Adam Dziedzic

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 54

Top Topics

Diffusion Models

Empirical Evaluation

Top Venues

Publications

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 (2024)
Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
LLM Dataset Inference: Did you train on my dataset? CoRR (2024)
Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz Michalak
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR (2024)
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 (2024)
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. CoRR (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR (2024)
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR (2024)
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 (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR (2024)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS (2023)
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 (2023)
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)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR (2023)
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)
Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. CoRR (2023)
Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS (2023)
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) (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR (2023)
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) (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Is Federated Learning a Practical PET Yet? CoRR (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS (2023)
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)
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR (2022)
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 (2022)
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR (2022)
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 (2022)
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. ICML (2022)
Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
Selective Classification Via Neural Network Training Dynamics. CoRR (2022)
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)
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
Increasing the Cost of Model Extraction with Calibrated Proof of Work. ICLR (2022)
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
Dataset Inference for Self-Supervised Models. CoRR (2022)
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 (2021)
Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
CaPC Learning: Confidential and Private Collaborative Learning. CoRR (2021)
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)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR (2021)
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
Pretrained Transformers Improve Out-of-Distribution Robustness. CoRR (2020)
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
Pretrained Transformers Improve Out-of-Distribution Robustness. ACL (2020)
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 (2020)
Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. ICNC (2020)
Adam Dziedzic, Sanjay Krishnan
An Empirical Evaluation of Perturbation-based Defenses. CoRR (2020)
Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin, John Paparrizos, Zechao Shang, Adam Dziedzic, Rui Liu
Artificial Intelligence in Resource-Constrained and Shared Environments. ACM SIGOPS Oper. Syst. Rev. 53 (1) (2019)
Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore
DeepLens: Towards a Visual Data Management System. CIDR (2019)
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
Band-limited Training and Inference for Convolutional Neural Networks. ICML (2019)
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
Band-limited Training and Inference for Convolutional Neural Networks. CoRR (2019)
Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. CoRR (2019)
Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore
DeepLens: Towards a Visual Data Management System. CoRR (2018)
Adam Dziedzic, Jingjing Wang, Sudipto Das, Bolin Ding, Vivek R. Narasayya, Manoj Syamala
Columnstore and B+ tree - Are Hybrid Physical Designs Important? SIGMOD Conference (2018)
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 (2017)
Vijay Gadepally, Kyle O'Brien, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Jennie Rogers, Zuohao She, Michael Stonebraker
BigDAWG version 0.1. HPEC (2017)
Tim Mattson, Vijay Gadepally, Zuohao She, Adam Dziedzic, Jeff Parkhurst
Demonstrating the BigDAWG Polystore System for Ocean Metagenomics Analysis. CIDR (2017)
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 (2017)
Adam Dziedzic, Aaron J. Elmore, Michael Stonebraker
Data transformation and migration in polystores. HPEC (2016)
Adam Dziedzic, Manos Karpathiotakis, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki
DBMS Data Loading: An Analysis on Modern Hardware. ADMS/IMDM@VLDB (2016)
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)