1. [TAKEN] Oleg Kiselyov, Chung-chieh Shan: Embedded Probabilistic Programming. DSL 2009: 360-384 http://okmij.org/ftp/kakuritu/dsl-paper.pdf
  2. Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas: Adaptive MCMC with Bayesian Optimization. AISTATS 2012: 751-760 http://www.cs.ubc.ca/~ziyuw/papers/mahendran12.pdf
  3. [TAKEN] Bhaskara Marthi, Hanna Pasula, Stuart J. Russell, Yuval Peres: Decayed MCMC Filtering. CoRR abs/1301.0584 (2013) http://arxiv.org/pdf/1301.0584v1.pdf
  4. CarloSeong-Hwan Jun, Alexandre Bouchard-Côté: Memory (and Time) Efficient Sequential Monte ; JMLR W&CP 32 (1) :514-522, 2014 http://jmlr.csail.mit.edu/proceedings/papers/v32/jun14.pdf
  5. Adnan Darwiche: Recursive conditioning. Artif. Intell. 126(1-2): 5-41 (2001) http://www.sciencedirect.com/science/article/pii/S0004370200000692# the "Download PDF" link is in the upper left corner
  6. Alex Graves, Greg Wayne, Ivo Danihelka: Neural Turing Machines. arXiv:1410.5401, 2014. http://arxiv.org/abs/1410.5401
  7. Lingfeng Yang, Pat Hanrahan, Noah D. Goodman: Generating Efficient MCMC Kernels from Probabilistic Programs. AISTATS 2014. http://jmlr.org/proceedings/papers/v33/yang14d.pdf
  8. [TAKEN] Michael A. Osborne, David K. Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, and Zoubin Ghahramani: Active Learning of Model Evidence Using Bayesian Quadrature, NIPS 2012. http://papers.nips.cc/paper/4657-active-learning-of-model-evidence-using-bayesian-quadrature.pdf
  9. Hal Daumé III, John Langford, Stephane Ross: Efficient programmable learning to search. arXiv:1406.1837 http://arxiv.org/abs/1406.1837
  10. Chris J Maddison and Daniel Tarlow: Structured Generative Models of Natural Source Code. ICML 2014 http://jmlr.org/proceedings/papers/v32/maddison14.pdf
  11. [TAKEN] Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani: Probabilistic Programming. ICSE 2014 http://research.microsoft.com/pubs/208585/fose-icse2014.pdf
  12. Tom Minka and John Winn: Gates: A graphical notation for mixture models. http://research.microsoft.com/pubs/78856/minka-gates-tr.pdf
  13. Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas: Bayesian Optimization in a Billion Dimensions via Random Embeddings. arXiv: 1301.1942 http://arxiv.org/pdf/1301.1942v1.pdf
  14. Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson and Adam Kalai: A Machine Learning Framework for Programming by Example . ICML 2013.http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2013_menon13.pdf
  15. [TAKEN] Adaptive MCMC (we expect all three papers to be presented together, since there is a lot of overlap)
    1. Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret: Adaptive Markov chain Monte Carlo: theory and methods. In: Bayesian Time Series Models http://dept.stat.lsa.umich.edu/~yvesa/afmp.pdf
    2. Christophe Andrieu, Johannes Thoms: A Tutorial on Adaptive MCMC. Statistics and Computing 18(4):343-373 (2008) http://link.springer.com/content/pdf/10.1007%2Fs11222-008-9110-y.pdf
    3. Gareth O. Roberts, Jeffrey S. Rosenthal: Examples of Adaptive MCMC. Journal of Computational and Graphical Statistics: Volume 18, Issue 2, 2009 http://amstat.tandfonline.com/doi/pdf/10.1198/jcgs.2009.06134