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ORCIDOm Thakkar 0001
Person information
- affiliation: Google, Mountain View, CA, USA
- affiliation (former): Boston University, MA, USA
Other persons with the same name
- Om Thakkar 0002
![[0000-0002-2753-4972]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-2753-4972")
— Ahmedabad University, Ahmedabad, India
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2020 – today
- 2024
[c19]Lun Wang, Om Thakkar, Rajiv Mathews:
Unintended Memorization in Large ASR Models, and How to Mitigate It. ICASSP 2024: 4655-4659- [c18]Matthew Jagielski, Om Thakkar, Lun Wang:
Noise Masking Attacks and Defenses for Pretrained Speech Models. ICASSP 2024: 4810-4814 - [i23]Matthew Jagielski, Om Thakkar, Lun Wang:
Noise Masking Attacks and Defenses for Pretrained Speech Models. CoRR abs/2404.02052 (2024) - [i22]Lun Wang, Om Thakkar, Zhong Meng, Nicole Rafidi, Rohit Prabhavalkar, Arun Narayanan:
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping. CoRR abs/2406.02004 (2024) - 2023
- [c17]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. ICLR 2023 - [c16]Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? ICML 2023: 10611-10627 - [i21]Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? CoRR abs/2302.09483 (2023) - [i20]Lun Wang, Om Thakkar, Rajiv Mathews:
Unintended Memorization in Large ASR Models, and How to Mitigate It. CoRR abs/2310.11739 (2023) - 2022
- [c15]Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar:
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. AAAI 2022: 7806-7813 - [c14]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. ICASSP 2022: 4338-4342 - [c13]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. ICML 2022: 517-535 - [c12]Ehsan Amid, Om Dipakbhai Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. INTERSPEECH 2022: 2803-2807 - [c11]W. Ronny Huang, Steve Chien, Om Dipakbhai Thakkar, Rajiv Mathews:
Detecting Unintended Memorization in Language-Model-Fused ASR. INTERSPEECH 2022: 2808-2812 - [i19]Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays:
Extracting Targeted Training Data from ASR Models, and How to Mitigate It. CoRR abs/2204.08345 (2022) - [i18]W. Ronny Huang, Steve Chien, Om Thakkar, Rajiv Mathews:
Detecting Unintended Memorization in Language-Model-Fused ASR. CoRR abs/2204.09606 (2022) - [i17]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. CoRR abs/2207.00099 (2022) - [i16]Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta:
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR abs/2210.01864 (2022) - 2021
- [c10]Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta:
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS 2021: 2638-2646 - [c9]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML 2021: 5213-5225 - [c8]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. NeurIPS 2021: 1727-1738 - [c7]Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy:
Differentially Private Learning with Adaptive Clipping. NeurIPS 2021: 17455-17466 - [i15]Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu:
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR abs/2103.00039 (2021) - [i14]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It. CoRR abs/2104.07815 (2021) - [i13]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. CoRR abs/2111.00556 (2021) - [i12]Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar:
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. CoRR abs/2111.04906 (2021) - [i11]Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. CoRR abs/2112.00193 (2021) - 2020
- [c6]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS 2020: 2830-2840 - [c5]Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. NeurIPS 2020 - [i10]Shuang Song, Om Thakkar, Abhradeep Thakurta:
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. CoRR abs/2006.06783 (2020) - [i9]Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Françoise Beaufays:
Understanding Unintended Memorization in Federated Learning. CoRR abs/2006.07490 (2020) - [i8]Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. CoRR abs/2007.06605 (2020) - [i7]Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays:
Training Production Language Models without Memorizing User Data. CoRR abs/2009.10031 (2020)
2010 – 2019
- 2019
- [b1]Om Dipakbhai Thakkar:
Advances in privacy-preserving machine learning. Boston University, USA, 2019 - [c4]Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang:
Towards Practical Differentially Private Convex Optimization. IEEE Symposium on Security and Privacy 2019: 299-316 - [i6]Om Thakkar, Galen Andrew, H. Brendan McMahan:
Differentially Private Learning with Adaptive Clipping. CoRR abs/1905.03871 (2019) - [i5]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - 2018
- [c3]Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion Revisited. ICML 2018: 2220-2229 - [c2]Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar:
Model-Agnostic Private Learning. NeurIPS 2018: 7102-7112 - [i4]Raef Bassily, Om Thakkar, Abhradeep Thakurta:
Model-Agnostic Private Learning via Stability. CoRR abs/1803.05101 (2018) - 2017
- [i3]Prateek Jain, Om Thakkar, Abhradeep Thakurta:
Differentially Private Matrix Completion, Revisited. CoRR abs/1712.09765 (2017) - 2016
- [c1]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS 2016: 487-494 - [i2]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR abs/1604.03924 (2016) - 2015
- [i1]Punit Mehta, Rahul Muthu, Gaurav Patel, Om Thakkar, Devanshi Vyas:
Improved Upper Bounds on a'(G☐H). CoRR abs/1507.01818 (2015)
Coauthor Index
aka: Abhradeep Guha Thakurta
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