Frederick Liu

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 43

Top Topics

Gaussian Kernel

Top Venues

AAAI Fall Symposia

Publications

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)
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)
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 (2024)
Terry Koo, Frederick Liu, Luheng He
Automata-based constraints for language model decoding. CoRR (2024)
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 (2023)
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 (2023)
Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin, Frederick Liu
UT5: Pretraining Non autoregressive T5 with unrolled denoising. CoRR (2023)
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)
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)
Maximilian Mozes, Tolga Bolukbasi, Ann Yuan, Frederick Liu, Nithum Thain, Lucas Dixon
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning. CoRR (2023)
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)
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 (2023)
Sid Mittal, Vineet Gupta, Frederick Liu, Mukund Sundararajan
Using Foundation Models to Detect Policy Violations with Minimal Supervision. CoRR (2023)
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR (2022)
Chi Zhang, Lijuan Liu, Xiaoxue Zang, Frederick Liu, Hao Zhang, Xinying Song, Jindong Chen
DETR++: Taming Your Multi-Scale Detection Transformer. CoRR (2022)
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
First is Better Than Last for Language Data Influence. NeurIPS (2022)
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 (2022)
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
First is Better Than Last for Training Data Influence. CoRR (2022)
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)
Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller
Chefs' Random Tables: Non-Trigonometric Random Features. CoRR (2022)
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 (2022)
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)
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)
Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller
Chefs' Random Tables: Non-Trigonometric Random Features. NeurIPS (2022)
Frederick Liu, Siamak Shakeri, Hongkun Yu, Jing Li
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks. CoRR (2021)
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 (2021)
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 (2021)
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)
Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan
Estimating Training Data Influence by Tracing Gradient Descent. NeurIPS (2020)
Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale
Estimating Training Data Influence by Tracking Gradient Descent. CoRR (2020)
Frederick Liu, Amir Najmi, Mukund Sundararajan
The Penalty Imposed by Ablated Data Augmentation. CoRR (2020)
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) (2019)
Frederick Liu, Besim Avci
Incorporating Priors with Feature Attribution on Text Classification. ACL (1) (2019)
Frederick Liu, Besim Avci
Incorporating Priors with Feature Attribution on Text Classification. CoRR (2019)
Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng
Differentially-Private "Draw and Discard" Machine Learning. CoRR (2018)
Frederick Liu, Han Lu, Graham Neubig
Handling Homographs in Neural Machine Translation. NAACL-HLT (2018)
Frederick Liu, Han Lu, Chieh Lo, Graham Neubig
Learning Character-level Compositionality with Visual Features. CoRR (2017)
Frederick Liu, Han Lu, Graham Neubig
Handling Homographs in Neural Machine Translation. CoRR (2017)
Frederick Liu, Han Lu, Chieh Lo, Graham Neubig
Learning Character-level Compositionality with Visual Features. ACL (1) (2017)
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)
Po-Yao Huang, Frederick Liu, Sz-Rung Shiang, Jean Oh, Chris Dyer
Attention-based Multimodal Neural Machine Translation. WMT (2016)
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)
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)
Frederick Liu, Jeremy Chiaming Yang, Jane Yung-jen Hsu
Learning Pronunciation and Accent from The Crowd. HCOMP (2014)