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
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
Stanford alpaca: An instruction-following llama model
R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ...
Fairness without demographics in repeated loss minimization
T Hashimoto, M Srivastava, H Namkoong, P Liang
International Conference on Machine Learning, 1929-1938, 2018
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
Generating sentences by editing prototypes
K Guu, TB Hashimoto, Y Oren, P Liang
Transactions of the Association for Computational Linguistics 6, 437-450, 2018
Holistic evaluation of language models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
arXiv preprint arXiv:2211.09110, 2022
Diffusion-lm improves controllable text generation
X Li, J Thickstun, I Gulrajani, PS Liang, TB Hashimoto
Advances in Neural Information Processing Systems 35, 4328-4343, 2022
Unifying human and statistical evaluation for natural language generation
TB Hashimoto, H Zhang, P Liang
arXiv preprint arXiv:1904.02792, 2019
Large language models can be strong differentially private learners
X Li, F Tramer, P Liang, T Hashimoto
arXiv preprint arXiv:2110.05679, 2021
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
The gem benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
arXiv preprint arXiv:2102.01672, 2021
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
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
Jury learning: Integrating dissenting voices into machine learning models
ML Gordon, MS Lam, JS Park, K Patel, J Hancock, T Hashimoto, ...
Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022
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
Improved natural language generation via loss truncation
D Kang, T Hashimoto
arXiv preprint arXiv:2004.14589, 2020
Distributionally robust losses against mixture covariate shifts
JC Duchi, T Hashimoto, H Namkoong
Under review 2 (1), 2019
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