Papers

• RankFromSets: Scalable Set Recommendation with Optimal Recall

  A recommendation modeling framework applied to food recommendation and recommending arXiv papers.

  J. Altosaar, R. Ranganath, and W. Tansey. Stat, 2021.

  Press: Columbia University Postdoc Society Blog

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    @article{altosaar2021rankfromsets,
      author  = {Altosaar, Jaan and Tansey, Wesley and Ranganath, Rajesh},
      journal = {Stat},
      title   = {{RankFromSets}: Scalable Set Recommendation with Optimal Recall},
      year    = {2021},
    }

• Recommending Interesting Writing using a Controllable, Explanation-Aware Visual Interface

  Recommendation system for longform journalism deployed on the AWS Lambda free tier.

  R. Bansal, J. Olmstead, U. Bram, R. Cottrell, G. Reder, J. Altosaar. Workshop on Interfaces and Human Decision Making for Recommender Systems, ACM Conference on Recommender Systems, 2020.

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  • Talk
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  • BibTeX

    @article{bansal2020recommending,
      author  = {Bansal, Rohan and Olmstead, Jordan and Bram, Uri and Cottrell, Robert and Reder, Gabriel and Altosaar, Jaan},
      journal = {Workshop on Interfaces and Human Decision Making for Recommender Systems, ACM Conference on Recommender Systems},
      title   = {Recommending Interesting Writing using a Controllable, Explanation-Aware Visual Interface},
      year    = {2020},
    }

• Evaluating representations by the complexity of learning low-loss predictors

  A framework to compare pre-trained representations for downstream tasks.

  W. F. Whitney, M. J. Song, D. Brandfonbrener, J. Altosaar, K. Cho. arXiv, 2020.

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  • arXiv
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    @article{whitney2020evaluating,
      title   = {Evaluating representations by the complexity of learning low-loss predictors},
      author  = {Whitney, William F and Song, Min Jae and Brandfonbrener, David and Altosaar, Jaan and Cho, Kyunghyun},
      journal = {arXiv:2009.07368},
      year    = {2020},
    }

• Probabilistic Modeling of Structure in Science: Statistical Physics to Recommender Systems

  It is time to go to the hooding.

  J. Altosaar. Ph.D. Thesis, Princeton University, 2020.

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    @phdthesis{altosaar2020probabilistic,
      author = {Jaan Altosaar},
      school = {Princeton University},
      title  = {Probabilistic Modeling of Structure in Science: Statistical Physics to Recommender Systems},
      year   = {2020},
    }

• ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

  Using language models to predict hospital readmission.

  K. Huang, J. Altosaar, R. Ranganath. ACM Conference on Health, Inference, and Learning, 2020.

  Press: VentureBeat

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  • arXiv
  • BibTeX

    @article{huang2019clinicalbert,
      title   = {ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission},
      author  = {Huang, Kexin and Altosaar, Jaan and Ranganath, Rajesh},
      journal = {ACM Conference on Health, Inference, and Learning},
      year    = {2020},
    }

• Noisin: Unbiased Regularization for Recurrent Neural Networks

  You dropout, we noisin'.

  A. Dieng, R. Ranganath, J. Altosaar, D. Blei. ICML, 2018.

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  • arXiv
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  • BibTeX

    @InProceedings{pmlr-v80-dieng18a,
      title     = {Noisin: Unbiased Regularization for Recurrent Neural Networks},
      author    = {Dieng, Adji Bousso and Ranganath, Rajesh and Altosaar, Jaan and Blei, David},
      booktitle = {Proceedings of the 35th International Conference on Machine Learning},
      pages     = {1252--1261},
      year      = {2018},
      editor    = {Dy, Jennifer and Krause, Andreas},
      volume    = {80},
      series    = {Proceedings of Machine Learning Research},
      address   = {Stockholmsm\"assan, Stockholm Sweden},
      month     = {10--15 Jul},
      publisher = {PMLR},
      pdf       = {http://proceedings.mlr.press/v80/dieng18a/dieng18a.pdf},
      url       = {http://proceedings.mlr.press/v80/dieng18a.html},
    }

• Proximity Variational Inference

  Preventing poor local optima in variational inference.

  J. Altosaar, R. Ranganath, D. Blei. AISTATS, 2018.

  Invited talks at Imperial College London, NIPS Approximate Inference Workshop Spotlight, Machine Intelligence Research Institute, and Bloomberg Machine Learning Group.

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  • arXiv
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    @InProceedings{pmlr-v84-altosaar18a,
      title     = {Proximity Variational Inference},
      author    = {Jaan Altosaar and Rajesh Ranganath and David Blei},
      booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
      pages     = {1961--1969},
      year      = {2018},
      editor    = {Amos Storkey and Fernando Perez-Cruz},
      volume    = {84},
      series    = {Proceedings of Machine Learning Research},
      address   = {Playa Blanca, Lanzarote, Canary Islands},
      month     = {09--11 Apr},
      publisher = {PMLR},
      pdf       = {http://proceedings.mlr.press/v84/altosaar18a/altosaar18a.pdf},
      url       = {http://proceedings.mlr.press/v84/altosaar18a.html},
    }

• Operator Variational Inference

  New divergences for variational inference.

  R. Ranganath, J. Altosaar, D. Tran, D. Blei. NIPS, 2016.

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  • arXiv
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  • BibTeX

    @inproceedings{ranganath2016operator,
      author    = {Ranganath, Rajesh and Altosaar, Jaan and Tran, Dustin and Blei, David M.},
      title     = {Operator Variational Inference},
      booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
      series    = {NIPS'16},
      year      = {2016},
      isbn      = {978-1-5108-3881-9},
      location  = {Barcelona, Spain},
      pages     = {496--504},
      numpages  = {9},
      publisher = {Curran Associates Inc.},
      address   = {USA},
      url       = {http://dl.acm.org/citation.cfm?id=3157096.3157152},
    }

• Factorization meets the item embedding

  Word embeddings for recommender systems.

  D. Liang, J. Altosaar, L. Charlin, and D. Blei. Recommender Systems, 2016.

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    @inproceedings{Liang:2016:FMI:2959100.2959182,
      author    = {Liang, Dawen and Altosaar, Jaan and Charlin, Laurent and Blei, David M.},
      title     = {Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence},
      booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
      series    = {RecSys '16},
      year      = {2016},
      isbn      = {978-1-4503-4035-9},
      location  = {Boston, Massachusetts, USA},
      pages     = {59--66},
      numpages  = {8},
      url       = {http://doi.acm.org/10.1145/2959100.2959182},
      doi       = {10.1145/2959100.2959182},
      acmid     = {2959182},
      publisher = {ACM},
      address   = {New York, NY, USA},
      keywords  = {collaborative filtering, implicit feedback, item embedding, matrix factorization},
    }

• Word embedding models applied to classical music recover the circle of fifths in embedding space

  Word embeddings applied to classical music.

  E. Bell, J. Altosaar. ICML Music Discovery Workshop, 2016.

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    @article{bell2016word,
      title     = {Word embedding models applied to classical music recover the circle of fifths in embedding space},
      author    = {Bell, Eamonn and Altosaar, Jaan},
      booktitle = {ICML Music Discovery Workshop},
      year      = {2016},
    }

• Refrustration and Competing Orders in a Spin Ice Material

  We showed a pyrochloric oxide, considered to be a classical system, has quantum behavior.

  P. Henelius, T. Lin, M. Enjalran, Z. Hao, J. G. Rau, J. Altosaar, F. Flicker, T. Yavors'kii, and M. J. P. Gingras. Phys. Rev. B, 2016.

  Featured on the PRB front page.

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  • arXiv
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    @article{PhysRevB.93.024402,
      title     = {Refrustration and competing orders in the prototypical ${\mathrm{Dy}}_{2}{\mathrm{Ti}}_{2}{\mathrm{O}}_{7}$ spin ice material},
      author    = {Henelius, P. and Lin, T. and Enjalran, M. and Hao, Z. and Rau, J. G. and Altosaar, J. and Flicker, F. and Yavors'kii, T. and Gingras, M. J. P.},
      journal   = {Phys. Rev. B},
      volume    = {93},
      issue     = {2},
      pages     = {024402},
      numpages  = {23},
      year      = {2016},
      month     = {Jan},
      publisher = {American Physical Society},
      doi       = {10.1103/PhysRevB.93.024402},
      url       = {https://link.aps.org/doi/10.1103/PhysRevB.93.024402},
    }

• Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

  Goal: model how words are used differently in arts versus sciences.

  J. Zhang, A. Gerow, J. Altosaar, J. Evans, R. J. So. EMNLP, 2015.

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  • arXiv
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    @inproceedings{zhang-etal-2015-fast,
      title     = {Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections},
      author    = {Zhang, Jingwei and Gerow, Aaron and Altosaar, Jaan and Evans, James and Jean So, Richard},
      booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
      month     = sep,
      year      = {2015},
      address   = {Lisbon, Portugal},
      publisher = {Association for Computational Linguistics},
      url       = {https://www.aclweb.org/anthology/D15-1179},
      doi       = {10.18653/v1/D15-1179},
      pages     = {1554--1564},
    }

• MusicMapper: Interactive 2D representations of music samples for in-browser remixing and exploration

  MusicMappr: let anyone make a beat in 30 seconds in their browser.

  E. Benjamin, J. Altosaar. New Interfaces For Musical Expression, 2015.

  Interview and live demo featured on The Wire magazine.

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    @inproceedings{Benjamin:2015:MIR:2993778.2993860,
      author    = {Benjamin, Ethan and Altosaar, Jaan},
      title     = {MusicMapper: Interactive 2D Representations of Music Samples for In-Browser Remixing and Exploration},
      booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
      series    = {NIME 2015},
      year      = {2015},
      isbn      = {978-0-692-49547-6},
      location  = {Baton Rouge, Louisiana, USA},
      pages     = {325--326},
      numpages  = {2},
      url       = {http://dl.acm.org/citation.cfm?id=2993778.2993860},
      acmid     = {2993860},
      publisher = {The School of Music and the Center for Computation and Technology (CCT), Louisiana State University},
      address   = {Baton Rouge, Louisiana, USA},
    }

• Sonification of Fish Movement Using Pitch Mesh Pairs

  Can we convey how fish move using generative music and computer vision?

  A. Mercer-Taylor, J. Altosaar. New Interfaces For Musical Expression, 2015.

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    @inproceedings{Mercer-Taylor:2015:SFM:2993778.2993785,
      author    = {Mercer-Taylor, Andrew and Altosaar, Jaan},
      title     = {Sonification of Fish Movement Using Pitch Mesh Pairs},
      booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
      series    = {NIME 2015},
      year      = {2015},
      isbn      = {978-0-692-49547-6},
      location  = {Baton Rouge, Louisiana, USA},
      pages     = {28--29},
      numpages  = {2},
      url       = {http://dl.acm.org/citation.cfm?id=2993778.2993785},
      acmid     = {2993785},
      publisher = {The School of Music and the Center for Computation and Technology (CCT), Louisiana State University},
      address   = {Baton Rouge, Louisiana, USA},
    }

Slide decks from some of these, and where they were given, are on talks.