Libraries & Services

• Tensorflow Model Garden
  Tensorflow Official Modeling library.
  [translation/Transformer] [object detection/DETR]


• Explainable AI
  Google Cloud Service. [white paper]


• USABLEPRIVACY.org
  NSF project.

Efficient Transformers

• Chefs' Random Tables: Non-Trigonometric Random Features
  Valerii Likhosherstov*, Krzysztof Choromanski*, Avinava Dubey*, Frederick Liu*, Tamas Sarlos, Adrian Weller.
  NeurIPS 2022.


• Leveraging redundancy in attention with Reuse Transformers
  Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar.
  Arxiv.

Non-Autoregressive Transformer

• EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models
  Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu, Jing Li.
  Arxiv.

Distillation

• Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
  Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
  ICLR 2023.

Accuracy Estimation

• Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
  Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang,
  Somesh Jha.
  NeurIPS 2021. [Blog Post]

Attribution

• Tracing Knowledge in Language Models Back to the Training Data
  Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu.
  Findings of EMNLP 2022.


• First is Better Than Last for Training Data Influence
  Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar.
  NeurIPS 2022.


• Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
  Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar.
  AISTATS 2022.


• Estimating Training Data Influence by Tracing Gradient Descent
  Garima Pruthi*, Frederick Liu*, Satyen Kale, Mukund Sundararajan.
  NeurIPS 2020 (Spotlight). [code] [Google AI Blog Post] [faq]


• The Penalty Imposed by Ablated Data Augmentation
  Frederick Liu, Amir Najmi, Mukund Sundararajan.
  ARXIV.


• Incorporating Priors with Feature Attribution on Text Classification
  Frederick Liu, Besim Avci.
  ACL 2019.

Federated Learning and Differential Privacy

• Differentially-Private “Draw and Discard” Machine Learning
  Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng.
  ARXIV. [Snap Engineering Blog Post]

Machine Translation

• Handling Homographs in Neural Machine Translation
  Frederick Liu*, Han Lu*, Graham Neubig.
  NAACL 2018. [code] [evaluation code]


• Learning Character-level Compositionality with Visual Features
  Frederick Liu, Han Lu, Chieh Lo, Graham Neubig.
  ACL 2017. [code] [dataset]


• Attention-based Multimodal Neural Machine Translation
  Po-Yao Huang, Frederick Liu, Sz-Rung Shiang, Jean Oh, Chris Dyer.
  WMT 2016.

Towards Effective Web Privacy Notice and Choice

• Analyzing Privacy Policies at Scale: From Crowdsourcing to Automated Annotations
  Shomir Wilson, Florian Schaub, Frederick Liu, Kanthashree Mysore Sathyendra, Daniel Smullen, Sebastian Zimmeck, Rohan Ramanath, Peter Story, Fei Liu, Norman Sadeh, and Noah A. Smith.
  TWEB 2018.


• The Creation and Analysis of a Website Privacy Policy Corpus
  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 Hovy, Joel Reidenberg, and Norman Sadeh.
  ACL 2016. [dataset]


• Crowdsourcing Annotations for Websites' Privacy Policies: Can It Really Work?
  Shomir Wilson, Florian Schaub, Rohan Ramanath, Norman Sadeh, Fei Liu, Noah Smith and Frederick Liu.
  WWW 2016.