{"total":125,"rows":[{"id":"2103.06624","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","created":"2021-03-11","cited_by_count":228,"area":null},{"id":"2101.08452","title":"Robust Reinforcement Learning on State Observations with Learned Optimal Adversary","created":"2021-01-21","cited_by_count":135,"area":null},{"id":"2208.05740","title":"General Cutting Planes for Bound-Propagation-Based Neural Network Verification","created":"2022-08-11","cited_by_count":103,"area":null},{"id":"2205.00263","title":"Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound","created":"2022-04-30","cited_by_count":92,"area":null},{"id":"2103.03638","title":"PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations","created":"2021-03-05","cited_by_count":77,"area":null},{"id":"2109.00498","title":"The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results","created":"2021-08-31","cited_by_count":71,"area":null},{"id":"2210.01787","title":"Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective","created":"2022-10-04","cited_by_count":51,"area":null},{"id":"2110.07719","title":"Certified Patch Robustness via Smoothed Vision Transformers","created":"2021-10-11","cited_by_count":45,"area":null},{"id":"2104.06718","title":"Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition","created":"2021-04-14","cited_by_count":44,"area":null},{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39,"area":null},{"id":"2202.07728","title":"Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis","created":"2022-02-15","cited_by_count":38,"area":null},{"id":"2211.00322","title":"DensePure: Understanding Diffusion Models towards Adversarial Robustness","created":"2022-11-01","cited_by_count":33,"area":"diffusion"},{"id":"2102.07861","title":"Low Curvature Activations Reduce Overfitting in Adversarial Training","created":"2021-02-15","cited_by_count":29,"area":null},{"id":"2302.01404","title":"Provably Bounding Neural Network Preimages","created":"2023-02-02","cited_by_count":29,"area":null},{"id":"2110.06850","title":"Boosting the Certified Robustness of L-infinity Distance Nets","created":"2021-10-13","cited_by_count":24,"area":null},{"id":"2201.01978","title":"An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks","created":"2022-01-06","cited_by_count":24,"area":null},{"id":"2203.11201","title":"Efficient Neural Network Analysis with Sum-of-Infeasibilities","created":"2022-03-19","cited_by_count":24,"area":null},{"id":"2107.04570","title":"ANCER: Anisotropic Certification via Sample-wise Volume Maximization","created":"2021-07-09","cited_by_count":23,"area":null},{"id":"2307.07171","title":"Certified Robustness for Large Language Models with Self-Denoising","created":"2023-07-14","cited_by_count":23,"area":null},{"id":"2206.14772","title":"IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound","created":"2022-06-29","cited_by_count":21,"area":null},{"id":"2308.14333","title":"DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing","created":"2023-08-28","cited_by_count":20,"area":"diffusion"},{"id":"2203.03153","title":"Scalable Verification of GNN-based Job Schedulers","created":"2022-03-07","cited_by_count":19,"area":null},{"id":"2303.01713","title":"Convex Bounds on the Softmax Function with Applications to Robustness Verification","created":"2023-03-03","cited_by_count":19,"area":null},{"id":"2109.06795","title":"ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation","created":"2021-09-14","cited_by_count":17,"area":null},{"id":"2103.16652","title":"Robustness Certification for Point Cloud Models","created":"2021-03-30","cited_by_count":16,"area":null},{"id":"2206.03669","title":"Toward Certified Robustness Against Real-World Distribution Shifts","created":"2022-06-08","cited_by_count":16,"area":null},{"id":"2104.00447","title":"Towards Evaluating and Training Verifiably Robust Neural Networks","created":"2021-04-01","cited_by_count":15,"area":null},{"id":"2210.06077","title":"Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2105.11363","title":"Learning Security Classifiers with Verified Global Robustness Properties","created":"2021-05-24","cited_by_count":14,"area":null},{"id":"2110.01388","title":"Neural Network Verification in Control","created":"2021-09-30","cited_by_count":14,"area":null},{"id":"2206.07311","title":"Can pruning improve certified robustness of neural networks?","created":"2022-06-15","cited_by_count":14,"area":null},{"id":"2304.01874","title":"Incremental Verification of Neural Networks","created":"2023-04-04","cited_by_count":14,"area":null},{"id":"2305.03686","title":"Provable Preimage Under-Approximation for Neural Networks (Full Version)","created":"2023-05-05","cited_by_count":14,"area":null},{"id":"2102.06700","title":"On the Paradox of Certified Training","created":"2021-02-12","cited_by_count":12,"area":null},{"id":"2107.08467","title":"GoTube: Scalable Stochastic Verification of Continuous-Depth Models","created":"2021-07-18","cited_by_count":12,"area":null},{"id":"2203.03034","title":"A Unified View of SDP-based Neural Network Verification through Completely Positive Programming","created":"2022-03-06","cited_by_count":12,"area":null},{"id":"2506.06665","title":"SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming","created":"2025-06-07","cited_by_count":12,"area":null},{"id":"2101.05844","title":"Scaling the Convex Barrier with Sparse Dual Algorithms","created":"2021-01-14","cited_by_count":10,"area":null},{"id":"2109.00542","title":"Shared Certificates for Neural Network Verification","created":"2021-09-01","cited_by_count":10,"area":null},{"id":"2206.07839","title":"Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness","created":"2022-06-15","cited_by_count":10,"area":null},{"id":"2209.05055","title":"CARE: Certifiably Robust Learning with Reasoning via Variational Inference","created":"2022-09-12","cited_by_count":10,"area":null},{"id":"2305.10157","title":"Efficient Error Certification for Physics-Informed Neural Networks","created":"2023-05-17","cited_by_count":10,"area":null},{"id":"2406.18451","title":"Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers","created":"2024-06-26","cited_by_count":10,"area":null},{"id":"2211.08453","title":"Improved techniques for deterministic l2 robustness","created":"2022-11-15","cited_by_count":9,"area":null},{"id":"2512.11087","title":"Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification","created":"2025-12-11","cited_by_count":9,"area":null},{"id":"2106.09117","title":"DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting","created":"2021-06-16","cited_by_count":8,"area":null},{"id":"2208.09872","title":"Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks","created":"2022-08-21","cited_by_count":8,"area":null},{"id":"2311.04015","title":"Expressivity of ReLU-Networks under Convex Relaxations","created":"2023-11-07","cited_by_count":8,"area":null},{"id":"2408.09262","title":"PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks","created":"2024-08-17","cited_by_count":8,"area":null},{"id":"2408.13140","title":"Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation","created":"2024-08-23","cited_by_count":7,"area":null},{"id":"2106.07221","title":"Certification of embedded systems based on Machine Learning: A survey","created":"2021-06-14","cited_by_count":6,"area":null},{"id":"2303.13401","title":"Optimization and Optimizers for Adversarial Robustness","created":"2023-03-23","cited_by_count":6,"area":null},{"id":"2506.01356","title":"Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion","created":"2025-06-02","cited_by_count":6,"area":null},{"id":"2102.01208","title":"Fast Training of Provably Robust Neural Networks by SingleProp","created":"2021-02-01","cited_by_count":5,"area":null},{"id":"2102.08868","title":"Bridging the Gap Between Adversarial Robustness and Optimization Bias","created":"2021-02-17","cited_by_count":5,"area":null},{"id":"2207.07539","title":"3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models","created":"2022-07-15","cited_by_count":5,"area":null},{"id":"2210.08069","title":"Zonotope Domains for Lagrangian Neural Network Verification","created":"2022-10-14","cited_by_count":5,"area":null},{"id":"2211.02578","title":"Data Models for Dataset Drift Controls in Machine Learning With Optical Images","created":"2022-11-04","cited_by_count":5,"area":null},{"id":"2405.10143","title":"Relational DNN Verification With Cross Executional Bound Refinement","created":"2024-05-16","cited_by_count":5,"area":null},{"id":"2112.05235","title":"The Fundamental Limits of Interval Arithmetic for Neural Networks","created":"2021-12-09","cited_by_count":4,"area":null},{"id":"2211.17244","title":"Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations","created":"2022-11-30","cited_by_count":4,"area":null},{"id":"2301.08842","title":"Limitations of Piecewise Linearity for Efficient Robustness Certification","created":"2023-01-21","cited_by_count":4,"area":null},{"id":"2302.04379","title":"Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples","created":"2023-02-09","cited_by_count":4,"area":null},{"id":"2309.11005","title":"It's Simplex! Disaggregating Measures to Improve Certified Robustness","created":"2023-09-20","cited_by_count":4,"area":null},{"id":"2506.13024","title":"Position: Certified Robustness Does Not (Yet) Imply Model Security","created":"2025-06-16","cited_by_count":4,"area":null},{"id":"2508.09320","title":"Exact Verification of Graph Neural Networks with Incremental Constraint Solving","created":"2025-08-12","cited_by_count":4,"area":null},{"id":"2111.07683","title":"Reachability analysis of neural networks using mixed monotonicity","created":"2021-11-15","cited_by_count":3,"area":null},{"id":"2204.13963","title":"Tailored Uncertainty Estimation for Deep Learning Systems","created":"2022-04-29","cited_by_count":3,"area":null},{"id":"2210.16940","title":"FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs","created":"2022-10-30","cited_by_count":3,"area":null},{"id":"2301.13845","title":"Interpreting Robustness Proofs of Deep Neural Networks","created":"2023-01-31","cited_by_count":3,"area":null},{"id":"2402.08400","title":"Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing","created":"2024-02-13","cited_by_count":3,"area":null},{"id":"2411.18235","title":"Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control","created":"2024-11-27","cited_by_count":3,"area":null},{"id":"2602.06105","title":"Robustness Verification of Polynomial Neural Networks","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2106.03099","title":"A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification","created":"2021-06-06","cited_by_count":2,"area":null},{"id":"2304.13019","title":"Certifying Ensembles: A General Certification Theory with S-Lipschitzness","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2305.16998","title":"A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation","created":"2023-05-26","cited_by_count":2,"area":null},{"id":"2309.13150","title":"Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations","created":"2023-09-22","cited_by_count":2,"area":null},{"id":"2406.03862","title":"Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation","created":"2024-06-06","cited_by_count":2,"area":"deeprl"},{"id":"2408.10491","title":"Achieving the Tightest Relaxation of Sigmoids for Formal Verification","created":"2024-08-20","cited_by_count":2,"area":null},{"id":"2410.06816","title":"Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification","created":"2024-10-09","cited_by_count":2,"area":null},{"id":"2502.16286","title":"Verification of Bit-Flip Attacks against Quantized Neural Networks","created":"2025-02-22","cited_by_count":2,"area":null},{"id":"2505.22798","title":"Efficient Preimage Approximation for Neural Network Certification","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2605.25346","title":"Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2102.05096","title":"Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2108.11299","title":"Certifiers Make Neural Networks Vulnerable to Availability Attacks","created":"2021-08-25","cited_by_count":1,"area":null},{"id":"2208.07464","title":"An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models","created":"2022-08-15","cited_by_count":1,"area":null},{"id":"2211.09810","title":"Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone","created":"2022-11-13","cited_by_count":1,"area":null},{"id":"2303.11552","title":"Boosting Verified Training for Robust Image Classifications via Abstraction","created":"2023-03-21","cited_by_count":1,"area":null},{"id":"2303.13588","title":"Efficient Symbolic Reasoning for Neural-Network Verification","created":"2023-03-23","cited_by_count":1,"area":"lr"},{"id":"2304.07980","title":"RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks","created":"2023-04-17","cited_by_count":1,"area":null},{"id":"2306.06995","title":"How robust accuracy suffers from certified training with convex relaxations","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2310.09361","title":"Is Certifying $\\ell_p$ Robustness Still Worthwhile?","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2405.17049","title":"Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization","created":"2024-05-27","cited_by_count":1,"area":null},{"id":"2501.13094","title":"Robust Representation Consistency Model via Contrastive Denoising","created":"2025-01-22","cited_by_count":1,"area":"diffusion"},{"id":"2504.08414","title":"Adversarial Examples in Environment Perception for Automated Driving (Review)","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2605.26577","title":"Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2607.14375","title":"Random Parameter Noise Does Not Make Exact ReLU Verification Easy","created":"2026-07-15","cited_by_count":1,"area":null},{"id":"2609.10951","title":"Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove","created":"2026-09-10","cited_by_count":1,"area":null},{"id":"2210.00178","title":"On the tightness of linear relaxation based robustness certification methods","created":"2022-10-01","cited_by_count":0,"area":null},{"id":"2210.12054","title":"Towards Global Neural Network Abstractions with Locally-Exact Reconstruction","created":"2022-10-21","cited_by_count":0,"area":null}],"next_cursor":"100"}