Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
In The Twelfth International Conference on Learning Representations (ICLR) 2024
@inproceedings{wang2024memorization, title = {Memorization in Self-Supervised Learning Improves Downstream Generalization}, author = {Wang, Wenhao and Kaleem, Muhammad Ahmad and Dziedzic, Adam and Backes, Michael and Papernot, Nicolas and Boenisch, Franziska}, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, year = {2024} }
@inproceedings{fang2024collaborative, title = {Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data}, author = {Fang, Congyu and Dziedzic, Adam and Zhang, Lin and Oliva, Laura and Verma, Amol and Razak, Fahad and Papernot, Nicolas and Wang, Bo}, booktitle = {eBioMedicine}, year = {2024} }
@inproceedings{pate2023pets, author = {Boenisch, Franziska and Mühl, Christopher and Rinberg, Roy and Ihrig, Jannis and Dziedzic, Adam}, title = {Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees}, booktitle = {Privacy Enhancing Technologies Symposium (PETS)}, year = {2023} }
@inproceedings{boenisch2023idpsgd, title = {Have it your way: Individualized Privacy Assignment for DP-SGD}, author = {Boenisch, Franziska and Mühl, Christopher and Dziedzic, Adam and Rinberg, Roy and Papernot, Nicolas}, year = {2023}, booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)}, eprint = {2303.17046}, archiveprefix = {arXiv}, primaryclass = {cs.LG} }
Paper Slides Video Code Blog Post
@inproceedings{duan2023flocks, title = {Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models}, author = {Duan, Haonan and Dziedzic, Adam and Papernot, Nicolas and Boenisch, Franziska}, booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)}, year = {2023} }
@inproceedings{franzeses2023p2pml, title = {Robust and Actively Secure Serverless Collaborative Learning}, author = {Franzese, Nicholas and Dziedzic, Adam and Choquette-Choo, Christopher A. and Thomas, Mark R. and Kaleem, Muhammad Ahmad and Rabanser, Stephan and Fang, Congyu and Jha, Somesh and Papernot, Nicolas and Wang, Xiao}, booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)}, year = {2023} }
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@inproceedings{dubinski2023bucks, title = {Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders}, author = {Dubiński, Jan and Pawlak, Stanisław and Boenisch, Franziska and Trzcinski, Tomasz and Dziedzic, Adam}, booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)}, year = {2023} }
@inproceedings{duan2023privacyICL, title = {On the privacy risk of in-context learning}, author = {Duan, Haonan and Dziedzic, Adam and Yaghini, Mohammad and Papernot, Nicolas and Boenisch, Franziska}, booktitle = {The 61st Annual Meeting Of The Association For Computational Linguistics}, year = {2023} }
@inproceedings{multilabel2023pets, title = {Private Multi-Winner Voting for Machine Learning}, author = {Dziedzic, Adam and Choquette-Choo, Christopher A and Dullerud, Natalie and Suriyakumar, Vinith Menon and Shamsabadi, Ali Shahin and Kaleem, Muhammad Ahmad and Jha, Somesh and Papernot, Nicolas and Wang, Xiao}, booktitle = {Privacy Enhancing Technologies Symposium (PETS)}, year = {2023} }
@inproceedings{datasetinference2022neurips, title = {Dataset Inference for Self-Supervised Models}, author = {Dziedzic, Adam and Duan, Haonan and Kaleem, Muhammad Ahmad and Dhawan, Nikita and Guan, Jonas and Cattan, Yannis and Boenisch, Franziska and Papernot, Nicolas}, booktitle = {NeurIPS (Neural Information Processing Systems)}, year = {2022} }
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@inproceedings{pow2022iclr, title = {Increasing the Cost of Model Extraction with Calibrated Proof of Work}, author = {Dziedzic, Adam and Kaleem, Muhammad Ahmad and Lu, Yu Shen and Papernot, Nicolas}, booktitle = {ICLR (International Conference on Learning Representations) [SPOTLIGTH]}, year = {2022} }
@inproceedings{sslextractions2022icml, title = {On the Difficulty of Defending Self-Supervised Learning against Model Extraction}, author = {Dziedzic, Adam and Dhawan, Nikita and Kaleem, Muhammad Ahmad and Guan, Jonas and Papernot, Nicolas}, booktitle = {ICML (International Conference on Machine Learning)}, year = {2022} }
Paper Slides Video Code Blog Post
@inproceedings{capc2021iclr, title = {CaPC Learning: Confidential and Private Collaborative Learning}, author = {Choquette-Choo, Christopher A. and Dullerud, Natalie and Dziedzic, Adam and Zhang, Yunxiang and Jha, Somesh and Papernot, Nicolas and Wang, Xiao}, booktitle = {ICLR (International Conference on Learning Representations)}, year = {2021} }
@inproceedings{hendrycks-etal-2020-pretrained, title = {Pretrained Transformers Improve Out-of-Distribution Robustness}, author = {Hendrycks, Dan and Liu, Xiaoyuan and Wallace, Eric and Dziedzic, Adam and Krishnan, Rishabh and Song, Dawn}, booktitle = { ACL (Association for Computational Linguistics)}, month = jul, year = {2020}, address = {Online}, publisher = {ACL (Association for Computational Linguistics)}, doi = {10.18653/v1/2020.acl-main.244}, pages = {2744--2751} }
@inproceedings{dziedzic2019band, title = {Band-limited Training and Inference for Convolutional Neural Networks}, author = {Dziedzic, Adam and Paparizzos, Ioannis and Krishnan, Sanjay and Elmore, Aaron and Franklin, Michael}, booktitle = {ICML (International Conference on Machine Learning)}, year = {2019} }
@inproceedings{dziedzic2018index, title = {Columnstore and B+ Tree - Are Hybrid Physical Designs Important?}, author = {Dziedzic, Adam and Wang, Jingjing and Das, Sudipto and Ding, Bolin and Narasayya, Vivek R. and Syamala, Manoj}, booktitle = {SIGMOD (ACM Special Interest Group on Management of Data)}, year = {2018} }
@inproceedings{mattson2017demonstrating, title = {Demonstrating the BigDAWG Polystore System for Ocean Metagenomics Analysis.}, author = {Mattson, Tim and Gadepally, Vijay and She, Zuohao and Dziedzic, Adam and Parkhurst, Jeff}, booktitle = {CIDR (Conference on Innovative Data Systems Research)}, year = {2017} }
@inproceedings{dziedzic2016dbms, title = {DBMS Data Loading: An Analysis on Modern Hardware}, author = {Dziedzic, Adam and Karpathiotakis, Manos and Alagiannis, Ioannis and Appuswamy, Raja and Ailamaki, Anastasia}, booktitle = {ADMS (Accelerating analytics and Data Management Systems)}, year = {2016} }
@inproceedings{dziedzic2016transformation, title = {Data Transformation and Migration in Polystores}, author = {Dziedzic, Adam and Elmore, Aaron and Stonebraker, Michael}, booktitle = {HPEC (IEEE High Performance Extreme Computing)}, year = {2016}, organization = {IEEE} }
@inproceedings{dziedzic2015bigdawg, title = {BigDAWG: a Polystore for Diverse Interactive Applications}, author = {Dziedzic, Adam and Duggan, Jennie and Elmore, Aaron J. and Gadepally, Vijay and Stonebraker, Michael}, booktitle = {DSIA (IEEE Viz Data Systems for Interactive Analysis)}, year = {2015} }
My research is focused on secure and trustworthy Machine Learning as a Service (MLaaS). I design robust and reliable machine learning methods for training and inference of ML models while preserving data privacy and model confidentiality.
Research on collaborative, private, and robust Machine Learning.
Research on the intersection of robust machine learning and database management systems (DBMSs).
Research on graceful degradation and avoidance of performance cliffs in the F1 system.
Carried out research on hybrid physical designs for diverse workloads.
Research on data loading to diverse database management systems.
I was granted the academic scholarship for the best faculty students (based on GPA).
Created a system for validating and suggesting underlyings for complex financial products.
Designed a system to store information on configuration and management of devices at computer center.
Worked on an application providing aspects of music social interactions.
Applied statistics, Web 2.0 and mobile interactions, spatial databases, logic programming.
Designed a database and developed application for a telecom company in Java and PL/SQL.
Worked on a financial and accounting system project in Java and Oracle 10g.
The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filters and data, called band-limiting, during training. The frequency domain constraints apply to both the feed-forward and back-propagation steps. Experimentally, we observe that Convolutional Neural Networks (CNNs) are resilient to this compression scheme and results suggest that CNNs learn to leverage lower-frequency components. In particular, we found: (1) band-limited training can effectively control the resource usage (GPU and memory); (2) models trained with band-limited layers retain high prediction accuracy; and (3) requires no modification to existing training algorithms or neural network architectures to use unlike other compression schemes.
paper slides talk bibtexWe extend the Database Engine Tuning Advisor for Microsoft SQL Server to recommend a suitable combination of B+ tree and columnstore indexes for a given workload. Through extensive experiments using industry-standard benchmarks and several real-world customer workloads, we quantify how a physical design tool capable of recommending hybrid physical designs can result in orders of magnitude better execution costs compared to approaches that rely either on columnstore-only or B+ tree-only designs.
paper slides bibtexAn open source project from researchers within the Intel Science and Technology Center for Big Data (ISTC). BigDAWG is a reference implementation of a polystore database. A polystore system is any database management system (DBMS) that is built on top of multiple, heterogeneous, integrated storage engines. I worked on the scaffolding of the system and then implemented a cast operator to move data between diverse DBMSs.
paper slides bibtexAn automated testing infrastructure was built to benchmark the loading performance of several commercial and open-source databases, perform an in-depth analysis to identify bottlenecks of the data loading process and investigate novel techniques which could be used to accelerate DBMS data loading.
paper slides bibtex© 2024 Adam Dziedzic