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Rylan Schaeffer
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2020 – today
- 2024
- [i21]Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, Sanmi Koyejo:
Investigating Data Contamination for Pre-training Language Models. CoRR abs/2401.06059 (2024) - [i20]Rylan Schaeffer, Nika Zahedi, Mikail Khona, Dhruv Pai, Sang T. Truong
, Yilun Du, Mitchell Ostrow, Sarthak Chandra, Andres Carranza, Ila Rani Fiete, Andrey Gromov, Sanmi Koyejo:
Bridging Associative Memory and Probabilistic Modeling. CoRR abs/2402.10202 (2024) - [i19]Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David L. Donoho, Sanmi Koyejo:
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data. CoRR abs/2404.01413 (2024) - [i18]Rylan Schaeffer, Hailey Schoelkopf, Brando Miranda, Gabriel Mukobi, Varun Madan, Adam Ibrahim, Herbie Bradley, Stella Biderman, Sanmi Koyejo:
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? CoRR abs/2406.04391 (2024) - [i17]Rylan Schaeffer, Victor Lecomte, Dhruv Bhandarkar Pai, Andres Carranza, Berivan Isik, Alyssa Unell, Mikail Khona, Thomas E. Yerxa, Yann LeCun, SueYeon Chung, Andrey Gromov, Ravid Shwartz-Ziv, Sanmi Koyejo:
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. CoRR abs/2406.09366 (2024) - [i16]Lovish Madaan, Aaditya K. Singh, Rylan Schaeffer, Andrew Poulton, Sanmi Koyejo, Pontus Stenetorp, Sharan Narang, Dieuwke Hupkes:
Quantifying Variance in Evaluation Benchmarks. CoRR abs/2406.10229 (2024) - [i15]Rylan Schaeffer, Mikail Khona, Sanmi Koyejo:
In-Context Learning of Energy Functions. CoRR abs/2406.12785 (2024) - [i14]Sunny Duan, Mikail Khona, Abhiram Iyer, Rylan Schaeffer, Ila R. Fiete:
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Large Language Models. CoRR abs/2406.14549 (2024) - [i13]Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager:
Open Problems in Technical AI Governance. CoRR abs/2407.14981 (2024) - [i12]Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, Ethan Perez:
When Do Universal Image Jailbreaks Transfer Between Vision-Language Models? CoRR abs/2407.15211 (2024) - 2023
- [c9]Trenton Bricken, Rylan Schaeffer, Bruno A. Olshausen, Gabriel Kreiman:
Emergence of Sparse Representations from Noise. ICML 2023: 3148-3191 - [c8]Rylan Schaeffer, Mikail Khona, Tzuhsuan Ma, Cristóbal Eyzaguirre, Sanmi Koyejo, Ila Fiete:
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells. NeurIPS 2023 - [c7]Rylan Schaeffer, Brando Miranda, Sanmi Koyejo:
Are Emergent Abilities of Large Language Models a Mirage? NeurIPS 2023 - [c6]Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li:
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. NeurIPS 2023 - [i11]Rylan Schaeffer, Mikail Khona, Zachary Robertson, Akhilan Boopathy, Kateryna Pistunova, Jason W. Rocks, Ila Rani Fiete, Oluwasanmi Koyejo:
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle. CoRR abs/2303.14151 (2023) - [i10]Rylan Schaeffer, Brando Miranda, Sanmi Koyejo:
Are Emergent Abilities of Large Language Models a Mirage? CoRR abs/2304.15004 (2023) - [i9]Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. CoRR abs/2306.11698 (2023) - [i8]Dhruv Pai, Andres Carranza, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo:
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation. CoRR abs/2307.10563 (2023) - [i7]Andres Carranza, Dhruv Pai, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo:
Deceptive Alignment Monitoring. CoRR abs/2307.10569 (2023) - [i6]Rylan Schaeffer, Kateryna Pistunova, Samar Khanna, Sarthak Consul, Sanmi Koyejo:
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting. CoRR abs/2307.10573 (2023) - [i5]Rylan Schaeffer:
Pretraining on the Test Set Is All You Need. CoRR abs/2309.08632 (2023) - [i4]Rylan Schaeffer, Mikail Khona, Tzuhsuan Ma, Cristóbal Eyzaguirre, Sanmi Koyejo, Ila Rani Fiete:
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells. CoRR abs/2311.02316 (2023) - [i3]Victor Lecomte, Kushal Thaman, Trevor Chow, Rylan Schaeffer, Sanmi Koyejo:
Incidental Polysemanticity. CoRR abs/2312.03096 (2023) - 2022
- [c5]Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du, Scott Linderman, Ila Rani Fiete:
Streaming Inference for Infinite Non-Stationary Clustering. CoLLAs 2022: 310-326 - [c4]Rylan Schaeffer, Yilun Du, Gabrielle K. Liu
Streaming Inference for Infinite Feature Models. ICML 2022: 19366-19387 - [c3]Rylan Schaeffer, Mikail Khona, Ila Fiete:
No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit. NeurIPS 2022 - [i2]Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du, Scott Linderman, Ila Rani Fiete:
Streaming Inference for Infinite Non-Stationary Clustering. CoRR abs/2205.01212 (2022) - 2021
- [c2]Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete:
Efficient online inference for nonparametric mixture models. UAI 2021: 2072-2081 - [i1]Rylan Schaeffer:
An Algorithmic Theory of Metacognition in Minds and Machines. CoRR abs/2111.03745 (2021) - 2020
- [c1]Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, International Brain Laboratory, Ila Fiete:
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice. NeurIPS 2020
Coauthor Index
aka: Sanmi Koyejo
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last updated on 2024-10-07 21:20 CEST by the dblp team
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