1994 - [i1]Ezra Black, Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Salim Roukos:
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing. CoRR abs/cmp-lg/9405007 (1994) - 1993
- [c5]Ezra Black, Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Salim Roukos:
Towards History-Based Grammars: Using Richer Models for Probabilistic Parsing. ACL 1993: 31-37 - 1992
- [j3]John D. Lafferty, Daniel N. Rockmore:
Fast Fourier Analysis for SL2 over a Finite Field and Related Numerical Experiments. Exp. Math. 1(2): 115-139 (1992) - [c4]Ezra Black, John D. Lafferty, Salim Roukos:
Development and Evaluation of a Broad-Coverage Probabilistic Grammar of English-Language Computer Manuals. ACL 1992: 185-192 - [c3]John D. Lafferty, Daniel N. Rockmore:
Numerical Investigation of the Spectrum for Certain Families of Cayley Graphs. Expanding Graphs 1992: 63-73 - [c2]Ezra Black, Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Salim Roukos:
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing. HLT 1992 - [c1]Ezra Black, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, Salim Roukos:
Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech. HLT 1992 - 1991
- [j2]Frederick Jelinek, John D. Lafferty:
Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars. Comput. Linguistics 17(3): 315-323 (1991) - 1990
- [j1]Peter F. Brown, John Cocke, Stephen Della Pietra, Vincent J. Della Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, Paul S. Roossin:
A Statistical Approach to Machine Translation. Comput. Linguistics 16(2): 79-85 (1990)
Coauthor Index
ChengXiang Zhai
aka: Chengxiang Zhai
aka: Chengxiang Zhai
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