Accepted Papers

Provably Correct Physics-Informed Neural Networks (Outstanding Paper Award)

Francisco Girbal Eiras (University of Oxford)*; Adel Bibi (University of Oxford); Rudy Bunel (Deepmind); Krishnamurthy Dvijotham (Google Research); Philip Torr (University of Oxford); M. Pawan Kumar (Google DeepMind)

Your Value Function is a Control Barrier Function (Outstanding Paper Award)

Daniel CH Tan (University College London)*; Fernando Acero (University College London); Robert McCarthy (University College London); Dimitrios Kanoulas (University College London); Zhibin (Alex) Li (University College London)

Formal Control Synthesis for Stochastic Neural Network Dynamic Models (Outstanding Paper Award)

Steven J.L. Adams (TU Delft)*; Morteza Lahijanian (University of Colorado Boulder); Luca Laurenti (TU Delft)

Interpreting Robustness Proofs of Deep Neural Networks (Outstanding Paper Award)

Debangshu Banerjee (University of Illinois Urbana-Champaign)*; Avaljot Singh (UIUC); Gagandeep Singh (VMware Research and UIUC)

Formal Verification for Counting Unsafe Inputs in Deep Neural Networks

Luca Marzari (University of Verona)*; Davide Corsi (University of Verona); Ferdinando Cicalese (University of Verona); Alessandro Farinelli (University of Verona, Italy)

(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy

Elan Rosenfeld (Carnegie Mellon University)*; Saurabh Garg (CMU)

One Pixel Adversarial Attacks via Sketched Programs

Tom Yuviler (Technion)*; Dana Drachsler-Cohen (Technion)

Constraint Satisfied Sampling for Formal Verification in Geometric Deep Learning and Molecular Modelling

Justin S Diamond (University of Basel)*; Markus Lill (University of Basel)

Learning Counterfactually Invariant Predictors

Francesco Quinzan (Hasso Plattner Institute)*; Cecilia Casolo (Helmholtz München); John Doe (Dark Matter); Yucen Luo (Max Planck Institute for Intelligent Systems); Niki Kilbertus (Helmholtz AI)

Probabilistic Global Robustness Verification of Arbitrary Supervised Machine Learning Models

Max-Lion Schumacher (Fraunhofer IPA)*; Marco Huber (University of Stuttgart)

Efficient Estimation of Local Robustness of Machine Learning Models

Tessa Han (Harvard University)*; Suraj Srinivas (Harvard University); Himabindu Lakkaraju (Harvard)

DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation

Haitham Khedr (University of California, Irvine)*; Yasser Shoukry (University of California, Irvine)

Robustness Verification for Perception Models against Camera Motion Perturbations

Hanjiang Hu (Carnegie Mellon University)*; Changliu Liu (Carnegie Mellon University); DING ZHAO (Carnegie Mellon University)

Stability Guarantees for Feature Attributions with Multiplicative Smoothing

Anton Xue (University of Pennsylvania)*; Rajeev Alur (University of Pennsylvania ); Eric Wong (University of Pennsylvania)

Towards Verifying Monotonicity and Robustness Properties for Domain-Specific DRL Algorithms

Mohammad Zangooei (University of Waterloo)*; Mina Tahmasbi Arashloo (University of Waterloo); Raouf Boutaba (University of Waterloo)

Toward Continuous Verification of DNNs

Shubham Ugare (UIUC)*; Debangshu Banerjee (University of Illinois Urbana-Champaign); Tarun Suresh (University of Illinois Urbana-Champaign); Sasa Misailovic (UIUC); Gagandeep Singh (VMware Research and UIUC)

A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems

Akash Harapanahalli (Georgia Institute of Technology)*; Saber Jafarpour (Georgia Institute of Technology); Sam Coogan (Georgia Tech)

Fast Feature Selection with Fairness Constraints

Francesco Quinzan (Hasso Plattner Institute)*; Rajiv Khanna (Purdue University); Moshik Hershcovitch (IBM Research); Sarel Cohen (Hasso Plattner Institute); Daniel G Waddington (IBM Research); Tobias Friedrich (Hasso Plattner Institute); Michael Mahoney (University of California, Berkeley)