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)

Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars. - Frederick Jelinek; John D. Lafferty (Comput. Linguistics, 1991)

Development and Evaluation of a Broad-Coverage Probabilistic Grammar of English-Language Computer Manuals. - Ezra Black; John D. Lafferty; Salim Roukos (ACL, 1992)

Numerical Investigation of the Spectrum for Certain Families of Cayley Graphs. - John D. Lafferty; Daniel N. Rockmore (Expanding Graphs, 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 (HLT, 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 (HLT, 1992)

Fast Fourier Analysis for SL2 over a Finite Field and Related Numerical Experiments. - John D. Lafferty; Daniel N. Rockmore (Exp. Math., 1992)

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 (ICGI, 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 (HLT, 1994)

Decision Tree Parsing using a Hidden Derivation Model. - Frederick Jelinek; John D. Lafferty; David M. Magerman; Robert L. Mercer; Adwait Ratnaparkhi; Salim Roukos (HLT, 1994)

A Robust Parsing Algorithm for Link Grammars. - Dennis Grinberg; John Lafferty; Daniel Dominic Sleator (IWPT, 1995)

Cluster Expansions and Iterative Scaling for Maximum Entropy Language Models - John D. Lafferty; Bernhard Suhm (CoRR, 1995)

Cheating with imperfect transcripts. - Paul Placeway; John D. Lafferty (ICSLP, 1996)

Word clustering with parallel spoken language corpora. - Ye-Yi Wang; John D. Lafferty; Alex Waibel (ICSLP, 1996)

A Model of Lexical Attraction and Repulsion. - Doug Beeferman; Adam L. Berger; John D. Lafferty (ACL, 1997)

Text Segmentation Using Exponential Models. - Doug Beeferman; Adam L. Berger; John D. Lafferty (EMNLP, 1997)

Spectral Techniques for Expander Codes. - John D. Lafferty; Daniel N. Rockmore (STOC, 1997)

Inducing Features of Random Fields. - Stephen Della Pietra; Vincent J. Della Pietra; John D. Lafferty (IEEE Trans. Pattern Anal. Mach. Intell., 1997)

Cyberpunc: a lightweight punctuation annotation system for speech. - Doug Beeferman; Adam L. Berger; John D. Lafferty (ICASSP, 1998)

Additive Models, Boosting, and Inference for Generalized Divergences. - John D. Lafferty (COLT, 1999)

The Weaver System for Document Retrieval. - Adam L. Berger; John D. Lafferty (TREC, 1999)

Statistical Models for Text Segmentation. - Doug Beeferman; Adam L. Berger; John D. Lafferty (Mach. Learn., 1999)

Ordered Binary Decision Diagrams and Minimal Trellises. - John D. Lafferty; Alexander Vardy (IEEE Trans. Computers, 1999)

Model-based Feedback in the Language Modeling Approach to Information Retrieval. - ChengXiang Zhai; John D. Lafferty (CIKM, 2001)

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. - John D. Lafferty; Andrew McCallum; Fernando C. N. Pereira (ICML, 2001)

Boosting and Maximum Likelihood for Exponential Models. - Guy Lebanon; John D. Lafferty (NIPS, 2001)

Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. - John D. Lafferty; Larry A. Wasserman (UAI, 2001)

Workshop on language modeling and information retrieval. - W. Bruce Croft; James P. Callan; John D. Lafferty (SIGIR Forum, 2001)

Combining Simple Discriminators for Object Discrimination. - Shyjan Mahamud; Martial Hebert; John D. Lafferty (ECCV (3), 2002)

Diffusion Kernels on Graphs and Other Discrete Input Spaces. - Risi Kondor; John D. Lafferty (ICML, 2002)

Cranking: Combining Rankings Using Conditional Probability Models on Permutations. - Guy Lebanon; John D. Lafferty (ICML, 2002)

Information Diffusion Kernels. - John D. Lafferty; Guy Lebanon (NIPS, 2002)

Conditional Models on the Ranking Poset. - Guy Lebanon; John D. Lafferty (NIPS, 2002)

Two-stage language models for information retrieval. - ChengXiang Zhai; John D. Lafferty (SIGIR, 2002)

Expectation-Propogation for the Generative Aspect Model. - Thomas P. Minka; John D. Lafferty (UAI, 2002)

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. - Xiaojin Zhu 001; Zoubin Ghahramani; John D. Lafferty (ICML, 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 001; et al. (SIGIR Forum, 2003)

Semi-supervised learning using randomized mincuts. - Avrim Blum; John D. Lafferty; Mugizi Robert Rwebangira; Rajashekar Reddy (ICML, 2004)

Kernel conditional random fields: representation and clique selection. - John D. Lafferty; Xiaojin Zhu 001; Yan Liu 002 (ICML, 2004)

Hyperplane margin classifiers on the multinomial manifold. - Guy Lebanon; John D. Lafferty (ICML, 2004)

Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning. - Xiaojin Zhu 001; Jaz S. Kandola; Zoubin Ghahramani; John D. Lafferty (NIPS, 2004)

Variational Chernoff Bounds for Graphical Models. - Pradeep Ravikumar; John D. Lafferty (UAI, 2004)

A study of smoothing methods for language models applied to information retrieval. - ChengXiang Zhai; John D. Lafferty (ACM Trans. Inf. Syst., 2004)

Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. - Xiaojin Zhu 001; John D. Lafferty (ICML, 2005)

Correlated Topic Models. - David M. Blei; John D. Lafferty (NIPS, 2005)

Rodeo: Sparse Nonparametric Regression in High Dimensions. - John D. Lafferty; Larry A. Wasserman (NIPS, 2005)

Preconditioner Approximations for Probabilistic Graphical Models. - Pradeep Ravikumar; John D. Lafferty (NIPS, 2005)

Diffusion Kernels on Statistical Manifolds. - John D. Lafferty; Guy Lebanon (J. Mach. Learn. Res., 2005)

Graph Kernels by Spectral Transforms. - Xiaojin Zhu 001; Jaz S. Kandola; John Lafferty; Zoubin Ghahramani (Semi-Supervised Learning, 2006)

Dynamic topic models. - David M. Blei; John D. Lafferty (ICML, 2006)

Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. - Pradeep Ravikumar; John D. Lafferty (ICML, 2006)

High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression. - Martin J. Wainwright; Pradeep Ravikumar; John D. Lafferty (NIPS, 2006)

A risk minimization framework for information retrieval. - ChengXiang Zhai; John D. Lafferty (Inf. Process. Manag., 2006)

Computationally Efficient M-Estimation of Log-Linear Structure Models. - Noah A. Smith; Douglas L. Vail; John D. Lafferty (ACL, 2007)

Conditional random fields for activity recognition. - Douglas L. Vail; Manuela M. Veloso; John D. Lafferty (AAMAS, 2007)

Signal Decomposition using Multiscale Admixture Models. - Matus Telgarsky; John D. Lafferty (ICASSP (2), 2007)

Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability. - Ramesh Nallapati; William W. Cohen; John D. Lafferty (ICDM Workshops, 2007)

Feature selection in conditional random fields for activity recognition. - Douglas L. Vail; John D. Lafferty; Manuela M. Veloso (IROS, 2007)

Multiscale topic tomography. - Ramesh Nallapati; Susan Ditmore; John D. Lafferty; Kin Ung (KDD, 2007)

Statistical Analysis of Semi-Supervised Regression. - John D. Lafferty; Larry A. Wasserman (NIPS, 2007)

SpAM: Sparse Additive Models. - Pradeep Ravikumar; Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2007)

Compressed Regression. - Shuheng Zhou 002; John D. Lafferty; Larry A. Wasserman (NIPS, 2007)

Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (AISTATS, 2007)

Nonparametric regression and classification with joint sparsity constraints. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2008)

Large-scale collaborative prediction using a nonparametric random effects model. - Kai Yu 001; John D. Lafferty; Shenghuo Zhu; Yihong Gong (ICML, 2009)

Fast nonparametric matrix factorization for large-scale collaborative filtering. - Kai Yu 001; Shenghuo Zhu; John D. Lafferty; Yihong Gong (SIGIR, 2009)

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2009)

Compressed and Privacy-Sensitive Sparse Regression. - Shuheng Zhou 002; John D. Lafferty; Larry A. Wasserman (IEEE Trans. Inf. Theory, 2009)

Forest Density Estimation. - Anupam Gupta 001; John D. Lafferty; Han Liu 001; Larry A. Wasserman; Min Xu 010 (COLT, 2010)

Graph-Valued Regression. - Han Liu 001; Xi Chen 010; John D. Lafferty; Larry A. Wasserman (NIPS, 2010)

Time varying undirected graphs. - Shuheng Zhou 002; John D. Lafferty; Larry A. Wasserman (Mach. Learn., 2010)

Learning image representations from the pixel level via hierarchical sparse coding. - Kai Yu 001; Yuanqing Lin; John D. Lafferty (CVPR, 2011)

Union Support Recovery in Multi-task Learning. - Mladen Kolar; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2011)

Forest Density Estimation. - Han Liu 001; Min Xu 010; Haijie Gu; Anupam Gupta 001; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2011)

Sparse Additive Functional and Kernel CCA. - Sivaraman Balakrishnan; Kriti Puniyani; John D. Lafferty (ICML, 2012)

Sequential Nonparametric Regression. - Haijie Gu; John D. Lafferty (ICML, 2012)

High Dimensional Semiparametric Gaussian Copula Graphical Models. - Han Liu 001; Fang Han; Ming Yuan 001; John D. Lafferty; Larry A. Wasserman (ICML, 2012)

Conditional Sparse Coding and Grouped Multivariate Regression. - Min Xu 010; John D. Lafferty (ICML, 2012)

Nonparametric Reduced Rank Regression. - Rina Foygel; Michael Horrell; Mathias Drton; John D. Lafferty (NIPS, 2012)

Exponential Concentration for Mutual Information Estimation with Application to Forests. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2012)

The Nonparanormal SKEPTIC. - Han Liu 001; Fang Han; Ming Yuan 001; John D. Lafferty; Larry A. Wasserman (CoRR, 2012)

Sparse Nonparametric Graphical Models - John D. Lafferty; Han Liu 001; Larry A. Wasserman (CoRR, 2012)

The huge Package for High-dimensional Undirected Graph Estimation in R. - Tuo Zhao; Han Liu 001; Kathryn Roeder; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2012)

The Bigraphical Lasso. - Alfredo A. Kalaitzis; John D. Lafferty; Neil D. Lawrence; Shuheng Zhou 002 (ICML (3), 2013)

Computation-Risk Tradeoffs for Covariance-Thresholded Regression. - Dinah Shender; John D. Lafferty (ICML (3), 2013)

Mismatched estimation and relative entropy in vector Gaussian channels. - Minhua Chen; John D. Lafferty (ISIT, 2013)

Blossom Tree Graphical Models. - Zhe Liu 011; John D. Lafferty (NIPS, 2014)

Quantized Estimation of Gaussian Sequence Models in Euclidean Balls. - Yuancheng Zhu; John D. Lafferty (NIPS, 2014)

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints. - Nikita Mishra; Huazhe Zhang; John D. Lafferty; Henry Hoffmann (ASPLOS, 2015)

A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements. - Qinqing Zheng; John D. Lafferty (NIPS, 2015)

Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. - ChengXiang Zhai; William W. Cohen; John D. Lafferty (SIGIR Forum, 2015)

Local Minimax Complexity of Stochastic Convex Optimization. - Sabyasachi Chatterjee; John C. Duchi; John D. Lafferty; Yuancheng Zhu (NIPS, 2016)

Selective inference for group-sparse linear models. - Fan Yang; Rina Foygel Barber; Prateek Jain 002; John D. Lafferty (NIPS, 2016)

Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent. - Qinqing Zheng; John D. Lafferty (CoRR, 2016)

ESP: A Machine Learning Approach to Predicting Application Interference. - Nikita Mishra; John D. Lafferty; Henry Hoffmann (ICAC, 2017)

Testing Network Structure Using Relations Between Small Subgraph Probabilities. - Chao Gao; John Lafferty (CoRR, 2017)

Testing for Global Network Structure Using Small Subgraph Statistics. - Chao Gao; John Lafferty (CoRR, 2017)

Information Retrieval as Statistical Translation. - Adam L. Berger; 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)

A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. - Chengxiang Zhai; John D. Lafferty (SIGIR Forum, 2017)

CALOREE: Learning Control for Predictable Latency and Low Energy. - Nikita Mishra; Connor Imes; John D. Lafferty; Henry Hoffmann (ASPLOS, 2018)

Prediction Rule Reshaping. - Matt Bonakdarpour; Sabyasachi Chatterjee; Rina Foygel Barber; John Lafferty (ICML, 2018)

Distributed Nonparametric Regression under Communication Constraints. - Yuancheng Zhu; John Lafferty (ICML, 2018)

Denoising Flows on Trees. - Sabyasachi Chatterjee; John D. Lafferty (IEEE Trans. Inf. Theory, 2018)

TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts. - Michihiro Yasunaga; John D. Lafferty (AAAI, 2019)

Surfing: Iterative Optimization Over Incrementally Trained Deep Networks. - Ganlin Song; Zhou Fan; John Lafferty (NeurIPS, 2019)

Fair quantile regression. - Dana Yang; John Lafferty; David Pollard (CoRR, 2019)

Convergence and Alignment of Gradient Descent with Random Backpropagation Weights. - Ganlin Song; Ruitu Xu; John Lafferty (NeurIPS, 2021)

Convergence and Alignment of Gradient Descentwith Random Back propagation Weights. - Ganlin Song; Ruitu Xu; John Lafferty (CoRR, 2021)

Emergent organization of receptive fields in networks of excitatory and inhibitory neurons. - Leon Lufkin; Ashish Puri; Ganlin Song; Xinyi Zhong; John Lafferty (CoRR, 2022)

From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces. - Qi Lin; Zifan Li; John Lafferty; Ilker Yildirim (CoRR, 2023)

Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning. - Awni Altabaa; Taylor W. Webb; Jonathan D. Cohen 003; 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 003 (CoRR, 2023)

Relational Convolutional Networks: A framework for learning representations of hierarchical relations. - Awni Altabaa; John Lafferty (CoRR, 2023)

Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers. - Awni Altabaa; Taylor Whittington Webb; Jonathan D. Cohen 003; John Lafferty (ICLR, 2024)

Approximation of relation functions and attention mechanisms. - Awni Altabaa; John Lafferty (CoRR, 2024)

Disentangling and Integrating Relational and Sensory Information in Transformer Architectures. - Awni Altabaa; John Lafferty (CoRR, 2024)

Learning Hierarchical Relational Representations through Relational Convolutions. - Awni Altabaa; John Lafferty (Trans. Mach. Learn. Res., 2024)

CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision. - Awni Altabaa; Omar Montasser; John Lafferty (CoRR, 2025)