Tatsunori Hashimoto
Postdoc, Stanford
thashim [AT] stanford.edu
Bio
I am currently a post-doc at Stanford working for John C. Duchi and Percy Liang on tradeoffs between the average and worst-case performance of machine learning models. Machine learning systems do well on their training domain, but often fail in dramatic and unexpected ways in the wild. I view these problems as coverage issues - systems can appear to do well even if they fail on rare examples (in prediction) or plagiarize from the training set (in generation). My work seeks to develop evaluations, representations, and training procedures to guarantee uniform, rather than just average performance of machine learning models.
Before this, I was a graduate student at MIT co-advised by Tommi Jaakkola and David Gifford, and a undergraduate student at Harvard in statistics and math advised by Edoardo Airoldi.
Publications
Most recent publications on Google Scholar.
Distributionally Robust Losses Against Mixture Covariate Shifts PDF
John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong
Preprint
A Retrieve-and-Edit Framework for Predicting Structured Outputs PDF
Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)
Fairness Without Demographics in Repeated Loss Minimization PDF
Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)
Derivative free optimization via repeated classification PDF
Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi
21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
Unsupervised Transformation Learning via Convex Relaxations PDF
Tatsunori B Hashimoto, Percy S Liang, John C Duchi
Advances in Neural Information Processing Systems 30 (NeurIPS 2017)
Continuous representations and models from random walk diffusion limits PDF
Tatsunori B Hashimoto
PhD Thesis, MIT CSAIL, 2016
Word embeddings as metric recovery in semantic spaces PDF
Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)
Learning Population-Level Diffusions with Generative RNNs PDF
Tatsunori B Hashimoto, David Gifford, Tommi Jaakkola
Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)
From random walks to distances on unweighted graphs PDF
Tatsunori B Hashimoto, Yi Sun, Tommi Jaakkola
Advances in Neural Information Processing Systems (NeurIPS 2015)
Metric recovery from directed unweighted graphs PDF
Tatsunori B Hashimoto, Yi Sun, Tommi Jaakkola
Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)
Unifying Human and Statistical Evaluation for Natural Language Generation PDF
Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)
A Retrieve-and-Edit Framework for Predicting Structured Outputs PDF
Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)
Generating Sentences by Editing Prototypes PDF
Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang
Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)
Continuous representations and models from random walk diffusion limits PDF
Tatsunori B Hashimoto
PhD Thesis, MIT CSAIL, 2016
Word embeddings as metric recovery in semantic spaces PDF
Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Emma Pierson*, Pang Wei Koh *, Tatsunori B Hashimoto *, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang
22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Continuous representations and models from random walk diffusion limits PDF
Tatsunori B Hashimoto
PhD Thesis, MIT CSAIL, 2016
Learning Population-Level Diffusions with Generative RNNs PDF
Tatsunori B Hashimoto, David Gifford, Tommi Jaakkola
Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)
A synergistic DNA logic predicts genome-wide chromatin accessibility PDF
Tatsunori B Hashimoto*, Richard I Sherwood*, Daniel D Kang*, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi Jaakkola, David K Gifford
Genome research (2016)
Cas9 Functionally Opens Chromatin PDF
Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood
PloS One (2016)
Cloning-free CRISPR PDF
Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood
Stem cell reports (2015)
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding PDF
Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford
Bioinformatics (2015)
Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells PDF
Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou
Nature Biotechnology (2014)
Universal count correction for high-throughput sequencing PDF
Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford
PLoS computational biology (2014)
Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape PDF
Richard I Sherwood *, Tatsunori B Hashimoto *, Charles W O'donnell *, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi Jaakkola, David K Gifford
Nature Biotechnology (2014)
Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates PDF
Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond
PloS one (2013)
Lineage-based identification of cellular states and expression programs PDF
Tatsunori B Hashimoto, Tommi Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford
Bioinformatics (2012)
Finding drug discovery rules of thumb with bump hunting PDF
Tatsunori B Hashimoto, Matthew Segall
Proceedings of the ACS (2010)
BFL: a node and edge betweenness based fast layout algorithm for large scale networks. PDF
Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano
BMC bioinformatics (2009)
Talks and slides
NeuralGen Workshop (NAACL 2019): Defining and Evaluating Diversity in Generation
Resume
-
Stanford CS / Statistics 2016 - nowPostdoc
Working for Percy Liang + John Duchi -
MIT CSAIL 2011 - 2016Ph.D.
Advised by Tommi Jaakkola + David Gifford -
RIKEN BSI 2007 - 2011Intern
Laboratory for neural circuit theory -
Optibrium LTD Summer 2010Summer Intern
ML engineer -
Harvard University 2007 - 2011Undergraduate
Cum laude and departmental honors in Statistics and Math. Advised by Edoardo Airoldi. -
U. Tokyo Institute of Medical Science 2006 - 2007Intern and Translator
Miyano research lab
Acknowledgement
This website uses the website design and template by Martin Saveski