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Jaan Altosaar
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2020 – today
- 2023
- [i10]Jacob S. Zelko, Sarah Gasman, Shenita R. Freeman, Dong Yun Lee, Jaan Altosaar, Azza Shoaibi, Gowtham Rao:
Developing a Robust Computable Phenotype Definition Workflow to Describe Health and Disease in Observational Health Research. CoRR abs/2304.06504 (2023) - 2022
- [c10]Tony Y. Sun, Shreyas Bhave, Jaan Altosaar, Noemie Elhadad:
Assessing Phenotype Definitions for Algorithmic Fairness. AMIA 2022 - [i9]Tony Y. Sun, Shreyas Bhave, Jaan Altosaar, Noémie Elhadad:
Assessing Phenotype Definitions for Algorithmic Fairness. CoRR abs/2203.05174 (2022) - [i8]Anton Stengel, Jaan Altosaar
, Rebecca Dittrich, Noémie Elhadad:
Assisted Living in the United States: an Open Dataset. CoRR abs/2212.14092 (2022) - 2021
- [c9]Tony Y. Sun, Oliver J. Bear Don't Walk IV, Jenny Chen, Jaan Altosaar, Harry Reyes Nieva, Noemie Elhadad:
Gender Differences in Time to Diagnosis through Fairness and Time Variant Evaluation of EHR Data. AMIA 2021 - [i7]Gabriel Reder
Supervised topic modeling for predicting molecular substructure from mass spectrometry. F1000Research 10: 403 (2021) - 2020
- [b1]Jaan Altosaar:
Probabilistic Modeling of Structure in Science: Statistical Physics to Recommender Systems. Princeton University, USA, 2020 - [c8]Rohan Bansal, Jordan Olmstead, Uri Bram, Robert Cottrell, Gabriel Reder, Jaan Altosaar:
Recommending Interesting Writing using a Controllable, Explanation-Aware Visual Interface. IntRS@RecSys 2020: 77-80 - [i6]William F. Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho:
Evaluating representations by the complexity of learning low-loss predictors. CoRR abs/2009.07368 (2020)
2010 – 2019
- 2019
- [i5]Kexin Huang, Jaan Altosaar, Rajesh Ranganath:
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. CoRR abs/1904.05342 (2019) - 2018
- [c7]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. AISTATS 2018: 1961-1969 - [c6]Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML 2018: 1251-1260 - [i4]Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR abs/1805.01500 (2018) - 2017
- [i3]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. CoRR abs/1705.08931 (2017) - 2016
- [c5]Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. NIPS 2016: 496-504 - [c4]Dawen Liang, Jaan Altosaar
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys 2016: 59-66 - [i2]Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei:
Operator Variational Inference. CoRR abs/1610.09033 (2016) - 2015
- [c3]Jingwei Zhang, Aaron Gerow, Jaan Altosaar
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections. EMNLP 2015: 1554-1564 - [c2]Andrew Mercer-Taylor, Jaan Altosaar:
Sonification of fish movement using pitch mesh pairs. NIME 2015: 28-29 - [c1]Ethan Benjamin, Jaan Altosaar:
Musicmapper: interactive 2d representations of music samples for in-browser remixing and exploration. NIME 2015: 325-326 - [i1]Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So:
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections. CoRR abs/1508.04562 (2015)
Coauthor Index
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