Adam Dziedzic - RankMe

98 papers A* 27A 5Misc 1Journal 60Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images. AAAI Aditya Kumar, Tom Blanchard, Adam Dziedzic, Franziska Boenisch
2026	J	`jnl`	Conditioned Activation Transport for T2I Safety Steering. CoRR Maciej Chrabaszcz, Aleksander Szymczyk, Jan Dubinski, Tomasz Trzcinski, Franziska Boenisch, Adam Dziedzic
2026	J	`jnl`	Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning. CoRR Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
2026	A*	`conf`	Demystifying Foreground-Background Memorization in Diffusion Models. AAAI Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2026	A*	`conf`	On Stealing Graph Neural Network Models. AAAI Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz Pawel Michalak
2026	J	`jnl`	SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation. CoRR Jan Kociszewski, Hubert Jastrzebski, Tymoteusz Stepkowski, Filip Manijak, Krzysztof Rojek, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs). CoRR Iyiola E. Olatunji, Franziska Boenisch, Jing Xu, Adam Dziedzic
2025	J	`jnl`	Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images. CoRR Aditya Kumar, Tom Blanchard, Adam Dziedzic, Franziska Boenisch
2025	J	`jnl`	BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models. CoRR Louis Kerner, Michel Meintz, Bihe Zhao, Franziska Boenisch, Adam Dziedzic
2025	A*	`conf`	CDI: Copyrighted Data Identification in Diffusion Models. CVPR Jan Dubinski, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic
2025	A*	`conf`	Captured by Captions: On Memorization and its Mitigation in CLIP Models. ICLR Wenhao Wang, Adam Dziedzic, Grace C. Kim, Michael Backes, Franziska Boenisch
2025	J	`jnl`	Captured by Captions: On Memorization and its Mitigation in CLIP Models. CoRR Wenhao Wang, Adam Dziedzic, Grace C. Kim, Michael Backes, Franziska Boenisch
2025	J	`jnl`	Demystifying Foreground-Background Memorization in Diffusion Models. CoRR Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2025	J	`jnl`	Differentially Private Federated Learning With Time-Adaptive Privacy Spending. CoRR Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic, Stark C. Draper, Franziska Boenisch
2025	A*	`conf`	Differentially Private Federated Learning with Time-Adaptive Privacy Spending. ICLR Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic, Stark C. Draper, Franziska Boenisch
2025	A*	`conf`	Differentially Private Prototypes for Imbalanced Transfer Learning. AAAI Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
2025	A*	`conf`	Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs. ICML Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic
2025	J	`jnl`	Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs. CoRR Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic
2025	J	`jnl`	Finding Dori: Memorization in Text-to-Image Diffusion Models Is Less Local Than Assumed. CoRR Antoni Kowalczuk, Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
2025	J	`jnl`	Implementing Adaptations for Vision AutoRegressive Model. CoRR Kaif Shaikh, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	Localizing and Mitigating Memorization in Image Autoregressive Models. CoRR Aditya Kasliwal, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. Trans. Mach. Learn. Res. Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
2025	J	`jnl`	Memorization in Graph Neural Networks. CoRR Adarsh Jamadandi, Jing Xu, Adam Dziedzic, Franziska Boenisch
2025	J	`jnl`	On Stealing Graph Neural Network Models. CoRR Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak
2025	A*	`conf`	Precise Parameter Localization for Textual Generation in Diffusion Models. ICLR Lukasz Staniszewski, Bartosz Cywinski, Franziska Boenisch, Kamil Deja, Adam Dziedzic
2025	J	`jnl`	Precise Parameter Localization for Textual Generation in Diffusion Models. CoRR Lukasz Staniszewski, Bartosz Cywinski, Franziska Boenisch, Kamil Deja, Adam Dziedzic
2025	A*	`conf`	Privacy Attacks on Image AutoRegressive Models. ICML Antoni Kowalczuk, Jan Dubinski, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	Privacy Attacks on Image AutoRegressive Models. CoRR Antoni Kowalczuk, Jan Dubinski, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models. CoRR Michel Meintz, Jan Dubinski, Franziska Boenisch, Adam Dziedzic
2025	A*	`conf`	Secure Noise Sampling for Differentially Private Collaborative Learning. CCS Olive Franzese, Congyu Fang, Radhika Garg, Xiao Wang, Somesh Jha, Nicolas Papernot, Adam Dziedzic
2025	J	`jnl`	Secure Noise Sampling for Differentially Private Collaborative Learning. IACR Cryptol. ePrint Arch. Olive Franzese, Congyu Fang, Radhika Garg, Somesh Jha, Nicolas Papernot, Xiao Wang, Adam Dziedzic
2025	J	`jnl`	Selective Prediction via Training Dynamics. Trans. Mach. Learn. Res. Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot
2025	J	`jnl`	Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. CoRR Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper
2025	A*	`conf`	Unlocking Post-hoc Dataset Inference with Synthetic Data. ICML Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic
2025	J	`jnl`	Unlocking Post-hoc Dataset Inference with Synthetic Data. CoRR Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic
2024	J	`jnl`	Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
2024	J	`jnl`	Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. CoRR Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic
2024	J	`jnl`	Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
2024	J	`jnl`	CDI: Copyrighted Data Identification in Diffusion Models. CoRR Jan Dubinski, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic
2024	J	`jnl`	Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. CoRR Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot, Bo Wang
2024	A	`conf`	Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. ECAI Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak
2024	J	`jnl`	Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak
2024	A*	`conf`	Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. NeurIPS Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
2024	J	`jnl`	Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. CoRR Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
2024	A*	`conf`	LLM Dataset Inference: Did you train on my dataset? NeurIPS Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
2024	J	`jnl`	LLM Dataset Inference: Did you train on my dataset? CoRR Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
2024	A*	`conf`	Localizing Memorization in SSL Vision Encoders. NeurIPS Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch
2024	J	`jnl`	Localizing Memorization in SSL Vision Encoders. CoRR Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch
2024	A*	`conf`	Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
2024	J	`jnl`	Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
2024	J	`jnl`	On the Privacy Risk of In-context Learning. CoRR Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, Franziska Boenisch
2024	A*	`conf`	Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives. NeurIPS Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji, Michael Backes, Adam Dziedzic
2024	J	`jnl`	Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives. CoRR Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji, Michael Backes, Adam Dziedzic
2023	A*	`conf`	Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
2023	J	`jnl`	Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. CoRR Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
2023	A*	`conf`	Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
2023	J	`jnl`	Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
2023	A*	`conf`	Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
2023	J	`jnl`	Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
2023	J	`jnl`	Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. Proc. Priv. Enhancing Technol. Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic
2023	J	`jnl`	Is Federated Learning a Practical PET Yet? CoRR Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2023	J	`jnl`	Private Multi-Winner Voting for Machine Learning. Proc. Priv. Enhancing Technol. Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	A	`conf`	Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2023	A*	`conf`	Robust and Actively Secure Serverless Collaborative Learning. NeurIPS Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	J	`jnl`	Robust and Actively Secure Serverless Collaborative Learning. CoRR Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang
2023	A	`conf`	When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2022	A*	`conf`	Dataset Inference for Self-Supervised Models. NeurIPS Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022	J	`jnl`	Dataset Inference for Self-Supervised Models. CoRR Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022	A*	`conf`	Increasing the Cost of Model Extraction with Calibrated Proof of Work. ICLR Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
2022	J	`jnl`	Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
2022	A*	`conf`	On the Difficulty of Defending Self-Supervised Learning against Model Extraction. ICML Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2022	J	`jnl`	On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2022	J	`jnl`	Private Multi-Winner Voting for Machine Learning. CoRR Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
2022	J	`jnl`	Selective Classification Via Neural Network Training Dynamics. CoRR Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot
2022	J	`jnl`	p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
2021	A*	`conf`	CaPC Learning: Confidential and Private Collaborative Learning. ICLR Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
2021	J	`jnl`	CaPC Learning: Confidential and Private Collaborative Learning. CoRR Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
2021	J	`jnl`	On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. CoRR Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot
2021	J	`jnl`	When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
2020	J	`jnl`	An Empirical Evaluation of Perturbation-based Defenses. CoRR Adam Dziedzic, Sanjay Krishnan
2020	Misc	`conf`	Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. ICNC Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan
2020	J	`jnl`	Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios. CoRR Adam Dziedzic, Vanlin Sathya, Muhammad Iqbal Rochman, Monisha Ghosh, Sanjay Krishnan
2020	A*	`conf`	Pretrained Transformers Improve Out-of-Distribution Robustness. ACL Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
2020	J	`jnl`	Pretrained Transformers Improve Out-of-Distribution Robustness. CoRR Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
2019	J	`jnl`	Artificial Intelligence in Resource-Constrained and Shared Environments. ACM SIGOPS Oper. Syst. Rev. Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin, John Paparrizos, Zechao Shang, Adam Dziedzic, Rui Liu
2019	A*	`conf`	Band-limited Training and Inference for Convolutional Neural Networks. ICML Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
2019	J	`jnl`	Band-limited Training and Inference for Convolutional Neural Networks. CoRR Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
2019	A	`conf`	DeepLens: Towards a Visual Data Management System. CIDR Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore
2019	J	`jnl`	Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. CoRR Vanlin Sathya, Adam Dziedzic, Monisha Ghosh, Sanjay Krishnan
2018	—	`conf`	Columnstore and B+ tree - Are Hybrid Physical Designs Important? SIGMOD Conference Adam Dziedzic, Jingjing Wang, Sudipto Das, Bolin Ding, Vivek R. Narasayya, Manoj Syamala
2018	J	`jnl`	DeepLens: Towards a Visual Data Management System. CoRR Sanjay Krishnan, Adam Dziedzic, Aaron J. Elmore
2017	J	`jnl`	BigDAWG Polystore Release and Demonstration. CoRR Kyle O'Brien, Vijay Gadepally, Jennie Duggan, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Zuohao She, Michael Stonebraker
2017	—	`conf`	BigDAWG version 0.1. HPEC Vijay Gadepally, Kyle O'Brien, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Jennie Rogers, Zuohao She, Michael Stonebraker
2017	A	`conf`	Demonstrating the BigDAWG Polystore System for Ocean Metagenomics Analysis. CIDR Tim Mattson, Vijay Gadepally, Zuohao She, Adam Dziedzic, Jeff Parkhurst
2017	J	`jnl`	Version 0.1 of the BigDAWG Polystore System. CoRR Vijay Gadepally, Kyle O'Brien, Adam Dziedzic, Aaron J. Elmore, Jeremy Kepner, Samuel Madden, Tim Mattson, Jennie Rogers, Zuohao She, Michael Stonebraker
2016	—	`conf`	DBMS Data Loading: An Analysis on Modern Hardware. ADMS/IMDM@VLDB Adam Dziedzic, Manos Karpathiotakis, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki
2016	—	`conf`	Data transformation and migration in polystores. HPEC Adam Dziedzic, Aaron J. Elmore, Michael Stonebraker
2016	—	`conf`	Integrating real-time and batch processing in a polystore. HPEC John Meehan, Stan Zdonik, Shaobo Tian, Yulong Tian, Nesime Tatbul, Adam Dziedzic, Aaron J. Elmore