Tatsunori Hashimoto
Tatsunori Hashimoto
Other namesTatsu Hashimoto, Tatsunori B. Hashimoto
Assistant Professor, Stanford
Verified email at - Homepage
Cited by
Cited by
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
S Sagawa, PW Koh, TB Hashimoto, P Liang
arXiv preprint arXiv:1911.08731, 2019
Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
RI Sherwood, T Hashimoto, CW O'donnell, S Lewis, AA Barkal, ...
Nature biotechnology 32 (2), 171-178, 2014
Fairness without demographics in repeated loss minimization
T Hashimoto, M Srivastava, H Namkoong, P Liang
International Conference on Machine Learning, 1929-1938, 2018
Generating sentences by editing prototypes
K Guu, TB Hashimoto, Y Oren, P Liang
Transactions of the Association for Computational Linguistics 6, 437-450, 2018
Unifying human and statistical evaluation for natural language generation
TB Hashimoto, H Zhang, P Liang
arXiv preprint arXiv:1904.02792, 2019
A retrieve-and-edit framework for predicting structured outputs
TB Hashimoto, K Guu, Y Oren, PS Liang
Advances in Neural Information Processing Systems 31, 2018
Distributionally robust language modeling
Y Oren, S Sagawa, TB Hashimoto, P Liang
arXiv preprint arXiv:1909.02060, 2019
Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells
GWQZ Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott ...
Nature Biotechnology 32, 1223-1230, 2014
Word embeddings as metric recovery in semantic spaces
TB Hashimoto, D Alvarez-Melis, TS Jaakkola
Transactions of the Association for Computational Linguistics 4, 273-286, 2016
The gem benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
arXiv preprint arXiv:2102.01672, 2021
Large language models can be strong differentially private learners
X Li, F Tramer, P Liang, T Hashimoto
arXiv preprint arXiv:2110.05679, 2021
Emergent abilities of large language models
J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ...
arXiv preprint arXiv:2206.07682, 2022
Cloning-free CRISPR
M Arbab, S Srinivasan, T Hashimoto, N Geijsen, RI Sherwood
Stem cell reports 5 (5), 908-917, 2015
Robustness to spurious correlations via human annotations
M Srivastava, T Hashimoto, P Liang
International Conference on Machine Learning, 9109-9119, 2020
Distributionally robust losses against mixture covariate shifts
JC Duchi, T Hashimoto, H Namkoong
Under review 2, 1, 2019
The disagreement deconvolution: Bringing machine learning performance metrics in line with reality
ML Gordon, K Zhou, K Patel, T Hashimoto, MS Bernstein
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
Learning population-level diffusions with generative RNNs
T Hashimoto, D Gifford, T Jaakkola
International Conference on Machine Learning, 2417-2426, 2016
Improved natural language generation via loss truncation
D Kang, T Hashimoto
arXiv preprint arXiv:2004.14589, 2020
Cas9 functionally opens chromatin
AA Barkal, S Srinivasan, T Hashimoto, DK Gifford, RI Sherwood
PLoS One 11 (3), e0152683, 2016
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