Om Thakkar
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
16
Venues
10
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
16 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ASRU | Improving Streaming ASR via Differentially Private Fusion of Data from Multiple Sources. | Virat Shejwalkar, Om Thakkar, Steve Chien, Nicole Rafidi, Arun Narayanan |
| 2024 | ICASSP | Noise Masking Attacks and Defenses for Pretrained Speech Models. | Matthew Jagielski, Om Thakkar, Lun Wang |
| 2024 | ICASSP | Unintended Memorization in Large ASR Models, and How to Mitigate It. | Lun Wang, Om Thakkar, Rajiv Mathews |
| 2024 | Interspeech | Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping. | Lun Wang, Om Thakkar, Zhong Meng, Nicole Rafidi, Rohit Prabhavalkar, Arun Narayanan |
| 2024 | Interspeech | Quantifying Unintended Memorization in BEST-RQ ASR Encoders. | Virat Shejwalkar, Om Thakkar, Arun Narayanan |
| 2023 | ICLR | Measuring Forgetting of Memorized Training Examples. | Matthew Jagielski, Om Thakkar, Florian Tramr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang |
| 2023 | ICML | Why Is Public Pretraining Necessary for Private Model Training? | Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang |
| 2022 | AAAI | The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. | Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar |
| 2022 | ICASSP | A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. | Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Franoise Beaufays |
| 2022 | ICML | Public Data-Assisted Mirror Descent for Private Model Training. | Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta |
| 2021 | AISTATS | Evading the Curse of Dimensionality in Unconstrained Private GLMs. | Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta |
| 2021 | ICML | Practical and Private (Deep) Learning Without Sampling or Shuffling. | Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu |
| 2020 | AISTATS | Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. | Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth |
| 2019 | VTC | On the Joint Impact of SU Mobility and PU Activity in Cognitive Vehicular Networks with Improved Energy Detection. | Om Thakkar, Dhaval K. Patel, Yong Liang Guan, Sumei Sun, Yoong Choon Chang, Joanne Mun-Yee Lim |
| 2019 | SP | Towards Practical Differentially Private Convex Optimization. | Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang |
| 2016 | FOCS | Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. | Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar |