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Classical machine learning research has been focused largely on models, optimizers, and computational challenges. As technical progress and hardware advancements ease these challenges, practitioners are now finding that the limitations and faults of their models are the result of their datasets. This is particularly true of deep networks, which often rely on huge datasets that are too large and unwieldy for domain experts to curate them by hand. This workshop addresses issues in the following areas: data harvesting, dealing with the challenges and opportunities involved in creating and labeling massive datasets; data security, dealing with protecting datasets against risks of poisoning and backdoor attacks; policy, security, and privacy, dealing with the social, ethical, and regulatory issues involved in collecting large datasets, especially with regards to privacy; and data bias, related to the potential of biased datasets to result in biased models that harm members of certain groups. Dates and details can be found at securedata.lol
Fri 6:00 a.m. - 6:30 a.m.
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Dawn Song (topic TBD)
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Invited talk
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Dawn Song 🔗 |
Fri 6:30 a.m. - 7:00 a.m.
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What Do Our Models Learn?
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Invited talk
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SlidesLive Video » Large-scale vision benchmarks have driven—and often even defined—progress in machine learning. However, these benchmarks are merely proxies for the real-world tasks we actually care about. How well do our benchmarks capture such tasks? In this talk, I will discuss the alignment between our benchmark-driven ML paradigm and the real-world uses cases that motivate it. First, we will explore examples of biases in the ImageNet dataset, and how state-of-the-art models exploit them. We will then demonstrate how these biases arise as a result of design choices in the data collection and curation processes. Throughout, we illustrate how one can leverage relatively standard tools (e.g., crowdsourcing, image processing) to quantify the biases that we observe. Based on joint works with Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras and Kai Xiao. |
Aleksander Madry 🔗 |
Fri 7:00 a.m. - 7:15 a.m.
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Discussion
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Discussion panel
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🔗 |
Fri 7:15 a.m. - 7:30 a.m.
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Break
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🔗 |
Fri 7:30 a.m. - 8:00 a.m.
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Darrell West (TBD)
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Invited talk
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Darrell West 🔗 |
Fri 8:00 a.m. - 8:30 a.m.
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Adversarial, Socially Aware, and Commonsensical Data
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Invited talk
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SlidesLive Video » |
Yejin Choi 🔗 |
Fri 8:30 a.m. - 8:45 a.m.
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Discussion panel
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Discussion
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Fri 8:45 a.m. - 10:00 a.m.
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Lunch Break
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Fri 10:00 a.m. - 10:30 a.m.
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Dataset Curation via Active Learning
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Invited talk
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Robert Nowak 🔗 |
Fri 10:30 a.m. - 11:00 a.m.
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Don't Steal Data
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Invited talk
)
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Liz O'Sullivan 🔗 |
Fri 11:30 a.m. - 1:00 p.m.
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Poster Session link » | 🔗 |
Author Information
Nathalie Baracaldo (IBM Research AI)
Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Her team focuses on two main areas: federated learning, where models are trained without directly accessing training data and adversarial machine learning, where defenses are designed to withstand potential attacks to the machine learning pipeline. Nathalie is the primary investigator for the DARPA program Guaranteeing AI Robustness Against Deception (GARD), where AI security is investigated. Her team contributes to the Adversarial Robustness 360 Toolbox (ART). Nathalie is also the co-editor of the book: “Federated Learning: A Comprehensive Overview of Methods and Applications”, 2022 available in paper and as e-book in Springer, Apple books and Amazon. Nathalie's primary research interests lie at the intersection of information security, privacy and trust. As part of her work, she has also designed and implemented secure systems in the areas of cloud computing, Platform as a Service, secure data sharing and Internet of the Things. She has also contributed to projects to design scalable systems that monitor, manage performance and manage service level agreements in cloud environments. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI initiative. Nathalie is associated Editor IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016. Her dissertation focused on preventing insider threats through the use of adaptive access control systems that integrate multiple sources of contextual information. Some of the topics that she has explored in the past include secure storage systems, privacy in online social networks, secure interoperability in distributed systems, risk management and trust evaluation. During her Ph.D. studies she received the 2014 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science by the School of Information Sciences at the University of Pittsburgh. Nathalie also holds a master’s degree with Cum Laude distinction in computer sciences from the Universidad de los Andes, Colombia. Prior to that, she earned two undergraduate degrees in Computer Science and Industrial Engineering at the same university.
Yonatan Bisk (LTI @ CMU)
Avrim Blum (Toyota Technological Institute at Chicago)
Michael Curry (University of Maryland)
John Dickerson (University of Maryland)
Micah Goldblum (UMD)
Tom Goldstein (University of Maryland)
Bo Li (UIUC)
Avi Schwarzschild (University of Maryland)
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Lisa Amini · Nitin Gupta · Parikshit Ram · Kiran Kate · Bhanukiran Vinzamuri · Nathalie Baracaldo · Martin Korytak · Daniel K Weidele · Dakuo Wang -
2019 : Poster Session »
Nathalie Baracaldo · Seth Neel · Tuyen Le · Dan Philps · Suheng Tao · Sotirios Chatzis · Toyo Suzumura · Wei Wang · WENHANG BAO · Solon Barocas · Manish Raghavan · Samuel Maina · Reginald Bryant · Kush Varshney · Skyler D. Speakman · Navdeep Gill · Nicholas Schmidt · Kevin Compher · Naveen Sundar Govindarajulu · Vivek Sharma · Praneeth Vepakomma · Tristan Swedish · Jayashree Kalpathy-Cramer · Ramesh Raskar · Shihao Zheng · Mykola Pechenizkiy · Marco Schreyer · Li Ling · Chirag Nagpal · Robert Tillman · Manuela Veloso · Hanjie Chen · Xintong Wang · Michael Wellman · Matthew van Adelsberg · Ben Wood · Hans Buehler · Mahmoud Mahfouz · Antonios Alexos · Megan Shearer · Antigoni Polychroniadou · Lucia Larise Stavarache · Dmitry Efimov · Johnston P Hall · Yukun Zhang · Emily Diana · Sumitra Ganesh · Vineeth Ravi · · Swetasudha Panda · Xavier Renard · Matthew Jagielski · Yonadav Shavit · Joshua Williams · Haoran Wei · Shuang (Sophie) Zhai · Xinyi Li · Hongda Shen · Daiki Matsunaga · Jaesik Choi · Alexis Laignelet · Batuhan Guler · Jacobo Roa Vicens · Ajit Desai · Jonathan Aigrain · Robert Samoilescu -
2019 Poster: Making the Cut: A Bandit-based Approach to Tiered Interviewing »
Candice Schumann · Zhi Lang · Jeffrey Foster · John Dickerson -
2019 Poster: Defending Against Neural Fake News »
Rowan Zellers · Ari Holtzman · Hannah Rashkin · Yonatan Bisk · Ali Farhadi · Franziska Roesner · Yejin Choi -
2019 Poster: Adversarial training for free! »
Ali Shafahi · Mahyar Najibi · Mohammad Amin Ghiasi · Zheng Xu · John Dickerson · Christoph Studer · Larry Davis · Gavin Taylor · Tom Goldstein -
2018 Workshop: Wordplay: Reinforcement and Language Learning in Text-based Games »
Adam Trischler · Angeliki Lazaridou · Yonatan Bisk · Wendy Tay · Nate Kushman · Marc-Alexandre Côté · Alessandro Sordoni · Daniel Ricks · Tom Zahavy · Hal Daumé III -
2018 Poster: Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks »
Ali Shafahi · W. Ronny Huang · Mahyar Najibi · Octavian Suciu · Christoph Studer · Tudor Dumitras · Tom Goldstein -
2018 Poster: On preserving non-discrimination when combining expert advice »
Avrim Blum · Suriya Gunasekar · Thodoris Lykouris · Nati Srebro -
2018 Poster: Visualizing the Loss Landscape of Neural Nets »
Hao Li · Zheng Xu · Gavin Taylor · Christoph Studer · Tom Goldstein -
2018 Demonstration: Game for Detecting Backdoor Attacks on Deep Neural Networks using Activation Clustering »
Casey Dugan · Werner Geyer · Narendra Nath Joshi · Ingrid Lange · Dustin Ramsey Torres · Bryant Chen · Nathalie Baracaldo · Heiko Ludwig -
2017 Poster: Collaborative PAC Learning »
Avrim Blum · Nika Haghtalab · Ariel Procaccia · Mingda Qiao -
2017 Poster: Training Quantized Nets: A Deeper Understanding »
Hao Li · Soham De · Zheng Xu · Christoph Studer · Hanan Samet · Tom Goldstein -
2015 : Spotlight »
Furong Huang · William Gray Roncal · Tom Goldstein -
2015 : Uncertainty in Dynamic Matching »
John P Dickerson -
2015 Poster: Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing »
Tom Goldstein · Min Li · Xiaoming Yuan -
2014 Poster: Learning Optimal Commitment to Overcome Insecurity »
Avrim Blum · Nika Haghtalab · Ariel Procaccia -
2014 Poster: Learning Mixtures of Ranking Models »
Pranjal Awasthi · Avrim Blum · Or Sheffet · Aravindan Vijayaraghavan -
2014 Poster: Active Learning and Best-Response Dynamics »
Maria-Florina F Balcan · Christopher Berlind · Avrim Blum · Emma Cohen · Kaushik Patnaik · Le Song -
2014 Spotlight: Learning Mixtures of Ranking Models »
Pranjal Awasthi · Avrim Blum · Or Sheffet · Aravindan Vijayaraghavan -
2010 Spotlight: Trading off Mistakes and Don't-Know Predictions »
Amin Sayedi · Avrim Blum · Morteza Zadimoghaddam -
2010 Poster: Trading off Mistakes and Don't-Know Predictions »
Amin Sayedi · Morteza Zadimoghaddam · Avrim Blum -
2009 Workshop: Clustering: Science or art? Towards principled approaches »
Margareta Ackerman · Shai Ben-David · Avrim Blum · Isabelle Guyon · Ulrike von Luxburg · Robert Williamson · Reza Zadeh -
2009 Poster: Tracking Dynamic Sources of Malicious Activity at Internet Scale »
Shobha Venkataraman · Avrim Blum · Dawn Song · Subhabrata Sen · Oliver Spatscheck -
2009 Spotlight: Tracking Dynamic Sources of Malicious Activity at Internet Scale »
Shobha Venkataraman · Avrim Blum · Dawn Song · Subhabrata Sen · Oliver Spatscheck -
2008 Workshop: New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces »
Maria-Florina F Balcan · Shai Ben-David · Avrim Blum · Kristiaan Pelckmans · John Shawe-Taylor