Lawrence K. Saul

134 papers A* 19A 19B 1C 3Misc 3Journal 44Unranked 41

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AC$\oplus$DC search: behind the winning solution to the FlyWire graph-matching challenge. Trans. Mach. Learn. Res. Daniel D. Lee, Arie Matsliah, Lawrence K. Saul
2025	A	`conf`	Batch, match, and patch: low-rank approximations for score-based variational inference. AISTATS Chirag Modi, Diana Cai, Lawrence K. Saul
2025	J	`jnl`	CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning. CoRR Ningyuan Huang, Richard Stiskalek, Jun-Young Lee, Adrian E. Bayer, Charles C. Margossian, Christian Kragh Jespersen, Lucia A. Perez, Lawrence K. Saul, Francisco Villaescusa-Navarro
2025	J	`jnl`	Fisher meets Feynman: score-based variational inference with a product of experts. CoRR Diana Cai, Robert M. Gower, David M. Blei, Lawrence K. Saul
2025	J	`jnl`	Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry. CoRR Charles C. Margossian, Lawrence K. Saul
2025	J	`jnl`	Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs. J. Mach. Learn. Res. Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
2025	A	`conf`	Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix. AISTATS Charles Margossian, Lawrence K. Saul
2024	J	`jnl`	An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. CoRR Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
2024	A*	`conf`	Batch and match: black-box variational inference with a score-based divergence. ICML Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	Batch and match: black-box variational inference with a score-based divergence. CoRR Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	Batch, match, and patch: low-rank approximations for score-based variational inference. CoRR Chirag Modi, Diana Cai, Lawrence K. Saul
2024	A*	`conf`	EigenVI: score-based variational inference with orthogonal function expansions. NeurIPS Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	EigenVI: score-based variational inference with orthogonal function expansions. CoRR Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix. CoRR Charles C. Margossian, Lawrence K. Saul
2023	A	`conf`	The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference. UAI Charles C. Margossian, Lawrence K. Saul
2023	A*	`conf`	Variational Inference with Gaussian Score Matching. NeurIPS Chirag Modi, Robert M. Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul
2023	J	`jnl`	Variational Inference with Gaussian Score Matching. CoRR Chirag Modi, Charles Margossian, Yuling Yao, Robert M. Gower, David M. Blei, Lawrence K. Saul
2023	J	`jnl`	Weight-balancing fixes and flows for deep learning. Trans. Mach. Learn. Res. Lawrence K. Saul
2022	J	`jnl`	A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data. SIAM J. Math. Data Sci. Lawrence K. Saul
2022	J	`jnl`	A geometrical connection between sparse and low-rank matrices and its application to manifold learning. Trans. Mach. Learn. Res. Lawrence K. Saul
2022	J	`jnl`	Measuring security practices. Commun. ACM Louis F. DeKoven, Audrey Randall, Ariana Mirian, Gautam Akiwate, Ansel Blume, Lawrence K. Saul, Aaron Schulman, Geoffrey M. Voelker, Stefan Savage
2021	J	`jnl`	An EM Algorithm for Capsule Regression. Neural Comput. Lawrence K. Saul
2021	A*	`conf`	An online passive-aggressive algorithm for difference-of-squares classification. NeurIPS Lawrence K. Saul
2020	—	`conf`	Generating correctness proofs with neural networks. MAPL@PLDI Alex Sanchez-Stern, Yousef Alhessi, Lawrence K. Saul, Sorin Lerner
2020	—	`conf`	SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference Vraj Shah, Side Li, Arun Kumar, Lawrence K. Saul
2019	A	`conf`	"Unobserved Corner" Prediction: Reducing Timing Analysis Effort for Faster Design Convergence in Advanced-Node Design. DATE Andrew B. Kahng, Uday Mallappa, Lawrence K. Saul, Shangyuan Tong
2019	—	`conf`	Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference Vraj Shah, Side Li, Kevin Yang, Arun Kumar, Lawrence K. Saul
2019	J	`jnl`	Generating Correctness Proofs with Neural Networks. CoRR Alex Sanchez-Stern, Yousef Alhessi, Lawrence K. Saul, Sorin Lerner
2019	A	`conf`	Measuring Security Practices and How They Impact Security. Internet Measurement Conference Louis F. DeKoven, Audrey Randall, Ariana Mirian, Gautam Akiwate, Ansel Blume, Lawrence K. Saul, Aaron Schulman, Geoffrey M. Voelker, Stefan Savage
2018	C	`conf`	Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis. ICCD Andrew B. Kahng, Uday Mallappa, Lawrence K. Saul
2017	—	`conf`	HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations. HICSS Nadir Weibel, Purvi Desai, Lawrence K. Saul, Amarnath Gupta, Susan Little
2017	—	`conf`	SpeakQL: Towards Speech-driven Multi-modal Querying. HILDA@SIGMOD Dharmil Chandarana, Vraj Shah, Arun Kumar, Lawrence K. Saul
2016	J	`jnl`	Failure analysis and prediction for the CIPRES science gateway. Concurr. Comput. Pract. Exp. Kritika Singh, Shava Smallen, Sameer Tilak, Lawrence K. Saul
2016	J	`jnl`	On the (In)effectiveness of Mosaicing and Blurring as Tools for Document Redaction. Proc. Priv. Enhancing Technol. Steven Hill, Zhimin Zhou, Lawrence K. Saul, Hovav Shacham
2015	A	`conf`	From .academy to .zone: An Analysis of the New TLD Land Rush. Internet Measurement Conference Tristan Halvorson, Matthew F. Der, Ian D. Foster, Stefan Savage, Lawrence K. Saul, Geoffrey M. Voelker
2015	A	`conf`	Who is .com?: Learning to Parse WHOIS Records. Internet Measurement Conference Suqi Liu, Ian D. Foster, Stefan Savage, Geoffrey M. Voelker, Lawrence K. Saul
2014	A	`conf`	A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data. AISTATS Do-kyum Kim, Matthew F. Der, Lawrence K. Saul
2014	A*	`conf`	Knock it off: profiling the online storefronts of counterfeit merchandise. KDD Matthew F. Der, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2014	A	`conf`	Search + Seizure: The Effectiveness of Interventions on SEO Campaigns. Internet Measurement Conference David Y. Wang, Matthew F. Der, Mohammad Karami, Lawrence K. Saul, Damon McCoy, Stefan Savage, Geoffrey M. Voelker
2014	J	`jnl`	Topic Modeling of Hierarchical Corpora. CoRR Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul
2013	—	`conf`	A Variational Approximation for Topic Modeling of Hierarchical Corpora. ICML (2) Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul
2013	J	`jnl`	Large Deviation Methods for Approximate Probabilistic Inference CoRR Michael J. Kearns, Lawrence K. Saul
2013	Misc	`conf`	eDoctor: Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones. NSDI Xiao Ma, Peng Huang, Xinxin Jin, Pei Wang, Soyeon Park, Dongcai Shen, Yuanyuan Zhou, Lawrence K. Saul, Geoffrey M. Voelker
2012	—	`conf`	Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning. NIPS Matthew F. Der, Lawrence K. Saul
2011	J	`jnl`	Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification CoRR Youngmin Cho, Lawrence K. Saul
2011	A	`conf`	Hidden-Unit Conditional Random Fields. AISTATS Laurens van der Maaten, Max Welling, Lawrence K. Saul
2011	—	`conf`	Judging a site by its content: learning the textual, structural, and visual features of malicious web pages. AISec Sushma Nagesh Bannur, Lawrence K. Saul, Stefan Savage
2011	J	`jnl`	Learning to detect malicious URLs. ACM Trans. Intell. Syst. Technol. Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2011	—	`conf`	Maximum Covariance Unfolding : Manifold Learning for Bimodal Data. NIPS Vijay Mahadevan, Chi Wah Wong, José Costa Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul
2011	J	`jnl`	Nonnegative Matrix Factorization for Semi-supervised Dimensionality Reduction CoRR Youngmin Cho, Lawrence K. Saul
2011	—	`conf`	Online learning of large margin hidden Markov models for automatic speech recognition. MLSLP Lawrence K. Saul, Chih-Chieh Cheng, Fei Sha
2011	—	`conf`	Topic modeling of freelance job postings to monitor web service abuse. AISec Do-kyum Kim, Marti Motoyama, Geoffrey M. Voelker, Lawrence K. Saul
2010	A*	`conf`	Beyond heuristics: learning to classify vulnerabilities and predict exploits. KDD Mehran Bozorgi, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2010	J	`jnl`	Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]. IEEE Signal Process. Mag. Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
2010	A	`conf`	Exploiting Feature Covariance in High-Dimensional Online Learning. AISTATS Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira
2010	J	`jnl`	Large-Margin Classification in Infinite Neural Networks. Neural Comput. Youngmin Cho, Lawrence K. Saul
2010	—	`conf`	Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. NIPS Diane Hu, Laurens van der Maaten, Youngmin Cho, Lawrence K. Saul, Sorin Lerner
2010	J	`jnl`	Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models. IEEE J. Sel. Top. Signal Process. Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
2009	—	`conf`	A Probabilistic Topic Model for Unsupervised Learning of Musical Key-Profiles. ISMIR Diane Hu, Lawrence K. Saul
2009	A	`conf`	A fast online algorithm for large margin training of continuous density hidden Markov models. INTERSPEECH Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
2009	A*	`conf`	Beyond blacklists: learning to detect malicious web sites from suspicious URLs. KDD Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2009	J	`jnl`	Distance Metric Learning for Large Margin Nearest Neighbor Classification. J. Mach. Learn. Res. Kilian Q. Weinberger, Lawrence K. Saul
2009	A*	`conf`	Identifying suspicious URLs: an application of large-scale online learning. ICML Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2009	—	`conf`	Kernel Methods for Deep Learning. NIPS Youngmin Cho, Lawrence K. Saul
2009	C	`conf`	Large-margin feature adaptation for automatic speech recognition. ASRU Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
2009	A*	`conf`	Learning dictionaries of stable autoregressive models for audio scene analysis. ICML Youngmin Cho, Lawrence K. Saul
2009	A*	`conf`	Matrix updates for perceptron training of continuous density hidden Markov models. ICML Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
2009	Misc	`conf`	Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models. ICASSP Youngmin Cho, Lawrence K. Saul
2009	J	`jnl`	Technical perspective - The ultimate pilot program. Commun. ACM Stuart Russell, Lawrence K. Saul
2008	A*	`conf`	Fast solvers and efficient implementations for distance metric learning. ICML Kilian Q. Weinberger, Lawrence K. Saul
2008	C	`conf`	Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data. ICMLA Joshua M. Lewis, Pincelli M. Hull, Kilian Q. Weinberger, Lawrence K. Saul
2008	Misc	`conf`	Nonnegative matrix factorization for real time musical analysis and sight-reading evaluation. ICASSP Chih-Chieh Cheng, D. Jingtong Hu, Lawrence K. Saul
2007	—	`conf`	Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. ICASSP (4) Fei Sha, Lawrence K. Saul
2007	B	`conf`	Multiplicative Updates for IDA Fei Sha, Y. Albert Park, Lawrence K. Saul
2007	J	`jnl`	Multiplicative Updates for Nonnegative Quadratic Programming. Neural Comput. Fei Sha, Yuanqing Lin, Lawrence K. Saul, Daniel D. Lee
2006	A*	`conf`	An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding. AAAI Kilian Q. Weinberger, Lawrence K. Saul
2006	—	`conf`	Graph Laplacian Regularization for Large-Scale Semidefinite Programming. NIPS Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul
2006	J	`jnl`	IDES: An Internet Distance Estimation Service for Large Networks. IEEE J. Sel. Areas Commun. Yun Mao, Lawrence K. Saul, Jonathan M. Smith
2006	—	`conf`	Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. ICASSP (1) Fei Sha, Lawrence K. Saul
2006	—	`conf`	Large Margin Hidden Markov Models for Automatic Speech Recognition. NIPS Fei Sha, Lawrence K. Saul
2006	—	`ch.`	Spectral Methods for Dimensionality Reduction. Semi-Supervised Learning Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee
2006	J	`jnl`	Unsupervised Learning of Image Manifolds by Semidefinite Programming. Int. J. Comput. Vis. Kilian Q. Weinberger, Lawrence K. Saul
2005	A*	`conf`	Analysis and extension of spectral methods for nonlinear dimensionality reduction. ICML Fei Sha, Lawrence K. Saul
2005	—	`conf`	Distance Metric Learning for Large Margin Nearest Neighbor Classification. NIPS Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul
2005	—	`conf`	Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. ISMIR John Ashley Burgoyne, Lawrence K. Saul
2005	A	`conf`	Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization. AISTATS Kilian Q. Weinberger, Benjamin Packer, Lawrence K. Saul
2005	A	`conf`	Semisupervised alignment of manifolds. AISTATS Jihun Ham, Daniel D. Lee, Lawrence K. Saul
2005	—	`conf`	Visualization of low Dimensional Structure in tonal pitch Space. ICMC John Ashley Burgoyne, Lawrence K. Saul
2004	—	`ed.`	Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada] NIPS Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf
2004	—	`conf`	Exploratory analysis and visualization of speech and music by locally linear embedding. ICASSP (3) Viren Jain, Lawrence K. Saul
2004	—	`conf`	Hierarchical Distributed Representations for Statistical Language Modeling. NIPS John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira
2004	A*	`conf`	Learning a kernel matrix for nonlinear dimensionality reduction. ICML Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
2004	A	`conf`	Modeling distances in large-scale networks by matrix factorization. Internet Measurement Conference Yun Mao, Lawrence K. Saul
2004	—	`conf`	Multiband statistical learning for f ICASSP (5) Fei Sha, John Ashley Burgoyne, Lawrence K. Saul
2004	—	`conf`	Nonnegative deconvolution for time of arrival estimation. ICASSP (2) Yuanqing Lin, Daniel D. Lee, Lawrence K. Saul
2004	—	`conf`	Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. NIPS Fei Sha, Lawrence K. Saul
2004	—	`conf`	Unsupervised Learning of Image Manifolds by Semidefinite Programming. CVPR (2) Kilian Q. Weinberger, Lawrence K. Saul
2003	A	`conf`	A Generalized Linear Model for Principal Component Analysis of Binary Data. AISTATS Andrew I. Schein, Lawrence K. Saul, Lyle H. Ungar
2003	A*	`conf`	Multiplicative Updates for Large Margin Classifiers. COLT Fei Sha, Lawrence K. Saul, Daniel D. Lee
2003	A	`conf`	Statistical signal processing with nonnegativity constraints. INTERSPEECH Lawrence K. Saul, Fei Sha, Daniel D. Lee
2003	J	`jnl`	Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold. J. Mach. Learn. Res. Lawrence K. Saul, Sam T. Roweis
2002	—	`conf`	Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS Fei Sha, Lawrence K. Saul, Daniel D. Lee
2002	—	`conf`	Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. NIPS Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell Jr., Yann LeCun
2001	J	`jnl`	A statistical model for robust integration of narrowband cues in speech. Comput. Speech Lang. Lawrence K. Saul, Mazin G. Rahim, Jont B. Allen
2001	—	`conf`	Global Coordination of Local Linear Models. NIPS Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton
2001	—	`conf`	Multiplicative Updates for Classification by Mixture Models. NIPS Lawrence K. Saul, Daniel D. Lee
2001	J	`jnl`	Robust numeric recognition in spoken language dialogue. Speech Commun. Mazin G. Rahim, Giuseppe Riccardi, Lawrence K. Saul, Jeremy H. Wright, Bruce Buntschuh, Allen L. Gorin
2000	J	`jnl`	Attractor Dynamics in Feedforward Neural Networks. Neural Comput. Lawrence K. Saul, Michael I. Jordan
2000	J	`jnl`	Markov Processes on Curves. Mach. Learn. Lawrence K. Saul, Mazin G. Rahim
2000	J	`jnl`	Maximum likelihood and minimum classification error factor analysis for automatic speech recognition. IEEE Trans. Speech Audio Process. Lawrence K. Saul, Mazin G. Rahim
2000	—	`conf`	Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech. NIPS Lawrence K. Saul, Jont B. Allen
1999	J	`jnl`	An Introduction to Variational Methods for Graphical Models. Mach. Learn. Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1999	J	`jnl`	Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Mach. Learn. Lawrence K. Saul, Michael I. Jordan
1999	—	`conf`	Modeling the rate of speech by Markov processes on curves. EUROSPEECH Lawrence K. Saul, Mazin G. Rahim
1998	—	`ch.`	A Mean Field Learning Algorithm for Unsupervised Neural Networks. Learning in Graphical Models Lawrence K. Saul, Michael I. Jordan
1998	—	`ch.`	An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1998	A*	`conf`	Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time. ICML Lawrence K. Saul
1998	—	`conf`	Inference in Multilayer Networks via Large Deviation Bounds. NIPS Michael J. Kearns, Lawrence K. Saul
1998	A	`conf`	Large Deviation Methods for Approximate Probabilistic Inference. UAI Michael J. Kearns, Lawrence K. Saul
1998	—	`conf`	Markov Processes on Curves for Automatic Speech Recognition. NIPS Lawrence K. Saul, Mazin G. Rahim
1997	J	`jnl`	Aggregate and mixed-order Markov models for statistical language processing CoRR Lawrence K. Saul, Fernando Pereira
1997	A*	`conf`	Aggregate and mixed-order Markov models for statistical language processing. EMNLP Lawrence K. Saul, Fernando Pereira
1997	A	`conf`	Mixed Memory Markov Models. AISTATS Lawrence K. Saul, Michael I. Jordan
1997	—	`conf`	Modeling Acoustic Correlations by Factor Analysis. NIPS Lawrence K. Saul, Mazin G. Rahim
1996	—	`conf`	A Variational Principle for Model-based Morphing. NIPS Lawrence K. Saul, Michael I. Jordan
1996	—	`conf`	Hidden Markov Decision Trees. NIPS Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul
1996	A*	`conf`	Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. COLT Lawrence K. Saul, Satinder P. Singh
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks CoRR Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1995	—	`conf`	Exploiting Tractable Substructures in Intractable Networks. NIPS Lawrence K. Saul, Michael I. Jordan
1995	—	`conf`	Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS Tommi S. Jaakkola, Lawrence K. Saul, Michael I. Jordan
1995	A*	`conf`	Markov Decision Processes in Large State Spaces. COLT Lawrence K. Saul, Satinder P. Singh
1994	—	`conf`	Boltzmann Chains and Hidden Markov Models. NIPS Lawrence K. Saul, Michael I. Jordan
1994	J	`jnl`	Learning in Boltzmann Trees. Neural Comput. Lawrence K. Saul, Michael I. Jordan