Automated Machine Learning and Data Science [AMLDS] Publications
2017
Automatic Frankensteining: Creating Complex Ensembles Autonomously
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 741-749, 2017
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 741-749, 2017
Learning Feature Engineering for Classification
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias Khalil, Deepak Turaga
IJCAI, 2017
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias Khalil, Deepak Turaga
IJCAI, 2017
Foresight: Recommending Visual Insights [Demo and Workshop]
Cagatay Demiralp, PeterJ. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
VLDB Demo Track. This paper has also been accepted for oral presentation at KDD IDEA 2017 Workshop.
Cagatay Demiralp, PeterJ. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
VLDB Demo Track. This paper has also been accepted for oral presentation at KDD IDEA 2017 Workshop.
REMIX: Automated Exploration for Interactive Outlier Detection
Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong
23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017
Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong
23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017
Outlier Detection with Autoencoder Ensembles
Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga, Deepak
Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90--98
Abstract
Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga, Deepak
Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90--98
Abstract
Feature Engineering for Predictive Modeling using Reinforcement Learning
Khurana, Udayan and Samulowitz, Horst and Turaga, Deepak
arXiv preprint arXiv:1709.07150, 2017
Abstract
Khurana, Udayan and Samulowitz, Horst and Turaga, Deepak
arXiv preprint arXiv:1709.07150, 2017
Abstract
System and Apparatus for Automatic Feature Engineering from Relational Databases for Predictive Modelling
Thanh Lam Hoang, Inventor Johann-Michael Thiebaut, Tiep Mai, Bei Chen, MATHIEU SINN
Thanh Lam Hoang, Inventor Johann-Michael Thiebaut, Tiep Mai, Bei Chen, MATHIEU SINN
2016
Hyperparameter Optimization Machines
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 41-50, 2016
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 41-50, 2016
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 199-214, 2016
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 199-214, 2016
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts
Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 33-48, 2016
Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 33-48, 2016
Selecting Near-Optimal Learners via Incremental Data Allocation.
Sabharwal, Ashish and Samulowitz, Horst and Tesauro, Gerald
AAAI, pp. 2007--2015, 2016
Abstract
Sabharwal, Ashish and Samulowitz, Horst and Tesauro, Gerald
AAAI, pp. 2007--2015, 2016
Abstract
Deep Learning for Algorithm Portfolios.
Loreggia, Andrea and Malitsky, Yuri and Samulowitz, Horst and Saraswat, Vijay A
AAAI, pp. 1280--1286, 2016
Abstract
Loreggia, Andrea and Malitsky, Yuri and Samulowitz, Horst and Saraswat, Vijay A
AAAI, pp. 1280--1286, 2016
Abstract
Adaptive data augmentation for image classification
Fawzi, Alhussein and Samulowitz, Horst and Turaga, Deepak and Frossard, Pascal
Image Processing (ICIP), 2016 IEEE International Conference on, pp. 3688--3692
Abstract
Fawzi, Alhussein and Samulowitz, Horst and Turaga, Deepak and Frossard, Pascal
Image Processing (ICIP), 2016 IEEE International Conference on, pp. 3688--3692
Abstract
Image inpainting through neural networks hallucinations
Fawzi, Alhussein and Samulowitz, Horst and Turaga, Deepak and Frossard, Pascal
Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016 IEEE 12th, pp. 1--5
Abstract
Fawzi, Alhussein and Samulowitz, Horst and Turaga, Deepak and Frossard, Pascal
Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016 IEEE 12th, pp. 1--5
Abstract
Automating Feature Engineering
Udayan Khurana, Fatemeh Nargesian, Horst Samulowitz, Elias Khalil, Deepak Turaga
NIPS workshop on Artificial Intelligence for Data Science, 2016
Udayan Khurana, Fatemeh Nargesian, Horst Samulowitz, Elias Khalil, Deepak Turaga
NIPS workshop on Artificial Intelligence for Data Science, 2016
Graph-based Exploration of Non-graph Datasets
Udayan Khurana, Srinivasan Parthasarathy, Deepak Turaga
VLDB, 2016
Udayan Khurana, Srinivasan Parthasarathy, Deepak Turaga
VLDB, 2016
AUTOMATIC ENUMERATION OF DATA ANALYSIS OPTIONS AND RAPID ANALYSIS OF STATISTICAL MODELS
Khurana, Udayan and Parthasarathy, Srinivasan and Pavuluri, Venkata N and Turaga, Deepak S and Vu, Long H
US Patent 20,160,110,410
Khurana, Udayan and Parthasarathy, Srinivasan and Pavuluri, Venkata N and Turaga, Deepak S and Vu, Long H
US Patent 20,160,110,410
Cognito: Automated Feature Engineering for Supervised Learning
Udayan Khurana, Deepak Turaga, Horst Samulowitz, Srinivasan Parthasrathy
ICDM, 2016
Udayan Khurana, Deepak Turaga, Horst Samulowitz, Srinivasan Parthasrathy
ICDM, 2016
Graph-based Exploration of Non-graph Datasets [Demo]
Udayan Khurana, Srinivasan Parthasarathy, Deepak S. Turaga
VLDB Demo Paper, 2016
Udayan Khurana, Srinivasan Parthasarathy, Deepak S. Turaga
VLDB Demo Paper, 2016
2015
Hyperparameter Optimization with Factorized Multilayer Perceptrons
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 87-103, 2015
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 87-103, 2015
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 104-119, 2015
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 104-119, 2015
Learning Hyperparameter Optimization Initializations
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-10, 2015
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-10, 2015
Sequential Model-Free Hyperparameter Tuning
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Mining (ICDM), pp. 1033-1038, 2015
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
IEEE International Conference on Data Mining (ICDM), pp. 1033-1038, 2015
Learning Data Set Similarities for Hyperparameter Optimization Initializations.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 15-26
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 15-26
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 72-79, 2015
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 72-79, 2015
Deep Learning for Algorithm Portfolios
Andrea Loreggia, Yuri Malitsky, Horst Samulowitz, Vijay Saraswat
2015 - cs.toronto.edu
Andrea Loreggia, Yuri Malitsky, Horst Samulowitz, Vijay Saraswat
2015 - cs.toronto.edu
Automated intelligent data navigation and prediction tool
Klinger, Tamir and Reddy, Chandrasekhara K and Sabharwal, Ashish and Samulowitz, Horst C and Tesauro, Gerald J and Turaga, Deepak S
US Patent App. 14/812,344
Abstract
Klinger, Tamir and Reddy, Chandrasekhara K and Sabharwal, Ashish and Samulowitz, Horst C and Tesauro, Gerald J and Turaga, Deepak S
US Patent App. 14/812,344
Abstract
Budgeted Prediction with Expert Advice.
Amin, Kareem and Kale, Satyen and Tesauro, Gerald and Turaga, Deepak S
AAAI, pp. 2490--2496, 2015
Abstract
Amin, Kareem and Kale, Satyen and Tesauro, Gerald and Turaga, Deepak S
AAAI, pp. 2490--2496, 2015
Abstract
Towards Cognitive Automation of Data Science.
A.Biem, M.Butrico, M.Feblowitz, T.Klinger, Y.Malitsky, K.Ng, A.Perer, C.Reddy, A.Riabov, H.Samulowitz, D.Sow, G.Tesauro, D.Turaga
AAAI, pp. 4268--4269, 2015
A.Biem, M.Butrico, M.Feblowitz, T.Klinger, Y.Malitsky, K.Ng, A.Perer, C.Reddy, A.Riabov, H.Samulowitz, D.Sow, G.Tesauro, D.Turaga
AAAI, pp. 4268--4269, 2015
2014
Large Scale Discriminative Metric Learning
Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem
ParLearning, 2014
Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem
ParLearning, 2014
2013
Managing a portfolio of experts
Stern, David and Samulowitz, Horst Cornelius and Herbrich, Ralf and Graepel, Thore
US Patent 8,433,660
Abstract
Stern, David and Samulowitz, Horst Cornelius and Herbrich, Ralf and Graepel, Thore
US Patent 8,433,660
Abstract
Resolution and parallelizability: barriers to the effficient parallelization of SAT solvers
Sabharwal, Ashish and Samulowitz, Horst and Simon, Laurent and others
Twenty-Seventh AAAI Conference on Artificial Intelligence. 2013; AAAI 2013-27th AAAI Conference, Bellevue, USA, 2013-07-14-2013-07-18,
Abstract
Sabharwal, Ashish and Samulowitz, Horst and Simon, Laurent and others
Twenty-Seventh AAAI Conference on Artificial Intelligence. 2013; AAAI 2013-27th AAAI Conference, Bellevue, USA, 2013-07-14-2013-07-18,
Abstract
Real-time analysis and management of big time-series data
Alain Biem, Hanhua Feng, AV Riabov, Deepak S Turaga
IBM Journal of Research and Development 57(3/4), 8--1, IBM, 2013
Alain Biem, Hanhua Feng, AV Riabov, Deepak S Turaga
IBM Journal of Research and Development 57(3/4), 8--1, IBM, 2013
Stronger inference through implied literals from conflicts and knapsack covers
Tobias Achterberg, Ashish Sabharwal, Horst Samulowitz
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 1--11, Springer, 2013
Tobias Achterberg, Ashish Sabharwal, Horst Samulowitz
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 1--11, Springer, 2013
Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Learning and Intelligent Optimization, pp. 153--167, Springer, 2013
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Learning and Intelligent Optimization, pp. 153--167, Springer, 2013
Snappy: A simple algorithm portfolio
Horst Samulowitz, Chandra Reddy, Ashish Sabharwal, Meinolf Sellmann
Theory and Applications of Satisfiability Testing--SAT 2013, pp. 422--428, Springer
Horst Samulowitz, Chandra Reddy, Ashish Sabharwal, Meinolf Sellmann
Theory and Applications of Satisfiability Testing--SAT 2013, pp. 422--428, Springer
Algorithm portfolios based on cost-sensitive hierarchical clustering
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pp. 608--614, 2013
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pp. 608--614, 2013
Automated Design of Search with Composability
Ashish Sabharwal, Horst Samulowitz, Tom Schrijvers, Peter J Stuckey, Guido Tack
Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013
Ashish Sabharwal, Horst Samulowitz, Tom Schrijvers, Peter J Stuckey, Guido Tack
Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013
2012
Parallel SAT solver selection and scheduling
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Principles and Practice of Constraint Programming, pp. 512--526, 2012
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Principles and Practice of Constraint Programming, pp. 512--526, 2012
Augmenting clause learning with implied literals
Arie Matsliah, Ashish Sabharwal, Horst Samulowitz
Theory and Applications of Satisfiability Testing--SAT 2012, pp. 500--501, Springer
Arie Matsliah, Ashish Sabharwal, Horst Samulowitz
Theory and Applications of Satisfiability Testing--SAT 2012, pp. 500--501, Springer
SatX10: A Scalable Plug&Play Parallel SAT Framework
Bard Bloom, David Grove, Benjamin Herta, Ashish Sabharwal, Horst Samulowitz, Vijay Saraswat
Theory and Applications of Satisfiability Testing -- SAT 2012, Lecture Notes in Computer Science, pp. 463-468, Springer
Abstract
Bard Bloom, David Grove, Benjamin Herta, Ashish Sabharwal, Horst Samulowitz, Vijay Saraswat
Theory and Applications of Satisfiability Testing -- SAT 2012, Lecture Notes in Computer Science, pp. 463-468, Springer
Abstract
An Introduction to Temporal Graph Data Management
Khurana, Udayan
Technical Report, Technical report, May, 2012
Khurana, Udayan
Technical Report, Technical report, May, 2012
Guiding combinatorial optimization with UCT
Ashish Sabharwal, Horst Samulowitz, Chandra Reddy
Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems, 356--361, Springer, 2012
Ashish Sabharwal, Horst Samulowitz, Chandra Reddy
Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems, 356--361, Springer, 2012
2011
Algorithm selection and scheduling
Serdar Kadioglu, Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Principles and Practice of Constraint Programming--CP 2011, 454--469, Springer
Serdar Kadioglu, Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Principles and Practice of Constraint Programming--CP 2011, 454--469, Springer
Non-model-based algorithm portfolios for SAT
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Theory and Applications of Satisfiability Testing-SAT 2011, 369--370, Springer
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Theory and Applications of Satisfiability Testing-SAT 2011, 369--370, Springer
2010
Towards a lightweight standard search language
Horst Samulowitz, Guido Tack, Julien Fischer, Mark Wallace, Peter Stuckey
Constraint Modeling and Reformulation (ModRef’10), 2010
Horst Samulowitz, Guido Tack, Julien Fischer, Mark Wallace, Peter Stuckey
Constraint Modeling and Reformulation (ModRef’10), 2010
Collaborative expert portfolio management
David Stern, Ralf Herbrich, Thore Graepel, Horst Samulowitz, Luca Pulina, Armando Tacchella
Proc. of AAAI, pp. 210--216, 2010
David Stern, Ralf Herbrich, Thore Graepel, Horst Samulowitz, Luca Pulina, Armando Tacchella
Proc. of AAAI, pp. 210--216, 2010
2009
Experiments with massively parallel constraint solving
Lucas Bordeaux, Youssef Hamadi, Horst Samulowitz
Proceedings of the 21st international jont conference on Artifical intelligence, pp. 443--448, 2009
Lucas Bordeaux, Youssef Hamadi, Horst Samulowitz
Proceedings of the 21st international jont conference on Artifical intelligence, pp. 443--448, 2009
Learning adaptation to solve constraint satisfaction problems
Yuehua Xu, David Stern, Horst Samulowitz
Proc. of Third Workshop on Learning and Intelligent Optimization (LION’03), Trento, Italy, 2009
Yuehua Xu, David Stern, Horst Samulowitz
Proc. of Third Workshop on Learning and Intelligent Optimization (LION’03), Trento, Italy, 2009
2007
On the stochastic constraint satisfaction framework
Lucas Bordeaux, Horst Samulowitz
Proceedings of the 2007 ACM symposium on Applied computing, pp. 316--320
Lucas Bordeaux, Horst Samulowitz
Proceedings of the 2007 ACM symposium on Applied computing, pp. 316--320
Dynamically partitioning for solving QBF
Horst Samulowitz, Fahiem Bacchus
Theory and Applications of Satisfiability Testing--SAT 2007, 215--229, Springer
Horst Samulowitz, Fahiem Bacchus
Theory and Applications of Satisfiability Testing--SAT 2007, 215--229, Springer
Learning to solve QBF
Horst Samulowitz, Roland Memisevic
Proceedings of the national conference on artificial intelligence, pp. 255, 2007
Horst Samulowitz, Roland Memisevic
Proceedings of the national conference on artificial intelligence, pp. 255, 2007
Automated Machine Learning and Data Science
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