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Search/Learning for optimization 

1. Marcela Zuluaga, Andreas Krause and Markus Püschel
   e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem
   Journal of Machine Learning Research, Vol. 17, No. 104, pp. 1-32, 2016
   
2. Marcela Zuluaga, Andreas Krause, Guillaume Sergent and Markus Püschel
   Active Learning for Multi-Objective Optimization
   Proc. International Conference on Machine Learning (ICML), pp. 462-470, 2013
   
3. Marcela Zuluaga, Andreas Krause and Markus Püschel
   Multi-Objective Optimization for High-Level Synthesis
   Proc. Workshop on High-Level Synthesis for High Performance Computing (HLS4HPC), 2013
   
4. Marcela Zuluaga, Andreas Krause, Peter A. Milder and Markus Püschel
   "Smart" Design Space Sampling to Predict Pareto-Optimal Solutions
   Proc. Languages, Compilers, Tools and Theory for Embedded Systems (LCTES), pp. 119-128 , 2012
   
5. Frédéric de Mesmay, Yevgen Voronenko and Markus Püschel
   Offline Library Adaptation Using Automatically Generated Heuristics
   Proc. IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1-10, 2010
   
6. Frédéric de Mesmay
   On the Computer Generation of Adaptive Numerical Libraries
   PhD. thesis, Electrical and Computer Engineering, Carnegie Mellon University, 2010
   
7. Frédéric de Mesmay, Arpad Rimmel, Yevgen Voronenko and Markus Püschel
   Bandit-Based Optimization on Graphs with Application to Library Performance Tuning
   Proc. International Conference on Machine Learning (ICML), pp. 729-736, 2009
   
8. Bryan Singer and Manuela Veloso
   Automating the Modeling and Optimization of the Performance of Signal Transforms
   IEEE Transactions on Signal Processing, Vol. 50, No. 8, pp. 2003-2014, 2002
   
9. Bryan Singer and Manuela Veloso
   Learning to Construct Fast Signal Processing Implementations
   Journal of Machine Learning Research, special issue on ``the Eighteenth International Conference on Machine Learning (ICML 2001)'', Vol. 3, pp. 887-919, 2002
   
10. Bryan Singer
    Automating the Modeling and Optimization of the Performance of Signal Processing Algorithms
    PhD. thesis, Computer Science, Carnegie Mellon University, 2001
    
11. Bryan Singer and Manuela Veloso
    Learning to Generate Fast Signal Processing Implementations
    Proc. International Conference on Machine Learning (ICML), pp. 529-536, 2001
    
12. Bryan Singer and Manuela Veloso
    Stochastic Search for Signal Processing Algorithm Optimization
    Proc. Supercomputing (SC), pp. 22, 2001
    
13. Pinit Kumhom, Jeremy Johnson and Prawat Nagvajara
    Design, optimization, and implementation of a universal FFT processor
    Proc. IEEE ASIC/SOC Conference, IEEE, pp. 182-186, 2000
    
14. Bryan Singer and Manuela Veloso
    Learning to Predict Performance from Formula Modeling and Training Data
    Proc. International Conference on Machine Learning (ICML), pp. 887-894, 2000
    

Publication interface designed and implemented by Patra Pantupat, Aliaksei Sandryhaila, and Markus Püschel
Electrical and Computer Engineering, Carnegie Mellon University, 2007