Conference and Journal Papers

• Edward Kim, Zach Jensen, Alexander van Grootel, Kevin Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti. Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. Journal of Chemical Information and Modeling, 2020.
  .bib journal version arXiv

  @article{kim2020inorganic,
  author = {Kim, Edward and Jensen, Zach and Grootel, Alexander van and Huang, Kevin and Staib, Matthew and Mysore, Sheshera and Chang, Haw-Shiuan and Strubell, Emma and McCallum, Andrew and Jegelka, Stefanie and Olivetti, Elsa},
  title = {Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks},
  journal = {Journal of Chemical Information and Modeling},
  volume = {0},
  number = {ja},
  pages = {null},
  year = {0},
  doi = {10.1021/acs.jcim.9b00995},
  note ={PMID: 31909619},
  URL = {https://doi.org/10.1021/acs.jcim.9b00995},
  eprint = {https://doi.org/10.1021/acs.jcim.9b00995}
  }
  

• Matthew Staib, Stefanie Jegelka. Distributionally Robust Optimization and Generalization in Kernel Methods. NeurIPS, 2019.
  .bib NeurIPS arXiv code

  @incollection{staib2019mmddro,
  title = {Distributionally Robust Optimization and Generalization in Kernel Methods},
  author = {Staib, Matthew and Jegelka, Stefanie},
  booktitle = {Advances in Neural Information Processing Systems 32},
  editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
  pages = {9131--9141},
  year = {2019},
  publisher = {Curran Associates, Inc.},
  url = {http://papers.nips.cc/paper/9113-distributionally-robust-optimization-and-generalization-in-kernel-methods.pdf}}

• Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra. Escaping Saddle Points with Adaptive Gradient Methods. ICML, 2019.
  .bib PMLR arXiv

  @inproceedings{staib2019escaping,
    title = 	 {Escaping Saddle Points with Adaptive Gradient Methods},
    author = 	 {Staib, Matthew and Reddi, Sashank and Kale, Satyen and Kumar, Sanjiv and Sra, Suvrit},
    booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
    pages = 	 {5956--5965},
    year = 	 {2019},
    editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
    volume = 	 {97},
    series = 	 {Proceedings of Machine Learning Research},
    address = 	 {Long Beach, California, USA},
    month = 	 {09--15 Jun},
    publisher = 	 {PMLR},
    pdf = 	 {http://proceedings.mlr.press/v97/staib19a/staib19a.pdf},
    url = 	 {http://proceedings.mlr.press/v97/staib19a.html}
  }

• Matthew Staib, Stefanie Jegelka. Robust Budget Allocation via Continuous Submodular Functions. Applied Mathematics & Optimization, Special Issue on Optimization for Data Sciences, 2019.
  .bib journal version

  @Article{staib2019robust,
  author="Staib, Matthew
  and Jegelka, Stefanie",
  title="Robust Budget Allocation Via Continuous Submodular Functions",
  journal="Applied Mathematics {\&} Optimization",
  year="2019",
  month="Mar",
  day="29",
  issn="1432-0606",
  doi="10.1007/s00245-019-09567-0",
  url="https://doi.org/10.1007/s00245-019-09567-0"
  }

• Matthew Staib*, Bryan Wilder*, Stefanie Jegelka. Distributionally Robust Submodular Maximization. AISTATS, 2019.
  .bib PMLR arXiv

  @inproceedings{staib2018distributionally,
   author = {Staib, Matthew and Wilder, Bryan and Jegelka, Stefanie},
   booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
   title = {Distributionally Robust Submodular Maximization},
   volume = {89},
   series = {Proceedings of Machine Learning Research},
   year = {2019}
  }

• Matthew Staib, Sebastian Claici, Justin Solomon, Stefanie Jegelka. Parallel Streaming Wasserstein Barycenters. NIPS, 2017.
  .bib NIPS arXiv code

  @incollection{staib2017parallel,
  title = {Parallel Streaming Wasserstein Barycenters},
  author = {Staib, Matthew and Claici, Sebastian and Solomon, Justin M and Jegelka, Stefanie},
  booktitle = {Advances in Neural Information Processing Systems 30},
  editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
  pages = {2644--2655},
  year = {2017},
  publisher = {Curran Associates, Inc.},
  url = {http://papers.nips.cc/paper/6858-parallel-streaming-wasserstein-barycenters.pdf}
  }

• Matthew Staib, Stefanie Jegelka. Robust Budget Allocation via Continuous Submodular Functions. ICML, 2017. (Superseded by journal version)
  .bib PMLR arXiv code

  @inproceedings{staib2017robust,
    title =  {Robust Budget Allocation via Continuous Submodular Functions},
    author =  {Matthew Staib and Stefanie Jegelka},
    booktitle =  {Proceedings of the 34th International Conference on Machine Learning},
    pages =  {3230--3240},
    year =  {2017},
    editor =  {Doina Precup and Yee Whye Teh},
    volume =  {70},
    series =  {Proceedings of Machine Learning Research},
    address =  {International Convention Centre, Sydney, Australia},
    month =  {06--11 Aug},
    publisher =  {PMLR},
    pdf =  {http://proceedings.mlr.press/v70/staib17a/staib17a.pdf},
    url =  {http://proceedings.mlr.press/v70/staib17a.html}
  }

• Tianlin Shi, Forest Agostinelli, Matthew Staib, David Wipf, Thomas Moscibroda. Improving Survey Aggregation with Sparsely Represented Signals. KDD, 2016.
  .bib .pdf

  @inproceedings{Shi:2016:ISA:2939672.2939876,
   author = {Shi, Tianlin and Agostinelli, Forest and Staib, Matthew and Wipf, David and Moscibroda, Thomas},
   title = {Improving Survey Aggregation with Sparsely Represented Signals},
   booktitle = {Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
   series = {KDD '16},
   year = {2016},
   isbn = {978-1-4503-4232-2},
   location = {San Francisco, California, USA},
   pages = {1845--1854},
   numpages = {10},
   url = {http://doi.acm.org/10.1145/2939672.2939876},
   doi = {10.1145/2939672.2939876},
   acmid = {2939876},
   publisher = {ACM},
   address = {New York, NY, USA},
   keywords = {compressive sensing, deep learning, james stein estimator, multi-task learning, presidential elections, survey aggregation},
  }

Workshops

• Matthew Staib*, Bryan Wilder*, Stefanie Jegelka. Distributionally Robust Submodular Maximization. ICML Workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018. Spotlight.
  .bib arXiv

  @inproceedings{staib2018distributionally,
   author = {Staib, Matthew and Wilder, Bryan and Jegelka, Stefanie},
   title = {Distributionally Robust Submodular Maximization},
   booktitle = {ICML 2018 Workshop on Modern Trends in Nonconvex Optimization for Machine Learning},
   year = {2018}
  }

• Matthew Staib, Stefanie Jegelka. Distributionally Robust Deep Learning as a Generalization of Adversarial Training. NIPS Machine Learning and Computer Security Workshop, 2017.
  .bib

  @inproceedings{staib2017distributionally,
   author = {Staib, Matthew and Jegelka, Stefanie},
   title = {Distributionally Robust Deep Learning as a Generalization of Adversarial Training},
   booktitle = {NIPS Machine Learning and Computer Security Workshop},
   year = {2017}
  }
  

• Matthew Staib, Stefanie Jegelka. Wasserstein k-means++ for Cloud Regime Histogram Clustering. Climate Informatics, 2017.
  .bib .pdf code

  @inproceedings{staib2017wasserstein,
   author = {Staib, Matthew and Jegelka, Stefanie},
   title = {Wasserstein k-means++ for Cloud Regime Histogram Clustering},
   booktitle = {Proceedings of the Seventh International Workshop on Climate
  Informatics: CI 2017},
   year = {2017}
  }
  

* indicates equal contribution.