ATVA 2021 Workshop on Security and Reliability of Machine Learning (SRML)

Date: Oct 18th, 2021

Location: The workshop will be held virtually

Zoom link: https://cmu.zoom.us/j/97234065013?pwd=VExidmdpaHJWODFLVEFjVGZEeUlXQT09

Registration link: https://formal-analysis.com/atva/2021/workshop.html

As machine learning models have increasingly been deployed in real-world applications, concerns are raised about the dangers of manipulation and misuse of these models, especially in security-critical domains. To analyze the reliability of the machine learning systems under malicious settings, traditional empirical attacks can easily provide an empirical upper bound of robustness performance. On the other hand, many recent certification techniques have been proposed and achieved great success in providing formal guarantees of the reliability of the machine learning systems, gradually approaching the certified lower bound of the robustness performance towards the empirical upper bounds.

This workshop will focus on recent research and future directions about the security and reliability of machine learning systems, from both empirical and certifiable analysis perspectives. We aim to bring together experts from machine learning, security, and formal verification communities highlighting recent work. Hopefully, we can chart out important and promising future directions towards secure and reliable machine learning systems and encourage cross-community collaborations.

Schedule - Time Zone: Pacific Time (PT)

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8:45 am -9:00 am PT: Opening remarks

Session 1:

9:00 am - 9:40 am PT: Keynote talk from David Wagner (UC Berkeley)

Title: Defense against adversarial examples

10:00 am -10:20 am PT: Contributed talk 1 from Patrick Henriksen (Imperial College London)

Title: VeriNet: A Symbolic Interval Propagation-Based Approach for Formal Verification of Neural Networks

10:20 am -10:40 am PT: Contributed talk 2 from Maksym Andriushchenko (École Polytechnique Fédérale de Lausanne)

Title: RobustBench: a Standardized Adversarial Robustness Benchmark

10:40 am -11:00 am PT: Contributed talk 3 from Alessandro De Palma (University of Oxford)

Title: Improving Branch and Bound for Neural Network Verification

11:00 am -11:20 am PT: Contributed talk 4 from Krishnamurthy (Dj) Dvijotham (DeepMind)

Title: Verification beyond Lp norm robustness: Probabilistic Specifications and Global Robustness

11:20 am -11:40 am PT: Contributed talk 5 from Aditi Raghunathan (Stanford University)

Title: The Many Facets of Robust Machine Learning: From Mathematical Guarantees to Real-world Shifts

11:40 am -12:30 pm PT: Panel session 1:

Matthias Hein (University of Tübingen), Cho-Jui Hsieh (UCLA), Taylor T. Johnson (Vanderbilt University), Wan-Yi Lin (Bosch Center for AI), Aditi Raghunathan (Stanford University), Florian Tramèr (Stanford University)

Session 2:

1:00 pm -1:40 pm PT: Keynote talk from Zico Kolter (Carnegie Mellon University)

Title: Robustness Beyond the Worst (or the Average) Case

2:00 pm -2:20 pm PT: Contributed talk 6 from Greg Yang (Microsoft)

Title: Randomized Smoothing of All Shapes and Sizes

2:20 pm -2:40 pm PT: Contributed talk 7 from Yizheng Chen (Columbia University)

Title: Learning Security Classifiers with Verified Global Robustness Properties

2:40 pm -3:00 pm PT: Contributed talk 8 from Hoang-Dung Tran (University of Nebraska, Lincoln)

Title: Robustness Verification for Semantic Segmentation Networks using Relaxed Reachability

3:00 pm -3:20 pm PT: Contributed talk 9 from Hadi Salman (Massachusetts Institute of Technology)

Title: Certified Patch Robustness via Smoothed Vision Transformers

3:20 pm -3:40 pm PT: Contributed talk 10 from Wenbo Guo (Pennsylvania State University)

Title: Explaining and Remediating Deep Reinforcement Learning Policies

3:40 pm -4:30 pm PT: Panel session 2:

Stanley Bak (Stony Brook University), Yizheng Chen (Columbia University), Nicholas Carlini (Google Brain), Changliu Liu (Carnegie Mellon University), David Wagner (UC Berkeley), Eric Wong (MIT)

Invited Speakers

Keynote

Panel

Talk

Organizers