| Are emergent abilities of Large Language Models a mirage? R Schaeffer, B Miranda, S Koyejo Advances in Neural Information Processing Systems, 2023 | 390 | 2023 |
| Decodingtrust: A comprehensive assessment of trustworthiness in gpt models B Wang, W Chen, H Pei, C Xie, M Kang, C Zhang, C Xu, Z Xiong, R Dutta, ... Advances in Neural Information Processing Systems (Datasets & Benchmarks Track), 2023 | 310 | 2023 |
| No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit R Schaeffer, M Khona, I Fiete Advances in Neural Information Processing Systems, 2022 | 61 | 2022 |
| Many-shot jailbreaking C Anil, E Durmus, N Rimsky, M Sharma, J Benton, S Kundu, J Batson, ... The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 60 | 2024 |
| Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice R Schaeffer, M Khona, L Meshulam, IR Fiete Advances in Neural Information Processing Systems, 2020 | 39 | 2020 |
| Investigating data contamination for pre-training language models M Jiang, KZ Liu, M Zhong, R Schaeffer, S Ouyang, J Han, S Koyejo arXiv preprint arXiv:2401.06059, 2024 | 38* | 2024 |
| Double descent demystified: Identifying, interpreting & ablating the sources of a deep learning puzzle R Schaeffer, M Khona, Z Robertson, A Boopathy, K Pistunova, JW Rocks, ... arXiv preprint arXiv:2303.14151, 2023 | 25 | 2023 |
| Is model collapse inevitable? breaking the curse of recursion by accumulating real and synthetic data M Gerstgrasser, R Schaeffer, A Dey, R Rafailov, H Sleight, J Hughes, ... arXiv preprint arXiv:2404.01413, 2024 | 22 | 2024 |
| A brain-wide map of neural activity during complex behaviour International Brain Laboratory, B Benson, J Benson, D Birman, ... Biorxiv, 2023.07. 04.547681, 2023 | 20 | 2023 |
| Brain-wide representations of prior information in mouse decision-making C Findling, F Hubert, International Brain Laboratory, L Acerbi, B Benson, ... BioRxiv, 2023.07. 04.547684, 2023 | 19 | 2023 |
| Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells R Schaeffer, M Khona, T Ma, C Eyzaguirre, S Koyejo, IR Fiete Advances in Neural Information Processing Systems (NeurIPS), 2023 | 17 | 2023 |
| Pretraining on the test set is all you need R Schaeffer arXiv preprint arXiv:2309.08632, 2023 | 15 | 2023 |
| Open problems in technical ai governance A Reuel, B Bucknall, S Casper, T Fist, L Soder, O Aarne, L Hammond, ... arXiv preprint arXiv:2407.14981, 2024 | 11 | 2024 |
| Deceptive alignment monitoring A Carranza, D Pai, R Schaeffer, A Tandon, S Koyejo ICML 2023 Workshop: Adversarial Machine Learning Frontiers, 2023 | 10 | 2023 |
| Emergence of sparse representations from noise T Bricken, R Schaeffer, B Olshausen, G Kreiman | 8 | 2023 |
| Quantifying Variance in Evaluation Benchmarks L Madaan, AK Singh, R Schaeffer, A Poulton, S Koyejo, P Stenetorp, ... arXiv preprint arXiv:2406.10229, 2024 | 6 | 2024 |
| Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? R Schaeffer, H Schoelkopf, B Miranda, G Mukobi, V Madan, A Ibrahim, ... arXiv preprint arXiv:2406.04391, 2024 | 6 | 2024 |
| Efficient online inference for nonparametric mixture models R Schaeffer, B Bordelon, M Khona, W Pan, IR Fiete Uncertainty in Artificial Intelligence, 2072-2081, 2021 | 4 | 2021 |
| Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Large Language Models S Duan, M Khona, A Iyer, R Schaeffer, IR Fiete arXiv preprint arXiv:2406.14549, 2024 | 3 | 2024 |
| What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes V Lecomte, K Thaman, R Schaeffer, N Bashkansky, T Chow, S Koyejo arXiv preprint arXiv:2312.03096, 2024 | 3* | 2024 |
Sanmi KoyejoAssistant Professor, Stanford UniversityEmail verificata su stanford.edu
