John D. Lafferty

According to our database, John D. Lafferty authored at least 117 papers between 1990 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Disentangling and Integrating Relational and Sensory Information in Transformer Architectures.

Awni Altabaa, John Lafferty

CoRR, 2024

Approximation of relation functions and attention mechanisms.

Awni Altabaa, John Lafferty

CoRR, 2024

Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers.

Awni Altabaa, Taylor Whittington Webb, Jonathan D. Cohen, John Lafferty

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Relational Convolutional Networks: A framework for learning representations of hierarchical relations.

Awni Altabaa, John Lafferty

CoRR, 2023

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction.

Taylor W. Webb, Steven M. Frankland, Awni Altabaa, Kamesh Krishnamurthy, Declan Campbell, Jacob L. Russin, Randall C. O'Reilly, John Lafferty, Jonathan D. Cohen

CoRR, 2023

Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning.

Awni Altabaa, Taylor W. Webb, Jonathan D. Cohen, John Lafferty

CoRR, 2023

From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces.

Qi Lin, Zifan Li, John Lafferty, Ilker Yildirim

CoRR, 2023

2022

Emergent organization of receptive fields in networks of excitatory and inhibitory neurons.

Leon Lufkin, Ashish Puri, Ganlin Song, Xinyi Zhong, John Lafferty

CoRR, 2022

2021

Convergence and Alignment of Gradient Descentwith Random Back propagation Weights.

Ganlin Song, Ruitu Xu, John Lafferty

CoRR, 2021

Convergence and Alignment of Gradient Descent with Random Backpropagation Weights.

Ganlin Song, Ruitu Xu, John Lafferty

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

Fair quantile regression.

Dana Yang, John Lafferty, David Pollard

CoRR, 2019

Surfing: Iterative Optimization Over Incrementally Trained Deep Networks.

Ganlin Song, Zhou Fan, John Lafferty

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts.

Michihiro Yasunaga, John D. Lafferty

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Denoising Flows on Trees.

Sabyasachi Chatterjee, John D. Lafferty

IEEE Trans. Inf. Theory, 2018

Distributed Nonparametric Regression under Communication Constraints.

Yuancheng Zhu, John Lafferty

Proceedings of the 35th International Conference on Machine Learning, 2018

Prediction Rule Reshaping.

Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty

Proceedings of the 35th International Conference on Machine Learning, 2018

CALOREE: Learning Control for Predictable Latency and Low Energy.

Nikita Mishra, Connor Imes, John D. Lafferty, Henry Hoffmann

Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, 2018

2017

A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval.

Chengxiang Zhai, John D. Lafferty

SIGIR Forum, 2017

Document Language Models, Query Models, and Risk Minimization for Information Retrieval.

John D. Lafferty, Chengxiang Zhai

SIGIR Forum, 2017

Information Retrieval as Statistical Translation.

Adam L. Berger, John D. Lafferty

SIGIR Forum, 2017

Testing for Global Network Structure Using Small Subgraph Statistics.

Chao Gao, John Lafferty

CoRR, 2017

Testing Network Structure Using Relations Between Small Subgraph Probabilities.

Chao Gao, John Lafferty

CoRR, 2017

ESP: A Machine Learning Approach to Predicting Application Interference.

Nikita Mishra, John D. Lafferty, Henry Hoffmann

Proceedings of the 2017 IEEE International Conference on Autonomic Computing, 2017

2016

Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent.

Qinqing Zheng, John D. Lafferty

CoRR, 2016

Selective inference for group-sparse linear models.

Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Local Minimax Complexity of Stochastic Convex Optimization.

Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval.

ChengXiang Zhai, William W. Cohen, John D. Lafferty

SIGIR Forum, 2015

A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements.

Qinqing Zheng, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints.

Nikita Mishra, Huazhe Zhang, John D. Lafferty, Henry Hoffmann

Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems, 2015

2014

Quantized Estimation of Gaussian Sequence Models in Euclidean Balls.

Yuancheng Zhu, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Blossom Tree Graphical Models.

Zhe Liu, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Mismatched estimation and relative entropy in vector Gaussian channels.

Minhua Chen, John D. Lafferty

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

Computation-Risk Tradeoffs for Covariance-Thresholded Regression.

Dinah Shender, John D. Lafferty

Proceedings of the 30th International Conference on Machine Learning, 2013

The Bigraphical Lasso.

Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

The huge Package for High-dimensional Undirected Graph Estimation in R.

Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman

J. Mach. Learn. Res., 2012

Sparse Nonparametric Graphical Models

John D. Lafferty, Han Liu, Larry A. Wasserman

CoRR, 2012

The Nonparanormal SKEPTIC.

Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman

CoRR, 2012

Exponential Concentration for Mutual Information Estimation with Application to Forests.

Han Liu, John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Nonparametric Reduced Rank Regression.

Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Conditional Sparse Coding and Grouped Multivariate Regression.

Min Xu, John D. Lafferty

Proceedings of the 29th International Conference on Machine Learning, 2012

High Dimensional Semiparametric Gaussian Copula Graphical Models.

Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman

Proceedings of the 29th International Conference on Machine Learning, 2012

Sequential Nonparametric Regression.

Haijie Gu, John D. Lafferty

Proceedings of the 29th International Conference on Machine Learning, 2012

Sparse Additive Functional and Kernel CCA.

Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Forest Density Estimation.

Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman

J. Mach. Learn. Res., 2011

Union Support Recovery in Multi-task Learning.

Mladen Kolar, John D. Lafferty, Larry A. Wasserman

J. Mach. Learn. Res., 2011

Learning image representations from the pixel level via hierarchical sparse coding.

Kai Yu, Yuanqing Lin, John D. Lafferty

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010

Time varying undirected graphs.

Shuheng Zhou, John D. Lafferty, Larry A. Wasserman

Mach. Learn., 2010

Graph-Valued Regression.

Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman

Proceedings of the 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, 2010

Forest Density Estimation.

Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu

Proceedings of the COLT 2010, 2010

2009

Compressed and Privacy-Sensitive Sparse Regression.

Shuheng Zhou, John D. Lafferty, Larry A. Wasserman

IEEE Trans. Inf. Theory, 2009

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs.

Han Liu, John D. Lafferty, Larry A. Wasserman

J. Mach. Learn. Res., 2009

Fast nonparametric matrix factorization for large-scale collaborative filtering.

Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gong

Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009

Large-scale collaborative prediction using a nonparametric random effects model.

Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Nonparametric regression and classification with joint sparsity constraints.

Han Liu, John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo.

Han Liu, John D. Lafferty, Larry A. Wasserman

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Compressed Regression.

Shuheng Zhou, John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

SpAM: Sparse Additive Models.

Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Statistical Analysis of Semi-Supervised Regression.

John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Multiscale topic tomography.

Ramesh Nallapati, Susan Ditmore, John D. Lafferty, Kin Ung

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007

Feature selection in conditional random fields for activity recognition.

Douglas L. Vail, John D. Lafferty, Manuela M. Veloso

Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29, 2007

Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability.

Ramesh Nallapati, William W. Cohen, John D. Lafferty

Proceedings of the Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Signal Decomposition using Multiscale Admixture Models.

Matus Telgarsky, John D. Lafferty

Proceedings of the IEEE International Conference on Acoustics, 2007

Conditional random fields for activity recognition.

Douglas L. Vail, Manuela M. Veloso, John D. Lafferty

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), 2007

Computationally Efficient M-Estimation of Log-Linear Structure Models.

Noah A. Smith, Douglas L. Vail, John D. Lafferty

Proceedings of the ACL 2007, 2007

2006

A risk minimization framework for information retrieval.

ChengXiang Zhai, John D. Lafferty

Inf. Process. Manag., 2006

High-Dimensional Graphical Model Selection Using ℓ₁-Regularized Logistic Regression.

Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Quadratic programming relaxations for metric labeling and Markov random field MAP estimation.

Pradeep Ravikumar, John D. Lafferty

Proceedings of the Machine Learning, 2006

Dynamic topic models.

David M. Blei, John D. Lafferty

Proceedings of the Machine Learning, 2006

Graph Kernels by Spectral Transforms.

Xiaojin Zhu, Jaz S. Kandola, John Lafferty, Zoubin Ghahramani

Proceedings of the Semi-Supervised Learning, 2006

2005

Diffusion Kernels on Statistical Manifolds.

John D. Lafferty, Guy Lebanon

J. Mach. Learn. Res., 2005

Preconditioner Approximations for Probabilistic Graphical Models.

Pradeep Ravikumar, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Rodeo: Sparse Nonparametric Regression in High Dimensions.

John D. Lafferty, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Correlated Topic Models.

David M. Blei, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning.

Xiaojin Zhu, John D. Lafferty

Proceedings of the Machine Learning, 2005

2004

A study of smoothing methods for language models applied to information retrieval.

ChengXiang Zhai, John D. Lafferty

ACM Trans. Inf. Syst., 2004

Variational Chernoff Bounds for Graphical Models.

Pradeep Ravikumar, John D. Lafferty

Proceedings of the UAI '04, 2004

Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning.

Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Hyperplane margin classifiers on the multinomial manifold.

Guy Lebanon, John D. Lafferty

Proceedings of the Machine Learning, 2004

Kernel conditional random fields: representation and clique selection.

John D. Lafferty, Xiaojin Zhu, Yan Liu

Proceedings of the Machine Learning, 2004

Semi-supervised learning using randomized mincuts.

Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy

Proceedings of the Machine Learning, 2004

2003

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.

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

SIGIR Forum, 2003

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.

Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty

Proceedings of the Machine Learning, 2003

2002

Expectation-Propogation for the Generative Aspect Model.

Thomas P. Minka, John D. Lafferty

Proceedings of the UAI '02, 2002

Two-stage language models for information retrieval.

ChengXiang Zhai, John D. Lafferty

Proceedings of the SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

Conditional Models on the Ranking Poset.

Guy Lebanon, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Information Diffusion Kernels.

John D. Lafferty, Guy Lebanon

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Cranking: Combining Rankings Using Conditional Probability Models on Permutations.

Guy Lebanon, John D. Lafferty

Proceedings of the Machine Learning, 2002

Diffusion Kernels on Graphs and Other Discrete Input Spaces.

Risi Kondor, John D. Lafferty

Proceedings of the Machine Learning, 2002

Combining Simple Discriminators for Object Discrimination.

Shyjan Mahamud, Martial Hebert, John D. Lafferty

Proceedings of the Computer Vision, 2002

2001

Workshop on language modeling and information retrieval.

W. Bruce Croft, James P. Callan, John D. Lafferty

SIGIR Forum, 2001

Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk.

John D. Lafferty, Larry A. Wasserman

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Boosting and Maximum Likelihood for Exponential Models.

Guy Lebanon, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.

John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

Model-based Feedback in the Language Modeling Approach to Information Retrieval.

ChengXiang Zhai, John D. Lafferty

Proceedings of the 2001 ACM CIKM International Conference on Information and Knowledge Management, 2001

1999

Ordered Binary Decision Diagrams and Minimal Trellises.

John D. Lafferty, Alexander Vardy

IEEE Trans. Computers, 1999

Statistical Models for Text Segmentation.

Doug Beeferman, Adam L. Berger, John D. Lafferty

Mach. Learn., 1999

The Weaver System for Document Retrieval.

Adam L. Berger, John D. Lafferty

Proceedings of The Eighth Text REtrieval Conference, 1999

Additive Models, Boosting, and Inference for Generalized Divergences.

John D. Lafferty

Proceedings of the Twelfth Annual Conference on Computational Learning Theory, 1999

1998

Cyberpunc: a lightweight punctuation annotation system for speech.

Doug Beeferman, Adam L. Berger, John D. Lafferty

Proceedings of the 1998 IEEE International Conference on Acoustics, 1998

1997

Inducing Features of Random Fields.

Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty

IEEE Trans. Pattern Anal. Mach. Intell., 1997

Spectral Techniques for Expander Codes.

John D. Lafferty, Daniel N. Rockmore

Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, 1997

Text Segmentation Using Exponential Models.

Doug Beeferman, Adam L. Berger, John D. Lafferty

Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997

A Model of Lexical Attraction and Repulsion.

Doug Beeferman, Adam L. Berger, John D. Lafferty

Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, 1997

1996

Word clustering with parallel spoken language corpora.

Ye-Yi Wang, John D. Lafferty, Alex Waibel

Proceedings of the 4th International Conference on Spoken Language Processing, 1996

Cheating with imperfect transcripts.

Paul Placeway, John D. Lafferty

Proceedings of the 4th International Conference on Spoken Language Processing, 1996

1995

Cluster Expansions and Iterative Scaling for Maximum Entropy Language Models

John D. Lafferty, Bernhard Suhm

CoRR, 1995

A Robust Parsing Algorithm for Link Grammars.

Dennis Grinberg, John Lafferty, Daniel Dominic Sleator

Proceedings of the Fourth International Workshop on Parsing Technologies, 1995

1994

Decision Tree Parsing using a Hidden Derivation Model.

Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Adwait Ratnaparkhi, Salim Roukos

Proceedings of the Human Language Technology, 1994

The Candide System for Machine Translation.

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

Proceedings of the Human Language Technology, 1994

Inference and Estimation of a Long-Range Trigram Model.

Stephen Della Pietra, Vincent J. Della Pietra, John R. Gillett, John D. Lafferty, Harry Printz, Lubos Ures

Proceedings of the Grammatical Inference and Applications, Second International Colloquium, 1994

1992

Fast Fourier Analysis for SL₂ over a Finite Field and Related Numerical Experiments.

John D. Lafferty, Daniel N. Rockmore

Exp. Math., 1992

Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech.

Ezra Black, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, Salim Roukos

Proceedings of the Speech and Natural Language: Proceedings of a Workshop Held at Harriman, 1992

Towards History-based Grammars: Using Richer Models for Probabilistic Parsing.

Ezra Black, Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Salim Roukos

Proceedings of the Speech and Natural Language: Proceedings of a Workshop Held at Harriman, 1992

Numerical Investigation of the Spectrum for Certain Families of Cayley Graphs.

John D. Lafferty, Daniel N. Rockmore

Proceedings of the Expanding Graphs, 1992

Development and Evaluation of a Broad-Coverage Probabilistic Grammar of English-Language Computer Manuals.

Ezra Black, John D. Lafferty, Salim Roukos

Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics, 28 June, 1992

1991

Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars.

Frederick Jelinek, John D. Lafferty

Comput. Linguistics, 1991

1990

A Statistical Approach to Machine Translation.

Peter F. Brown, John Cocke, Stephen Della Pietra, Vincent J. Della Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, Paul S. Roossin

Comput. Linguistics, 1990