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ORCIDFrederick Liu
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2020 – today
- 2024
[c20]Michael Xieyang Liu
, Frederick Liu, Alexander J. Fiannaca, Terry Koo, Lucas Dixon, Michael Terry, Carrie J. Cai:
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output. CHI Extended Abstracts 2024: 10:1-10:9- [c19]Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael S. Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamás Sarlós, Ken Oslund, Karol Hausman, Kanishka Rao:
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention. ICRA 2024: 6920-6927 - [i23]Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca, Terry Koo, Lucas Dixon, Michael Terry, Carrie J. Cai:
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output. CoRR abs/2404.07362 (2024) - [i22]Terry Koo, Frederick Liu, Luheng He:
Automata-based constraints for language model decoding. CoRR abs/2407.08103 (2024) - 2023
- [c18]Ke Hu, Tara N. Sainath, Bo Li, Yu Zhang, Yong Cheng, Tao Wang, Yujing Zhang, Frederick Liu:
Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text. ASRU 2023: 1-7 - [c17]Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji:
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. ICLR 2023 - [c16]Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel. NeurIPS 2023 - [i21]Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features. CoRR abs/2302.00787 (2023) - [i20]Maximilian Mozes, Tolga Bolukbasi, Ann Yuan, Frederick Liu, Nithum Thain, Lucas Dixon:
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning. CoRR abs/2302.06598 (2023) - [i19]Sid Mittal, Vineet Gupta, Frederick Liu, Mukund Sundararajan:
Using Foundation Models to Detect Policy Violations with Minimal Supervision. CoRR abs/2306.06234 (2023) - [i18]Yaqing Wang, Jialin Wu, Tanmaya Dabral, Jiageng Zhang, Geoff Brown, Chun-Ta Lu, Frederick Liu, Yi Liang, Bo Pang, Michael Bendersky, Radu Soricut:
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling. CoRR abs/2310.12100 (2023) - [i17]Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin, Frederick Liu:
UT5: Pretraining Non autoregressive T5 with unrolled denoising. CoRR abs/2311.08552 (2023) - [i16]Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael S. Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamás Sarlós, Ken Oslund, Karol Hausman, Kanishka Rao:
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention. CoRR abs/2312.01990 (2023) - 2022
- [c15]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. AISTATS 2022: 1485-1502 - [c14]Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng:
Differentially-Private "Draw and Discard" Machine Learning: Training Distributed Model from Enormous Crowds. CSCML 2022: 468-486 - [c13]Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu:
Towards Tracing Knowledge in Language Models Back to the Training Data. EMNLP (Findings) 2022: 2429-2446 - [c12]Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
Chefs' Random Tables: Non-Trigonometric Random Features. NeurIPS 2022 - [c11]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Language Data Influence. NeurIPS 2022 - [i15]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Training Data Influence. CoRR abs/2202.11844 (2022) - [i14]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR abs/2202.11919 (2022) - [i13]Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu:
Tracing Knowledge in Language Models Back to the Training Data. CoRR abs/2205.11482 (2022) - [i12]Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller:
Chefs' Random Tables: Non-Trigonometric Random Features. CoRR abs/2205.15317 (2022) - [i11]Chi Zhang, Lijuan Liu, Xiaoxue Zang, Frederick Liu, Hao Zhang, Xinying Song, Jindong Chen:
DETR++: Taming Your Multi-Scale Detection Transformer. CoRR abs/2206.02977 (2022) - [i10]Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji:
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. CoRR abs/2210.11768 (2022) - 2021
- [c10]Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha:
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. NeurIPS 2021: 14980-14992 - [i9]Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha:
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles. CoRR abs/2106.15728 (2021) - [i8]Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar:
Leveraging redundancy in attention with Reuse Transformers. CoRR abs/2110.06821 (2021) - [i7]Frederick Liu, Siamak Shakeri, Hongkun Yu, Jing Li:
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks. CoRR abs/2110.08426 (2021) - 2020
- [c9]Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan:
Estimating Training Data Influence by Tracing Gradient Descent. NeurIPS 2020 - [i6]Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale:
Estimating Training Data Influence by Tracking Gradient Descent. CoRR abs/2002.08484 (2020) - [i5]Frederick Liu, Amir Najmi, Mukund Sundararajan:
The Penalty Imposed by Ablated Data Augmentation. CoRR abs/2006.04769 (2020)
2010 – 2019
- 2019
- [j1]Shomir Wilson, Florian Schaub, Frederick Liu, Kanthashree Mysore Sathyendra, Daniel Smullen, Sebastian Zimmeck, Rohan Ramanath, Peter Story, Fei Liu, Norman M. Sadeh, Noah A. Smith:
Analyzing Privacy Policies at Scale: From Crowdsourcing to Automated Annotations. ACM Trans. Web 13(1): 1:1-1:29 (2019) - [c8]Frederick Liu, Besim Avci:
Incorporating Priors with Feature Attribution on Text Classification. ACL (1) 2019: 6274-6283 - [i4]Frederick Liu, Besim Avci:
Incorporating Priors with Feature Attribution on Text Classification. CoRR abs/1906.08286 (2019) - 2018
- [c7]Frederick Liu, Han Lu, Graham Neubig:
Handling Homographs in Neural Machine Translation. NAACL-HLT 2018: 1336-1345 - [i3]Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng:
Differentially-Private "Draw and Discard" Machine Learning. CoRR abs/1807.04369 (2018) - 2017
- [c6]Frederick Liu, Han Lu, Chieh Lo, Graham Neubig:
Learning Character-level Compositionality with Visual Features. ACL (1) 2017: 2059-2068 - [i2]Frederick Liu, Han Lu, Chieh Lo, Graham Neubig:
Learning Character-level Compositionality with Visual Features. CoRR abs/1704.04859 (2017) - [i1]Frederick Liu, Han Lu, Graham Neubig:
Handling Homographs in Neural Machine Translation. CoRR abs/1708.06510 (2017) - 2016
- [c5]Frederick Liu, Shomir Wilson, Florian Schaub, Norman M. Sadeh:
Analyzing Vocabulary Intersections of Expert Annotations and Topic Models for Data Practices in Privacy Policies. AAAI Fall Symposia 2016 - [c4]Shomir Wilson, Florian Schaub, Aswarth Abhilash Dara, Frederick Liu, Sushain Cherivirala, Pedro Giovanni Leon, Mads Schaarup Andersen, Sebastian Zimmeck, Kanthashree Mysore Sathyendra, N. Cameron Russell, Thomas B. Norton, Eduard H. Hovy, Joel R. Reidenberg, Norman M. Sadeh:
The Creation and Analysis of a Website Privacy Policy Corpus. ACL (1) 2016 - [c3]Po-Yao Huang, Frederick Liu, Sz-Rung Shiang, Jean Oh, Chris Dyer:
Attention-based Multimodal Neural Machine Translation. WMT 2016: 639-645 - [c2]Shomir Wilson, Florian Schaub, Rohan Ramanath, Norman M. Sadeh, Fei Liu, Noah A. Smith, Frederick Liu:
Crowdsourcing Annotations for Websites' Privacy Policies: Can It Really Work? WWW 2016: 133-143 - 2014
- [c1]Frederick Liu, Jeremy Chiaming Yang, Jane Yung-jen Hsu:
Learning Pronunciation and Accent from The Crowd. HCOMP 2014: 38-39
Coauthor Index
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