Eric Anthony Mitchell
Eric Anthony Mitchell
PhD Candidate, Stanford University
Verified email at - Homepage
Cited by
Cited by
Direct preference optimization: Your language model is secretly a reward model
R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn
Neural Information Processing Systems (NeurIPS), 2023
Detectgpt: Zero-shot machine-generated text detection using probability curvature
E Mitchell, Y Lee, A Khazatsky, CD Manning, C Finn
International Conference on Machine Learning (ICML), 2023
Fast model editing at scale
E Mitchell, C Lin, A Bosselut, C Finn, CD Manning
International Conference on Learning Representations (ICLR), 2021
Memory-Based Model Editing at Scale
E Mitchell, C Lin, A Bosselut, CD Manning, C Finn
International Conference on Machine Learning (ICML), 2022
FlyWire: online community for whole-brain connectomics
S Dorkenwald, CE McKellar, T Macrina, N Kemnitz, K Lee, R Lu, J Wu, ...
Nature methods 19 (1), 119-128, 2022
Learning language-conditioned robot behavior from offline data and crowd-sourced annotation
S Nair, E Mitchell, K Chen, S Savarese, C Finn
Conference on Robot Learning, 1303-1315, 2022
Functional connectomics spanning multiple areas of mouse visual cortex
MICrONS Consortium, JA Bae, M Baptiste, CA Bishop, AL Bodor, ...
BioRxiv, 2021.07. 28.454025, 2021
Offline Meta-Reinforcement Learning with Advantage Weighting
E Mitchell, R Rafailov, XB Peng, S Levine, C Finn
International Conference on Machine Learning (ICML), 2021
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
K Tian, E Mitchell, A Zhou, A Sharma, R Rafailov, H Yao, C Finn, ...
Empirical Methods in Natural Language Processing (EMNLP), 2023
Neuronal wiring diagram of an adult brain
S Dorkenwald, A Matsliah, AR Sterling, P Schlegel, SC Yu, CE McKellar, ...
bioRxiv, 2023
On the opportunities and risks of foundation models. arXiv 2021
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2023
Fine-tuning language models for factuality
K Tian, E Mitchell, H Yao, CD Manning, C Finn
arXiv preprint arXiv:2311.08401, 2023
Identifying and mitigating the security risks of generative ai
C Barrett, B Boyd, E Bursztein, N Carlini, B Chen, J Choi, AR Chowdhury, ...
Foundations and TrendsŪ in Privacy and Security 6 (1), 1-52, 2023
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
E Mitchell, JJ Noh, S Li, WS Armstrong, A Agarwal, P Liu, C Finn, ...
Empirical Methods in Natural Language Processing (EMNLP), 2022
Petascale neural circuit reconstruction: automated methods
T Macrina, K Lee, R Lu, NL Turner, J Wu, S Popovych, W Silversmith, ...
bioRxiv, 2021.08. 04.455162, 2021
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
CM Schneider-Mizell, A Bodor, D Brittain, JA Buchanan, DJ Bumbarger, ...
bioRxiv, 2023
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
R Simmons-Edler, B Eisner, E Mitchell, S Seung, D Lee
RL for Real Life Workshop, International Conference on Machine Learning, 2019
Self-destructing models: Increasing the costs of harmful dual uses of foundation models
P Henderson, E Mitchell, C Manning, D Jurafsky, C Finn
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 287-296, 2023
Higher-Order Function Networks for Learning Composable 3D Object Representations
E Mitchell, S Engin, V Isler, DD Lee
International Conference on Learning Representations (ICLR), 2020
Siamese encoding and alignment by multiscale learning with self-supervision
E Mitchell, S Keselj, S Popovych, D Buniatyan, HS Seung
arXiv preprint arXiv:1904.02643, 2019
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