var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n\n \n \n\n \n\n\n\n
\n\n \n\n\n
\n \n\n Excellent! Next, you can embed this page using\n one of several\n options.\n
\n\n
\n \n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
  2. \n
  3. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.
  4. \n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2018\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (3)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Hartford, J.; Graham, D. R; Leyton-Brown, K.; and Ravanbakhsh, S.\n \n\n\n \n \n \n Deep Models of Interactions Across Sets.\n \n\n\n \n\n\n\n arXiv preprint arXiv:1803.02879. 2018.\n \n\nto appear in ICML'18\n\n
\n\n\n \n \n \n \"Deep arxiv\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@article{hartford2018deep,\n  title={Deep Models of Interactions Across Sets},\n  author={Hartford, Jason and Graham, Devon R and Leyton-Brown, Kevin and Ravanbakhsh, Siamak},\n  journal={arXiv preprint arXiv:1803.02879},\n  year={2018},\n  url_arXiv = {https://arxiv.org/abs/1803.02879},\n  Bibbase_Note={to appear in ICML'18}\n}\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n He, S.; Ravanbakhsh, S.; and Ho, S.\n \n\n\n \n \n \n Analysis of Cosmic Microwave Background with Deep Learning.\n \n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), workshop track, 2018. \n \n\n\n\n
\n\n\n \n \n \n \"Analysis pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{he_cmb,\n  title={Analysis of Cosmic Microwave Background with Deep Learning},\n  author={He, Siyu and Ravanbakhsh, Siamak and Ho, Shirley},\n  booktitle={International Conference on Learning Representations (ICLR), workshop track},\n  year={2018},\n  url_pdf = {https://openreview.net/pdf?id=B15uoOyvz},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Lanusse, F.; Ma, Q.; Li, N.; Collett, T. E.; Li, C.; Ravanbakhsh, S.; Mandelbaum, R.; and Póczos, B.\n \n\n\n \n \n \n CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding.\n \n\n\n \n\n\n\n Monthly Notices of the Royal Astronomical Society, 473(3): 3895-3906. 2018.\n \n\n\n\n
\n\n\n \n \n \n \"CMUPaper\n  \n \n \n \"CMU in the news\n  \n \n \n \"CMU code\n  \n \n \n \"CMU arxiv\n  \n \n\n \n \n doi\n  \n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@article{doi:10.1093/mnras/stx1665,\nauthor = {Lanusse, François and Ma, Quanbin and Li, Nan and Collett, Thomas E. and Li, Chun-Liang and Ravanbakhsh, Siamak and Mandelbaum, Rachel and Póczos, Barnabás},\ntitle = {CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding},\njournal = {Monthly Notices of the Royal Astronomical Society},\nvolume = {473},\nnumber = {3},\npages = {3895-3906},\nyear = {2018},\ndoi = {10.1093/mnras/stx1665},\nURL = {http://dx.doi.org/10.1093/mnras/stx1665},\nurl_in_the_news={http://www.cmu.edu/mcs/news-events/2017/0512-Strong-Lensing-Challenge.html},\nurl_code={https://github.com/McWilliamsCenter/CMUDeepLens},\nurl_arXiv = {https://arxiv.org/abs/1703.02642},  \n}\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (2)\n \n \n
\n
\n \n \n
\n
\n  \n 3\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Schneider, J.; and Poczos, B.\n \n\n\n \n \n \n Equivariance Through Parameter-Sharing.\n \n\n\n \n\n\n\n In Proceedings of the 34th International Conference on Machine Learning, volume 70, of JMLR: W&CP, August 2017. \n \n\n\n\n
\n\n\n \n \n \n \"Equivariance arxiv\n  \n \n \n \"Equivariance pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@InProceedings{ravanbakhsh_equivariance,\nauthor={Ravanbakhsh, Siamak and Schneider, Jeff and Poczos, Barnabas},\ntitle={Equivariance Through Parameter-Sharing},\norder={3},\n  booktitle = {Proceedings of the 34th International Conference on Machine Learning},\n  shortbooktitle = {ICML '17},\n  series =  {JMLR: W&CP},\n  Volume={70},  \n  year =      {2017},\n  location =  {Sydney, Australia, GB},\n  month =     {August},\n  url_arXiv = {https://arxiv.org/abs/1702.08389},\n  url_pdf = {http://proceedings.mlr.press/v70/ravanbakhsh17a/ravanbakhsh17a-supp.pdf}\n}\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n undefined\n \n \n (5)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Zaheer, M.; Kottur, S.; Ravanbakhsh, S.; Poczos, B.; Salakhutdinov, R. R; and Smola, A. J\n \n\n\n \n \n \n Deep Sets.\n \n\n\n \n\n\n\n In Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H.; Fergus, R.; Vishwanathan, S.; and Garnett, R., editor(s), Advances in Neural Information Processing Systems 30, pages 3391–3401, 2017. Curran Associates, Inc.\n \n\noral presentation (1.2% acceptance rate)\n\n
\n\n\n \n \n \n \"Deep pdf\n  \n \n \n \"Deep supplemental\n  \n \n \n \"Deep code\n  \n \n \n \"Deep arxiv\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@InProceedings{NIPS2017_6931,\ntitle = {Deep Sets},\nauthor = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Ruslan R and Smola, Alexander J},\nbooktitle = {Advances in Neural Information Processing Systems 30},\neditor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},\npages = {3391--3401},\nyear = {2017},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/6931-deep-sets.pdf},\nurl_supplemental = {https://papers.nips.cc/paper/6931-deep-sets-supplemental.zip},\nurl_code = {https://github.com/manzilzaheer/DeepSets},\nurl_arXiv = {https://arxiv.org/abs/1703.06114},\nBibbase_Note={<font color="#E74C3C">oral presentation (1.2\\% acceptance rate)</font>},\n}\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Srinivasa, C.; Givoni, I.; Ravanbakhsh, S.; and Frey, B. J\n \n\n\n \n \n \n Min-Max Propagation.\n \n\n\n \n\n\n\n In Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H.; Fergus, R.; Vishwanathan, S.; and Garnett, R., editor(s), Advances in Neural Information Processing Systems 30, pages 5565–5573, 2017. Curran Associates, Inc.\n \n\n\n\n
\n\n\n \n \n \n \"Min-Max pdf\n  \n \n \n \"Min-Max supplemental\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@InProceedings{NIPS2017_7140,\ntitle = {Min-Max Propagation},\nauthor = {Srinivasa, Christopher and Givoni, Inmar and Ravanbakhsh, Siamak and Frey, Brendan J},\nbooktitle = {Advances in Neural Information Processing Systems 30},\neditor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},\npages = {5565--5573},\nyear = {2017},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/7140-min-max-propagation.pdf},\nurl_supplemental = {http://papers.nips.cc/paper/7140-min-max-propagation-supplemental.zip}\n}\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Lanusse, F.; Mandelbaum, R.; Schneider, J.; and Poczos, B.\n \n\n\n \n \n \n Enabling Dark Energy Science with Deep Generative Models of Galaxy Images.\n \n\n\n \n\n\n\n In Proceedings of the Thirty First AAAI Conference on Artificial Intelligence, 2017. \n \n\n\n\n
\n\n\n \n \n \n \"Enabling arxiv\n  \n \n \n \"Enabling pdf\n  \n \n \n \"Enabling in nature news\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_gengalaxy,\n  title={Enabling Dark Energy Science with Deep Generative Models of Galaxy Images},\n  author={Ravanbakhsh, Siamak and Lanusse, Francois and Mandelbaum, Rachel and Schneider, Jeff and Poczos, Barnabas},\n  booktitle={Proceedings of the Thirty First AAAI Conference on Artificial Intelligence},\n  url_arXiv = {https://arxiv.org/abs/1609.05796},\n  url_pdf = {http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14765/13939},\n  url_in_Nature_News={http://www.nature.com/news/astronomers-explore-uses-for-ai-generated-images-1.21398},\n  year={2017},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Schneider, J.; and Poczos, B.\n \n\n\n \n \n \n Deep Learning with Sets and Point Clouds.\n \n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), workshop track, 2017. \n \n\n\n\n
\n\n\n \n \n \n \"Deep arxiv\n  \n \n \n \"Deep code\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_sets,\n  title={Deep Learning with Sets and Point Clouds},\n  author={Ravanbakhsh, Siamak and Schneider, Jeff and Poczos, Barnabas},\n  booktitle={International Conference on Learning Representations (ICLR), workshop track},\n  year={2017},\nurl_arXiv = {https://arxiv.org/abs/1611.04500},\nurl_code = {https://github.com/manzilzaheer/DeepSets},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Lanusse, F.; Ravanbakhsh, S.; Mandelbaum, R.; Schneider, J.; and Poczos, B.\n \n\n\n \n \n \n Deep Generative Models of Galaxy Images for the Calibration of the Next Generation of Weak Lensing Surveys.\n \n\n\n \n\n\n\n In American Astronomical Society Meeting Abstracts, volume 229, pages 342.05, January 2017. \n \n\n\n\n
\n\n\n \n \n \n \"Deep abstract\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@INPROCEEDINGS{lanusse_AAS_workshop,\n   author = {{Lanusse}, Francois, and {Ravanbakhsh}, Siamak and {Mandelbaum}, Rachel and \n\t{Schneider}, Jeff and {Poczos}, Barnabas},\n    title = "{Deep Generative Models of Galaxy Images for the Calibration of the Next Generation of Weak Lensing Surveys}",\nbooktitle = {American Astronomical Society Meeting Abstracts},\n     year = 2017,\n   series = {American Astronomical Society Meeting Abstracts},\n   volume = 229,\n    month = jan,\n      eid = {342.05},\n    pages = {342.05},\n    url_abstract = {http://adsabs.harvard.edu/abs/2017AAS...22934205L},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (4)\n \n \n
\n
\n \n \n
\n
\n  \n 4\n \n \n (2)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Poczos, B.; Schneider, J.; Schuurmans, D.; and Greiner, R.\n \n\n\n \n \n \n Stochastic Neural Networks with Monotonic Activation Functions.\n \n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics, volume 51, of JMLR: W&CP, pages 809–818, 2016. \n \n\noral presentation (6.5% acceptance rate)\n\n
\n\n\n \n \n \n \"Stochastic pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@INPROCEEDINGS{ravanbakhsh_exprbm,\n    author = {Ravanbakhsh, Siamak and Poczos, Barnabas and Schneider, Jeff and Schuurmans, Dale and Greiner, Russell},\n    title = {Stochastic Neural Networks with Monotonic Activation Functions},\n    Booktitle  = {International Conference on Artificial Intelligence and Statistics},\n    Shortbooktitle={AISTATS},\n\tSeries = {JMLR: W&CP},\n    order = {4},  \n    Volume={51},\n    Pages = {809–818},\n    year = {2016},\n    location = {Cadiz, Spain},\n url_pdf = {http://www.jmlr.org/proceedings/papers/v51/ravanbakhsh16.pdf},\nBibbase_Note={<font color="#E74C3C">oral presentation (6.5\\% acceptance rate)</font>}, \n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Póczos, B.; and Greiner, R.\n \n\n\n \n \n \n Boolean Matrix Factorization and Noisy Completion via Message Passing.\n \n\n\n \n\n\n\n In Proceedings of The 33rd International Conference on Machine Learning, volume 48, of JMLR: W&CP, 2016. \n \n\n\n\n
\n\n\n \n \n \n \"Boolean paper\n  \n \n \n \"Boolean code\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@INPROCEEDINGS{ravanbakhsh_boolean,\n  title = {Boolean Matrix Factorization and Noisy Completion via Message Passing},\n  author = {Ravanbakhsh, Siamak and P{\\'o}czos, Barnab{\\'a}s and Greiner, Russell},\n  booktitle = {Proceedings of The 33rd International Conference on Machine Learning},\n  Shortbooktitle={ICML},\n  Series = {JMLR: W&CP},\n  Volume={48},\n  order = {4},    \n  year = {2016},\n  location = {New York, USA},\n  url_Paper = {http://jmlr.org/proceedings/papers/v48/ravanbakhsha16.pdf},\n  url_Code = {https://github.com/mravanba/BooleanFactorization},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 5\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Oliva, J.; Fromenteau, S.; Price, L. C; Ho, S.; Schneider, J.; and Póczos, B.\n \n\n\n \n \n \n Estimating Cosmological Parameters from the Dark Matter Distribution.\n \n\n\n \n\n\n\n In Balcan, M.; and Weinberger, K., editor(s), Proceedings of The 33rd International Conference on Machine Learning, volume 48, of JMLR: W&CP, 2016. \n \n\n\n\n
\n\n\n \n \n \n \"Estimating paper\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@INPROCEEDINGS{ravanbakhsh_lambdacdm,\n  title={Estimating Cosmological Parameters from the Dark Matter Distribution},\n  author={Ravanbakhsh, Siamak and Oliva, Junier and Fromenteau, Sebastien and Price, Layne C and Ho, Shirley and Schneider, Jeff and P{\\'o}czos, Barnab{\\'a}s},\n  booktitle = {Proceedings of The 33rd International Conference on Machine Learning},\n  editor = {Maria Balcan and Kilian Weinberger},\n  Shortbooktitle={ICML},\n  Series = {JMLR: W&CP},\n  order = {5},      \n  Volume={48},\n  year = {2016},\n  location = {New York, USA},\n  url_Paper = {http://jmlr.org/proceedings/papers/v48/ravanbakhshb16.pdf},\n}\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 6\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Srinivasa, C.; Ravanbakhsh, S.; and Frey, B.\n \n\n\n \n \n \n Survey Propagation beyond Constraint Satisfaction Problems.\n \n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics, volume 51, of JMLR: W&CP, pages 286–295, 2016. \n \n\noral presentation (6.5% acceptance rate)\n\n
\n\n\n \n \n \n \"Survey pdf\n  \n \n \n \"Survey supplemental\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@INPROCEEDINGS{Srinivasa_sp,\n    author = {Srinivasa, Christopher and Ravanbakhsh, Siamak and Frey, Brendan},\n    title = {Survey Propagation beyond Constraint Satisfaction Problems},\n    Booktitle  = {International Conference on Artificial Intelligence and Statistics},\n    Shortbooktitle={AISTATS},\nSeries = {JMLR: W&CP},\n    Volume={51},\n    Pages = {286–295},\n    order = {6},  \n    year = {2016},\n    location = {Cadiz, Spain},\nurl_pdf={http://www.jmlr.org/proceedings/papers/v51/srinivasa16.pdf},\nurl_supplemental = {http://proceedings.mlr.press/v51/srinivasa16-supp.pdf},\nBibbase_Note={<font color="#E74C3C">oral presentation (6.5\\% acceptance rate)</font>},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n undefined\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Welle, P.; Ravanbakhsh, S.; Póczos, B.; and Mauter, M.\n \n\n\n \n \n \n Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing.\n \n\n\n \n\n\n\n In American Geophysical Union, Fall Meeting Abstracts, 2016. \n \n\n\n\n
\n\n\n \n \n \n \"Leveraging abstract\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{welle2016leveraging,\n  title={Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing},\n  author={Welle, Paul and Ravanbakhsh, Siamak and P{\\'o}czos, Barnabas and Mauter, Megan},\n  booktitle={American Geophysical Union, Fall Meeting Abstracts},\n  url_abstract = {http://adsabs.harvard.edu/abs/2016AGUFMIN51B1852W},\n  year={2016}\n}\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2015\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (3)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Mirzazadeh, F.; Ravanbakhsh, S.; Ding, N.; and Schuurmans, D.\n \n\n\n \n \n \n Embedding Inference for Structured Multilabel Prediction.\n \n\n\n \n\n\n\n In Cortes, C.; Lawrence, N. D.; Lee, D. D.; Sugiyama, M.; and Garnett, R., editor(s), Advances in Neural Information Processing Systems 28, pages 3555–3563. Curran Associates, Inc., 2015.\n \n\n\n\n
\n\n\n \n \n \n \"Embedding pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n  \n\n \n buy\n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@incollection{NIPS2015_5675,\ntitle = {Embedding Inference for Structured Multilabel Prediction},\nauthor = {Mirzazadeh, Farzaneh and Ravanbakhsh, Siamak and Ding, Nan and Schuurmans, Dale},\nbooktitle = {Advances in Neural Information Processing Systems 28},\neditor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},\npages = {3555--3563},\nyear = {2015},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/5675-embedding-inference-for-structured-multilabel-prediction.pdf}\n}\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; and Greiner, R.\n \n\n\n \n \n \n Perturbed Message Passing for Constraint Satisfaction Problems.\n \n\n\n \n\n\n\n Journal of Machine Learning Research, 16: 1249-1274. 2015.\n \n\n\n\n
\n\n\n \n \n \n \"Perturbed pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@article{ravanbakhsh_csp,\n  title={Perturbed Message Passing for Constraint Satisfaction Problems},\n  author={Ravanbakhsh, Siamak and Greiner, Russell},\n  journal={Journal of Machine Learning Research},\n  volume={16},\n  pages={1249-1274},\n  year={2015},\n  url_pdf = {http://www.jmlr.org/papers/volume16/ravanbakhsh15a/ravanbakhsh15a.pdf},\n}\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Liu, P.; Bjordahl, T.; Mandal, R.; Grant, J.; Wilson, M.; Eisner, R.; Sinelnikov, I.; Hu, X.; Luchinat, C.; Greiner, R.; and Wishart, D.\n \n\n\n \n \n \n Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics.\n \n\n\n \n\n\n\n PLoS ONE, 10(5): e0124219. 05 2015.\n \n\n\n\n
\n\n\n \n \n \n \"Accurate, arxiv\n  \n \n \n \"Accurate, pdf\n  \n \n \n \"Accurate, website\n  \n \n \n \"Accurate, in the news\n  \n \n\n \n \n doi\n  \n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@article{ravanbakhsh_plosone,\n    author = {Ravanbakhsh, Siamak and Liu, Philip and Bjordahl, Trent  and Mandal, Rupasri and Grant, Jason  and Wilson, Michael and Eisner, Roman and Sinelnikov, Igor and Hu, Xiaoyu and Luchinat, Claudio and Greiner, Russell and Wishart, David },\n    journal = {PLoS ONE},\n    publisher = {Public Library of Science},\n    title = {Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics},\n    year = {2015},\n    month = {05},\n    volume = {10},\n    pages = {e0124219},\n    number = {5},\n    doi = {10.1371/journal.pone.0124219},\n    url_arXiv = {https://arxiv.org/abs/1409.1456},\n\turl_pdf = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124219},\n\turl_WebSite = {http://bayesil.ca/},\n  url_in_the_news = {https://www.ualberta.ca/news-and-events/newsarticles/2015/may/machine-learning-breakthrough-could-revolutionize-medicine},  \t\n}        \n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2014\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (3)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Rabbany, R.; and Greiner, R.\n \n\n\n \n \n \n Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning.\n \n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, pages 289–297, Cambridge, MA, USA, 2014. MIT Press\n \n\n\n\n
\n\n\n \n \n \n \"Augmentative paper\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_tsp,\n author = {Ravanbakhsh, Siamak and Rabbany, Reihaneh and Greiner, Russell},\n title = {Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning},\n booktitle = {Advances in Neural Information Processing Systems},\n year = {2014},\n location = {Montreal, Canada},\n pages = {289--297},\n numpages = {9},\n acmid = {2968859},\n publisher = {MIT Press},\n address = {Cambridge, MA, USA},\n url_Paper = {https://papers.nips.cc/paper/5601-augmentative-message-passing-for-traveling-salesman-problem-and-graph-partitioning.pdf},\n} \n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Srinivasa, C.; Frey, B.; and Greiner, R.\n \n\n\n \n \n \n Min-Max Problems on Factor Graphs.\n \n\n\n \n\n\n\n In Proceedings of the 31st International Conference on Machine Learning, pages 1035–1043, 2014. \n \n\n\n\n
\n\n\n \n \n \n \"Min-Max pdf\n  \n \n \n \"Min-Max supplement\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_minmax,\n  title={Min-Max Problems on Factor Graphs},\n  author={Ravanbakhsh, Siamak and Srinivasa, Christopher and Frey, Brendan and Greiner, Russell},\n  booktitle={Proceedings of the 31st International Conference on Machine Learning},\n  pages={1035--1043},\n  year={2014},\n  keywords={inference,message passing,combinatorial optimization,application},\n  url_pdf = {http://jmlr.org/proceedings/papers/v32/ravanbakhsh14.pdf},\n  url_supplement = {http://proceedings.mlr.press/v32/ravanbakhsh14-supp.pdf}\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n
\n
\n \n\n \n \n Ravanbakhsh, S.; Greiner, R.; and Frey, B.\n \n\n\n \n \n \n Training Restricted Boltzmann Machine by Perturbation.\n \n\n\n \n\n\n\n In NIPS:workshop on perturbation, optimization and statistics, 2014. \n \n\n\n\n
\n\n\n \n \n \n \"Training pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_pmrbm,\n  title={Training Restricted Boltzmann Machine by Perturbation},\n  author={Ravanbakhsh, Siamak and Greiner, Russell and Frey, Brendan},\n  booktitle={NIPS:workshop on perturbation, optimization and statistics},\n  location={lake Tahoe, USA},\n  url_pdf = {https://arxiv.org/pdf/1405.1436v1.pdf},\n  year={2014},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2013\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Gajewski, M.; Greiner, R.; and Tuszynski, J. A\n \n\n\n \n \n \n Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy.\n \n\n\n \n\n\n\n Theoretical Biology and Medical Modelling, 10(1): 1. 2013.\n \n\n\n\n
\n\n\n \n \n \n \"Determination pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@article{ravanbakhsh_tubulin,\n  title={Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy},\n  author={Ravanbakhsh, Siamak and Gajewski, Melissa and Greiner, Russell and Tuszynski, Jack A},\n  journal={Theoretical Biology and Medical Modelling},\n  volume={10},\n  number={1},\n  pages={1},\n  year={2013},\n  publisher={BioMed Central},\n  url_pdf = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651705/pdf/1742-4682-10-29.pdf},\n}\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2012\n \n \n (2)\n \n \n
\n
\n \n \n
\n
\n  \n 10\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Yu, C.; and Greiner, R.\n \n\n\n \n \n \n A Generalized Loop Correction Method for Approximate Inference in Graphical Models.\n \n\n\n \n\n\n\n In Langford, J.; and Pineau, J., editor(s), Proceedings of the 29th International Conference on Machine Learning, of ICML '12, pages 543–550, New York, NY, USA, July 2012. Omnipress\n \n\n\n\n
\n\n\n \n \n \n \"A pdf\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@InProceedings{ravanbakhsh_glc,\n  author =    {Siamak Ravanbakhsh and Chun-Nam Yu and Russell Greiner},\n  title =     {A Generalized Loop Correction Method  for Approximate Inference in Graphical Models},\n  booktitle = {Proceedings of the 29th International Conference on Machine Learning},\n  series =    {ICML '12},\n  year =      {2012},\n  editor =    {John Langford and Joelle Pineau},\n  location =  {Edinburgh, Scotland, GB},\n  isbn =      {978-1-4503-1285-1},\n  month =     {July},\n  order = {10},    \n  publisher = {Omnipress},\n  address =   {New York, NY, USA},\n  pages=      {543--550},\n  url_pdf = {http://icml.cc/2012/papers/304.pdf},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n undefined\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; and Greiner, R.\n \n\n\n \n \n \n Benchmarking CEED: Analysis of a Batch Sampling Method for Structured Optimization.\n \n\n\n \n\n\n\n In NIPS workshop on Monte Carlo Methods for Bayesian Inference, 2012. \n \n\n\n\n
\n\n\n \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_ceed_wshp,\n  title={Benchmarking CEED: Analysis of a Batch Sampling Method for Structured Optimization},\n  author={Ravanbakhsh, Siamak and Greiner, Russell},\n  booktitle={NIPS workshop on Monte Carlo Methods for Bayesian Inference},\n  location={Whistler, Canada},\n  year={2012},\n}\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2010\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n 100\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n \n\n \n \n Ravanbakhsh, S.; Poczos, B.; and Greiner, R.\n \n\n\n \n \n \n A Cross Entropy Optimization Method for Partially Decomposable Problems.\n \n\n\n \n\n\n\n In M. Fox, D. P., editor(s), Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Special Track on AI and Bioinformatics, pages 1280–1286, Atlanta, USA, July 11 – 15 2010. AAAI Press\n \n\n\n\n
\n\n\n \n \n \n \"A paper\n  \n \n \n \"A slides\n  \n \n\n \n\n bibtex \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n\n\n
\n
@inproceedings{ravanbakhsh_nmr,\n  author    = {Ravanbakhsh, Siamak and Poczos, Barnabas and Greiner, Russ},\n  title     = {A Cross Entropy Optimization Method for Partially Decomposable Problems},\n  booktitle = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Special Track on AI and Bioinformatics},\n  pages     = {1280--1286},\n  year      = {2010},\n  editor    = {M. Fox, D. Poole},\n  publisher = {AAAI Press},\n  month     = {July 11 {--} 15 },\n  address   = {Atlanta, USA},\n  order = {100},  \n  url_Paper = {http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1848/2196},\n  url_Slides = {http://www.cs.ubc.ca/~siamakx/presentations/AAAI_ceed_presentation.pdf},\n}\n\n\n
\n
\n\n\n\n
\n\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n
\n
\n \n

Embedding in another Page

\n
\n
\n\n

\n Copy&paste any of the following snippets into an existing\n page to embed this page. For more details see the\n documention.\n

\n\n JavaScript\n (Easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=cs.ubc.ca%2F~siamakx%2Fall.bib&jsonp=1&authorFirst=1&css=css/bibbase_css.css&group0=year&group1=order&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=cs.ubc.ca%2F~siamakx%2Fall.bib&jsonp=1&authorFirst=1&css=css/bibbase_css.css&group0=year&group1=order\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n
\n \n <iframe src=\"https://bibbase.org/show?bib=cs.ubc.ca%2F~siamakx%2Fall.bib&jsonp=1&authorFirst=1&css=css/bibbase_css.css&group0=year&group1=order\"></iframe>\n \n
\n\n\n
\n
\n \n
\n
\n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);