Om Thakkar - RankMe

44 papers A* 9A 4C 1Misc 3Journal 26Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	C	`conf`	Improving Streaming ASR via Differentially Private Fusion of Data from Multiple Sources. ASRU Virat Shejwalkar, Om Thakkar, Steve Chien, Nicole Rafidi, Arun Narayanan
2025	J	`jnl`	Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints. Proc. Priv. Enhancing Technol. Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Yarong Mu, Shuang Song, Om Thakkar, Abhradeep Thakurta, Xinyi Zheng
2024	J	`jnl`	Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models. CoRR Hongbin Liu, Lun Wang, Om Thakkar, Abhradeep Thakurta, Arun Narayanan
2024	A	`conf`	Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping. INTERSPEECH Lun Wang, Om Thakkar, Zhong Meng, Nicole Rafidi, Rohit Prabhavalkar, Arun Narayanan
2024	J	`jnl`	Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping. CoRR Lun Wang, Om Thakkar, Zhong Meng, Nicole Rafidi, Rohit Prabhavalkar, Arun Narayanan
2024	Misc	`conf`	Noise Masking Attacks and Defenses for Pretrained Speech Models. ICASSP Matthew Jagielski, Om Thakkar, Lun Wang
2024	J	`jnl`	Noise Masking Attacks and Defenses for Pretrained Speech Models. CoRR Matthew Jagielski, Om Thakkar, Lun Wang
2024	A	`conf`	Quantifying Unintended Memorization in BEST-RQ ASR Encoders. INTERSPEECH Virat Shejwalkar, Om Thakkar, Arun Narayanan
2024	—	`conf`	Training Large ASR Encoders With Differential Privacy. SLT Geeticka Chauhan, Steve Chien, Om Thakkar, Abhradeep Thakurta, Arun Narayanan
2024	J	`jnl`	Training Large ASR Encoders with Differential Privacy. CoRR Geeticka Chauhan, Steve Chien, Om Thakkar, Abhradeep Thakurta, Arun Narayanan
2024	Misc	`conf`	Unintended Memorization in Large ASR Models, and How to Mitigate It. ICASSP Lun Wang, Om Thakkar, Rajiv Mathews
2023	A*	`conf`	Measuring Forgetting of Memorized Training Examples. ICLR Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
2023	J	`jnl`	Unintended Memorization in Large ASR Models, and How to Mitigate It. CoRR Lun Wang, Om Thakkar, Rajiv Mathews
2023	A*	`conf`	Why Is Public Pretraining Necessary for Private Model Training? ICML Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang
2023	J	`jnl`	Why Is Public Pretraining Necessary for Private Model Training? CoRR Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
2022	Misc	`conf`	A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. ICASSP Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays
2022	J	`jnl`	Detecting Unintended Memorization in Language-Model-Fused ASR. CoRR W. Ronny Huang, Steve Chien, Om Thakkar, Rajiv Mathews
2022	J	`jnl`	Extracting Targeted Training Data from ASR Models, and How to Mitigate It. CoRR Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, Françoise Beaufays
2022	J	`jnl`	Measuring Forgetting of Memorized Training Examples. CoRR Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
2022	A*	`conf`	Public Data-Assisted Mirror Descent for Private Model Training. ICML Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
2022	J	`jnl`	Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta
2022	A*	`conf`	The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. AAAI Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
2021	J	`jnl`	A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It. CoRR Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays
2021	A*	`conf`	Differentially Private Learning with Adaptive Clipping. NeurIPS Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy
2021	A	`conf`	Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta
2021	A*	`conf`	Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Public Data-Assisted Mirror Descent for Private Model Training. CoRR Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
2021	A*	`conf`	Revealing and Protecting Labels in Distributed Training. NeurIPS Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays
2021	J	`jnl`	Revealing and Protecting Labels in Distributed Training. CoRR Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays
2021	J	`jnl`	The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. CoRR Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
2020	J	`jnl`	Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. CoRR Shuang Song, Om Thakkar, Abhradeep Thakurta
2020	A	`conf`	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
2020	J	`jnl`	Privacy Amplification via Random Check-Ins. CoRR Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
2020	J	`jnl`	Training Production Language Models without Memorizing User Data. CoRR Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays
2020	J	`jnl`	Understanding Unintended Memorization in Federated Learning. CoRR Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Françoise Beaufays
2019	J	`jnl`	Differentially Private Learning with Adaptive Clipping. CoRR Om Thakkar, Galen Andrew, H. Brendan McMahan
2019	J	`jnl`	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
2019	A*	`conf`	Towards Practical Differentially Private Convex Optimization. IEEE Symposium on Security and Privacy Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang
2018	J	`jnl`	Model-Agnostic Private Learning via Stability. CoRR Raef Bassily, Om Thakkar, Abhradeep Thakurta
2017	J	`jnl`	Differentially Private Matrix Completion, Revisited. CoRR Prateek Jain, Om Thakkar, Abhradeep Thakurta
2016	J	`jnl`	Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar
2016	A*	`conf`	Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar
2015	J	`jnl`	Improved Upper Bounds on a'(G☐H). CoRR Punit Mehta, Rahul Muthu, Gaurav Patel, Om Thakkar, Devanshi Vyas