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ORCIDAlexander C. Li
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Conference and Workshop Papers
- 2023
[c8]Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown
, Deepak Pathak:
Your Diffusion Model is Secretly a Zero-Shot Classifier. ICCV 2023: 2206-2217- [c7]Alexander Cong Li, Ellis Langham Brown, Alexei A. Efros, Deepak Pathak:
Internet Explorer: Targeted Representation Learning on the Open Web. ICML 2023: 19385-19406 - [c6]Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki:
Test-time Adaptation of Discriminative Models via Diffusion Generative Feedback. NeurIPS 2023 - 2022
- [c5]Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak:
Internet Curiosity: Directed Unsupervised Learning on Uncurated Internet Data. ECCV Workshops (4) 2022: 100-104 - [c4]Alexander C. Li, Alexei A. Efros, Deepak Pathak:
Understanding Collapse in Non-contrastive Siamese Representation Learning. ECCV (31) 2022: 490-505 - 2021
- [c3]Alexander C. Li, Deepak Pathak:
Functional Regularization for Reinforcement Learning via Learned Fourier Features. NeurIPS 2021: 19046-19055 - 2020
- [c2]Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel:
Sub-policy Adaptation for Hierarchical Reinforcement Learning. ICLR 2020 - [c1]Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. NeurIPS 2020
Informal and Other Publications
- 2024
- [i9]Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra:
An Introduction to Vision-Language Modeling. CoRR abs/2405.17247 (2024) - 2023
- [i8]Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak:
Internet Explorer: Targeted Representation Learning on the Open Web. CoRR abs/2302.14051 (2023) - [i7]Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak:
Your Diffusion Model is Secretly a Zero-Shot Classifier. CoRR abs/2303.16203 (2023) - [i6]Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki:
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback. CoRR abs/2311.16102 (2023) - 2022
- [i5]Alexander C. Li, Alexei A. Efros, Deepak Pathak:
Understanding Collapse in Non-Contrastive Learning. CoRR abs/2209.15007 (2022) - 2021
- [i4]Alexander C. Li, Deepak Pathak:
Functional Regularization for Reinforcement Learning via Learned Fourier Features. CoRR abs/2112.03257 (2021) - 2020
- [i3]Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. CoRR abs/2002.11708 (2020) - 2019
- [i2]Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel:
Sub-policy Adaptation for Hierarchical Reinforcement Learning. CoRR abs/1906.05862 (2019) - [i1]Murtaza Dalal, Alexander C. Li, Rohan Taori:
Autoregressive Models: What Are They Good For? CoRR abs/1910.07737 (2019)
Coauthor Index
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