1 code implementation • 13 Mar 2024 • Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, Rui Xin, Marianna Nezhurina, Igor Vasiljevic, Jenia Jitsev, Alexandros G. Dimakis, Gabriel Ilharco, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
no code implementations • 10 Mar 2021 • Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases.
no code implementations • ICLR 2021 • Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations.
BIG-bench Machine Learning Interpretable Machine Learning +1
no code implementations • ICLR 2020 • Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning.