Da Yu

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All published works

Action	Title	Year	Authors
+ PDF Chat	On Memorization of Large Language Models in Logical Reasoning	2024	Chulin Xie Yangsibo Huang Chiyuan Zhang Da Yu Xinyun Chen Bill Lin Bo Li Badih Ghazi Ravi Kumar
+ PDF Chat	Differentially Private Fine-tuning of Language Models	2024	Da Yu Saurabh Naik Artūrs Bačkurs Sivakanth Gopi Huseyin A. Inan Gautam Kamath Janardhan Kulkarni Yin Tat Lee Andre Manoel Lukas Wutschitz
+ PDF Chat	Differentially Private Synthetic Data via Foundation Model APIs 2: Text	2024	Chulin Xie Zinan Lin Artūrs Bačkurs Sivakanth Gopi Da Yu Huseyin A Inan Harsha Nori Haotian Jiang Huishuai Zhang Yin Tat Lee

Common Coauthors

Coauthor	Papers Together
Sergey Yekhanin	2
Huishuai Zhang	2
Sivakanth Gopi	2
Chulin Xie	2
Artūrs Bačkurs	2
Yin Tat Lee	2
Gautam Kamath	1
Harsha Nori	1
Lukas Wutschitz	1
Bill Lin	1
Saurabh Naik	1
Huseyin A. Inan	1
Yangsibo Huang	1
Bo Li	1
Bo Li	1
Zinan Lin	1
Ravi Kumar	1
Haotian Jiang	1
Xinyun Chen	1
Chiyuan Zhang	1
Badih Ghazi	1
Andre Manoel	1
Huseyin A Inan	1
Janardhan Kulkarni	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Fingerprinting codes and the price of approximate differential privacy	2014	Mark Bun Jonathan Ullman Salil Vadhan	1
+ PDF Chat	Deep Learning with Differential Privacy	2016	Martı́n Abadi Andy Chu Ian Goodfellow H. Brendan McMahan Ilya Mironov Kunal Talwar Li Zhang	1
+	Mixed Precision Training	2017	Paulius Micikevicius Sharan Narang Jonah Alben Gregory Diamos Erich Elsen David García Boris Ginsburg Michael Houston Oleksii Kuchaiev Ganesh Venkatesh	1
+	Learning Differentially Private Recurrent Language Models	2017	H. Brendan McMahan Daniel Ramage Kunal Talwar Li Zhang	1
+	Scalable Private Learning with PATE	2018	Nicolas Papernot Shuang Song Ilya Mironov Ananth Raghunathan Kunal Talwar Úlfar Erlingsson	1
+ PDF Chat	Private Learning and Sanitization: Pure vs. Approximate Differential Privacy	2013	Amos Beimel Kobbi Nissim Uri Stemmer	1
+	Gaussian Error Linear Units (GELUs)	2016	Dan Hendrycks Kevin Gimpel	1
+	Decoupled Weight Decay Regularization	2017	Ilya Loshchilov Frank Hutter	1
+	GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding	2018	Alex Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel Bowman	1
+	The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks	2018	Nicholas Carlini Chang Liu Úlfar Erlingsson Jernej Kos Dawn Song	1
+	Measuring the Intrinsic Dimension of Objective Landscapes	2018	Chunyuan Li Heerad Farkhoor Rosanne Liu Jason Yosinski	1
+	Scaling Neural Machine Translation	2018	Myle Ott Sergey Edunov David Grangier Michael Auli	1
+	A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference	2018	Adina Williams Nikita Nangia Samuel Bowman	1
+	The E2E Dataset: New Challenges For End-to-End Generation	2017	Jekaterina Novikova Ondřej Dušek Verena Rieser	1
+	Parameter-Efficient Transfer Learning for NLP	2019	Neil Houlsby Andrei Giurgiu Stanisław Jastrzȩbski Bruna Morrone Quentin de Laroussilhe Andréa Gesmundo Mona Attariyan Sylvain Gelly	1
+	RoBERTa: A Robustly Optimized BERT Pretraining Approach	2019	Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer Veselin Stoyanov	1
+	Privately Answering Classification Queries in the Agnostic PAC Model	2019	Anupama Nandi Raef Bassily	1
+	Limits of Private Learning with Access to Public Data	2019	Noga Alon Raef Bassily Shay Moran	1
+	Computing Tight Differential Privacy Guarantees Using FFT	2019	Antti Koskela Joonas Jälkö Antti Honkela	1
+	Plug and Play Language Models: A Simple Approach to Controlled Text Generation	2019	Sumanth Dathathri Andrea Madotto Janice Lan Jane Hung Eric Frank Piero Molino Jason Yosinski Rosanne Liu	1
+	Does learning require memorization? a short tale about a long tail	2020	Vitaly Feldman	1
+	Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification	2020	Yingxue Zhou Zhiwei Steven Wu Arindam Banerjee	1
+	Learning from Mixtures of Private and Public Populations	2020	Raef Bassily Shay Moran Anupama Nandi	1
+	Differentially Private Learning Needs Better Features (or Much More Data)	2020	Florian Tramèr Dan Boneh	1
+	TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning	2020	Han Cai Chuang Gan Ligeng Zhu Song Han	1
+ PDF Chat	Numerical Composition of Differential Privacy	2024	Sivakanth Gopi Yin Tat Lee Lukas Wutschitz	1
+	LoRA: Low-Rank Adaptation of Large Language Models	2021	J. Edward Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Weizhu Chen	1
+ PDF Chat	Large Scale Private Learning via Low-rank Reparametrization	2021	Dahua Yu Huishuai Zhang Wei Chen Jian Yin Tie‐Yan Liu	1
+	Prefix-Tuning: Optimizing Continuous Prompts for Generation	2021	Xiang Lisa Li Percy Liang	1
+	Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning	2021	Armen Aghajanyan Sonal Gupta Luke Zettlemoyer	1
+	Large-Scale Differentially Private BERT	2022	Rohan Anil Badih Ghazi Vineet Gupta Ravi Kumar Pasin Manurangsi	1
+	Large Language Models Can Be Strong Differentially Private Learners	2021	Xuechen Li Florian Tramèr Percy Liang Tatsunori Hashimoto	1
+ PDF Chat	The Power of Scale for Parameter-Efficient Prompt Tuning	2021	Brian Lester Rami Al‐Rfou Noah Constant	1
+ PDF Chat	AdapterDrop: On the Efficiency of Adapters in Transformers	2021	Andreas Rücklé Gregor Geigle Max Glockner Tilman Beck Jonas Pfeiffer Nils Reimers Iryna Gurevych	1
+	Unlocking High-Accuracy Differentially Private Image Classification through Scale	2022	Soham De Leonard Berrada Jamie Hayes Samuel Smith Borja Balle	1
+	Public Data-Assisted Mirror Descent for Private Model Training	2021	Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Ramaswamy Pillai Shuang Song Thomas Steinke Vinith M. Suriyakumar Om Thakkar Abhradeep Thakurta	1
+	Submix: Practical Private Prediction for Large-Scale Language Models	2022	Antonio Ginart Laurens van der Maaten James Zou Chuan Guo	1
+	Large Scale Transfer Learning for Differentially Private Image Classification	2022	Harsh Mehta Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky	1
+	Differentially Private Model Compression	2022	Fatemehsadat Mireshghallah Artūrs Bačkurs Huseyin A Inan Lukas Wutschitz Janardhan Kulkarni	1
+	Pre-trained Perceptual Features Improve Differentially Private Image Generation	2022	Fredrik Harder Milad Jalali Asadabadi Danica J. Sutherland Mijung Park	1
+	When Does Differentially Private Learning Not Suffer in High Dimensions?	2022	Xuechen Li Daogao Liu Tatsunori Hashimoto Huseyin A. Inan Janardhan Kulkarni Yin Tat Lee Abhradeep Thakurta	1
+	Benchmarking Differential Privacy and Federated Learning for BERT Models	2021	Priyam Basu Tiasa Singha Roy Rakshit Naidu Zümrüt Müftüoğlu Sahib Singh Fatemehsadat Mireshghallah	1
+	Compacter: Efficient Low-Rank Hypercomplex Adapter Layers	2021	Rabeeh Karimi Mahabadi James Henderson Sebastian Ruder	1
+	Extracting Training Data from Large Language Models	2020	Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss Katherine Lee Adam Roberts Tom Brown Dawn Song Úlfar Erlingsson	1
+	Training Production Language Models without Memorizing User Data	2020	Swaroop Ramaswamy Pillai Om Thakkar Rajiv Mathews Galen Andrew H. Brendan McMahan Françoise Beaufays	1
+	Language Models are Few-Shot Learners	2020	T. B. Brown Benjamin F. Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell	1
+	Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT	2020	Antti Koskela Joonas Jälkö Lukas Prediger Antti Honkela	1
+	Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data	2016	Nicolas Papernot Martı́n Abadi Úlfar Erlingsson Ian Goodfellow Kunal Talwar	1
+	Differentially Private Optimization on Large Model at Small Cost	2022	Zhiqi Bu Yuxiang Wang Sheng Zha George Karypis	1
+	Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization	2020	Pranav Subramani Nicholas Vadivelu Gautam Kamath	1