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Adam Dziedzic
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2020 – today
- 2024
- [c22]Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak:
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. ECAI 2024: 1438-1445 - [c21]Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR 2024 - [i31]Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR abs/2401.12233 (2024) - [i30]Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol A. Verma, Fahad Razak, Nicolas Papernot, Bo Wang:
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. CoRR abs/2402.00205 (2024) - [i29]Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak:
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR abs/2405.12295 (2024) - [i28]Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch:
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. CoRR abs/2406.02366 (2024) - [i27]Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao:
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR abs/2406.03603 (2024) - [i26]Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic:
LLM Dataset Inference: Did you train on my dataset? CoRR abs/2406.06443 (2024) - [i25]Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch:
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR abs/2406.08039 (2024) - [i24]Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic:
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. CoRR abs/2407.12588 (2024) - [i23]Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch:
Localizing Memorization in SSL Vision Encoders. CoRR abs/2409.19069 (2024) - 2023
- [j3]Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic:
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. Proc. Priv. Enhancing Technol. 2023(1): 158-176 (2023) - [j2]Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang:
Private Multi-Winner Voting for Machine Learning. Proc. Priv. Enhancing Technol. 2023(1): 527-555 (2023) - [c20]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P 2023: 175-199 - [c19]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P 2023: 241-257 - [c18]Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS 2023 - [c17]Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS 2023 - [c16]Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic:
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS 2023 - [c15]Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang:
Robust and Actively Secure Serverless Collaborative Learning. NeurIPS 2023 - [i22]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Is Federated Learning a Practical PET Yet? CoRR abs/2301.04017 (2023) - [i21]Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR abs/2303.17046 (2023) - [i20]Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR abs/2305.15594 (2023) - [i19]Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic:
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. CoRR abs/2310.08571 (2023) - [i18]Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang:
Robust and Actively Secure Serverless Collaborative Learning. CoRR abs/2310.16678 (2023) - 2022
- [c14]Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot:
Increasing the Cost of Model Extraction with Calibrated Proof of Work. ICLR 2022 - [c13]Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot:
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. ICML 2022: 5757-5776 - [c12]Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. NeurIPS 2022 - [i17]Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot:
Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR abs/2201.09243 (2022) - [i16]Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot:
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR abs/2205.07890 (2022) - [i15]Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot:
Selective Classification Via Neural Network Training Dynamics. CoRR abs/2205.13532 (2022) - [i14]Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot:
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR abs/2207.12545 (2022) - [i13]Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. CoRR abs/2209.09024 (2022) - [i12]Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang:
Private Multi-Winner Voting for Machine Learning. CoRR abs/2211.15410 (2022) - 2021
- [c11]Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang:
CaPC Learning: Confidential and Private Collaborative Learning. ICLR 2021 - [i11]Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang:
CaPC Learning: Confidential and Private Collaborative Learning. CoRR abs/2102.05188 (2021) - [i10]Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot:
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. CoRR abs/2108.02010 (2021) - [i9]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR abs/2112.02918 (2021) - 2020
- [c10]Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song:
Pretrained Transformers Improve Out-of-Distribution Robustness. ACL 2020: 2744-2751 - [c9]Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan:
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. ICNC 2020: 596-601 - [i8]Adam Dziedzic, Sanjay Krishnan:
An Empirical Evaluation of Perturbation-based Defenses. CoRR abs/2002.03080 (2020) - [i7]Adam Dziedzic, Vanlin Sathya, Muhammad Iqbal Rochman, Monisha Ghosh, Sanjay Krishnan:
Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios. CoRR abs/2003.13652 (2020) - [i6]Dan Hendrycks, Xiaoyuan Liu
, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song:
Pretrained Transformers Improve Out-of-Distribution Robustness. CoRR abs/2004.06100 (2020)
2010 – 2019
- 2019
- [j1]Sanjay Krishnan, Aaron J. Elmore
Artificial Intelligence in Resource-Constrained and Shared Environments. ACM SIGOPS Oper. Syst. Rev. 53(1): 1-6 (2019) - [c8]Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore
DeepLens: Towards a Visual Data Management System. CIDR 2019 - [c7]Adam Dziedzic
Band-limited Training and Inference for Convolutional Neural Networks. ICML 2019: 1745-1754 - [i5]Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin:
Band-limited Training and Inference for Convolutional Neural Networks. CoRR abs/1911.09287 (2019) - [i4]Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan:
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. CoRR abs/1911.09292 (2019) - 2018
- [c6]Adam Dziedzic
Columnstore and B+ tree - Are Hybrid Physical Designs Important? SIGMOD Conference 2018: 177-190 - [i3]Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore:
DeepLens: Towards a Visual Data Management System. CoRR abs/1812.07607 (2018) - 2017
- [c5]Tim Mattson, Vijay Gadepally, Zuohao She, Adam Dziedzic, Jeff Parkhurst:
Demonstrating the BigDAWG Polystore System for Ocean Metagenomics Analysis. CIDR 2017 - [c4]Vijay Gadepally, Kyle O'Brien, Adam Dziedzic, Aaron J. Elmore
BigDAWG version 0.1. HPEC 2017: 1-7 - [i2]Kyle O'Brien, Vijay Gadepally, Jennie Duggan, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Zuohao She, Michael Stonebraker:
BigDAWG Polystore Release and Demonstration. CoRR abs/1701.05799 (2017) - [i1]Vijay Gadepally, Kyle O'Brien, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Jennie Rogers, Zuohao She, Michael Stonebraker:
Version 0.1 of the BigDAWG Polystore System. CoRR abs/1707.00721 (2017) - 2016
- [c3]Adam Dziedzic, Aaron J. Elmore
Data transformation and migration in polystores. HPEC 2016: 1-6 - [c2]John Meehan, Stan Zdonik, Shaobo Tian, Yulong Tian, Nesime Tatbul, Adam Dziedzic, Aaron J. Elmore
Integrating real-time and batch processing in a polystore. HPEC 2016: 1-7 - [c1]Adam Dziedzic, Manos Karpathiotakis, Ioannis Alagiannis, Raja Appuswamy
DBMS Data Loading: An Analysis on Modern Hardware. ADMS/IMDM@VLDB 2016: 95-117
Coauthor Index
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last updated on 2024-10-28 21:14 CET by the dblp team
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