**Click to Read Paper and Get Code**

**Click to Read Paper and Get Code**

* Supplementary material attached as Ancillary File; in PKDD 2016: European Conference on Machine Learning and Knowledge Discovery in Databases

**Click to Read Paper and Get Code**

A Subsequence Interleaving Model for Sequential Pattern Mining

Nov 11, 2016

Jaroslav Fowkes, Charles Sutton

Nov 11, 2016

Jaroslav Fowkes, Charles Sutton

* 10 pages in KDD 2016: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

**Click to Read Paper and Get Code**

Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models

Jun 15, 2014

Yichuan Zhang, Charles Sutton

Jun 15, 2014

Yichuan Zhang, Charles Sutton

**Click to Read Paper and Get Code**

* Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)

**Click to Read Paper and Get Code**

* Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)

**Click to Read Paper and Get Code**

Bayesian inference for queueing networks and modeling of internet services

Apr 15, 2011

Charles Sutton, Michael I. Jordan

Apr 15, 2011

Charles Sutton, Michael I. Jordan

* Annals of Applied Statistics 2011, Vol. 5, No. 1, 254-282

* Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

**Click to Read Paper and Get Code**

Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields.

* 90 pages

* 90 pages

**Click to Read Paper and Get Code**
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code

Mar 13, 2019

Rafael-Michael Karampatsis, Charles Sutton

Mar 13, 2019

Rafael-Michael Karampatsis, Charles Sutton

**Click to Read Paper and Get Code**

* Published as a conference paper at ICLR 2015

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Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation

Jun 08, 2016

Akash Srivastava, James Zou, Charles Sutton

Jun 08, 2016

Akash Srivastava, James Zou, Charles Sutton

**Click to Read Paper and Get Code**

A Convolutional Attention Network for Extreme Summarization of Source Code

May 25, 2016

Miltiadis Allamanis, Hao Peng, Charles Sutton

May 25, 2016

Miltiadis Allamanis, Hao Peng, Charles Sutton

* Code, data and visualization at http://groups.inf.ed.ac.uk/cup/codeattention/

**Click to Read Paper and Get Code**

Latent Bayesian melding for integrating individual and population models

Oct 30, 2015

Mingjun Zhong, Nigel Goddard, Charles Sutton

Oct 30, 2015

Mingjun Zhong, Nigel Goddard, Charles Sutton

* 11 pages, Advances in Neural Information Processing Systems (NIPS), 2015. (Spotlight Presentation)

**Click to Read Paper and Get Code**

Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic

Nov 02, 2018

Maria I. Gorinova, Andrew D. Gordon, Charles Sutton

Nov 02, 2018

Maria I. Gorinova, Andrew D. Gordon, Charles Sutton

**Click to Read Paper and Get Code**

Learning Continuous Semantic Representations of Symbolic Expressions

Jun 10, 2017

Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton

Jun 10, 2017

Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton

* Accepted to ICML 2017

**Click to Read Paper and Get Code**

Learning Semantic Annotations for Tabular Data

May 30, 2019

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

May 30, 2019

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

* IJCAI 2019

* 7 pages

**Click to Read Paper and Get Code**

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

Nov 04, 2018

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

Nov 04, 2018

Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

* AAAI 2019

**Click to Read Paper and Get Code**

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models

May 31, 2018

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

May 31, 2018

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

* Code: https://github.com/akashgit/RM-MMDnet

**Click to Read Paper and Get Code**

A Survey of Machine Learning for Big Code and Naturalness

May 05, 2018

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton

May 05, 2018

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton

* Website accompanying this survey paper can be found at https://ml4code.github.io

**Click to Read Paper and Get Code**