{"total":143,"rows":[{"id":"1811.07871","title":"Scalable agent alignment via reward modeling: a research direction","created":"2018-11-19","cited_by_count":447,"area":"alignment"},{"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":"2308.06767","title":"A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations","created":"2023-08-13","cited_by_count":202,"area":null},{"id":"2406.10162","title":"Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models","created":"2024-06-14","cited_by_count":178,"area":null},{"id":"2309.01446","title":"Open Sesame! Universal Black Box Jailbreaking of Large Language Models","created":"2023-09-04","cited_by_count":134,"area":"safety"},{"id":"2104.09425","title":"Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?","created":"2021-04-19","cited_by_count":113,"area":null},{"id":"2102.08452","title":"Globally-Robust Neural Networks","created":"2021-02-16","cited_by_count":108,"area":null},{"id":"2104.07167","title":"Orthogonalizing Convolutional Layers with the Cayley Transform","created":"2021-04-14","cited_by_count":105,"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":"2105.11417","title":"Skew Orthogonal Convolutions","created":"2021-05-24","cited_by_count":67,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2111.01395","title":"Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds","created":"2021-11-02","cited_by_count":65,"area":null},{"id":"2110.12690","title":"A Dynamical System Perspective for Lipschitz Neural Networks","created":"2021-10-25","cited_by_count":60,"area":null},{"id":"2108.04204","title":"Meta Gradient Adversarial Attack","created":"2021-08-09","cited_by_count":55,"area":null},{"id":"2210.04871","title":"Certified Training: Small Boxes are All You Need","created":"2022-10-10","cited_by_count":52,"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":"2108.04062","title":"Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100","created":"2021-08-05","cited_by_count":47,"area":null},{"id":"2103.17268","title":"Fast Certified Robust Training with Short Warmup","created":"2021-03-31","cited_by_count":46,"area":null},{"id":"2110.07719","title":"Certified Patch Robustness via Smoothed Vision Transformers","created":"2021-10-11","cited_by_count":45,"area":null},{"id":"2102.05363","title":"Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons","created":"2021-02-10","cited_by_count":40,"area":null},{"id":"2203.14195","title":"How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective","created":"2022-03-27","cited_by_count":39,"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":"2104.02466","title":"A Review of Formal Methods applied to Machine Learning","created":"2021-04-06","cited_by_count":33,"area":null},{"id":"2108.12099","title":"Learning to Give Checkable Answers with Prover-Verifier Games","created":"2021-08-27","cited_by_count":32,"area":null},{"id":"2102.07861","title":"Low Curvature Activations Reduce Overfitting in Adversarial Training","created":"2021-02-15","cited_by_count":29,"area":null},{"id":"2306.12688","title":"Towards quantum enhanced adversarial robustness in machine learning","created":"2023-06-22","cited_by_count":28,"area":null},{"id":"2306.10426","title":"Understanding Certified Training with Interval Bound Propagation","created":"2023-06-17","cited_by_count":26,"area":null},{"id":"2102.04154","title":"Efficient Certified Defenses Against Patch Attacks on Image Classifiers","created":"2021-02-08","cited_by_count":24,"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":"2105.12508","title":"Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers","created":"2021-05-26","cited_by_count":23,"area":null},{"id":"2305.13991","title":"Expressive Losses for Verified Robustness via Convex Combinations","created":"2023-05-23","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":"2210.06983","title":"Denoising Masked AutoEncoders Help Robust Classification","created":"2022-10-10","cited_by_count":21,"area":null},{"id":"2106.06685","title":"Adversarial Robustness via Fisher-Rao Regularization","created":"2021-06-12","cited_by_count":19,"area":null},{"id":"2302.01757","title":"RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion","created":"2023-01-31","cited_by_count":17,"area":null},{"id":"2311.09994","title":"Towards more Practical Threat Models in Artificial Intelligence Security","created":"2023-11-16","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":"2108.05623","title":"Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks","created":"2021-08-12","cited_by_count":16,"area":null},{"id":"2202.11910","title":"Robust Probabilistic Time Series Forecasting","created":"2022-02-24","cited_by_count":15,"area":null},{"id":"2206.10057","title":"Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum","created":"2022-06-21","cited_by_count":15,"area":null},{"id":"2208.13058","title":"Adversarial Robustness for Tabular Data through Cost and Utility Awareness","created":"2022-08-27","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":"2206.07311","title":"Can pruning improve certified robustness of neural networks?","created":"2022-06-15","cited_by_count":14,"area":null},{"id":"2308.01578","title":"Unsupervised Representation Learning for Time Series: A Review","created":"2023-08-03","cited_by_count":14,"area":null},{"id":"2406.04848","title":"CTBENCH: A Library and Benchmark for Certified Training","created":"2024-06-07","cited_by_count":14,"area":null},{"id":"2102.01886","title":"Learning Diverse-Structured Networks for Adversarial Robustness","created":"2021-02-03","cited_by_count":12,"area":null},{"id":"2102.06700","title":"On the Paradox of Certified Training","created":"2021-02-12","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":"2112.12084","title":"Input-Specific Robustness Certification for Randomized Smoothing","created":"2021-12-21","cited_by_count":11,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2305.04574","title":"TAPS: Connecting Certified and Adversarial Training","created":"2023-05-08","cited_by_count":11,"area":null},{"id":"2402.02629","title":"PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks","created":"2024-02-04","cited_by_count":11,"area":null},{"id":"2201.12733","title":"TPC: Transformation-Specific Smoothing for Point Cloud Models","created":"2022-01-30","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":"2207.02152","title":"UniCR: Universally Approximated Certified Robustness via Randomized Smoothing","created":"2022-07-05","cited_by_count":10,"area":null},{"id":"2210.01111","title":"MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples","created":"2022-10-03","cited_by_count":9,"area":null},{"id":"2211.08453","title":"Improved techniques for deterministic l2 robustness","created":"2022-11-15","cited_by_count":9,"area":null},{"id":"2211.10752","title":"Towards Robust Dataset Learning","created":"2022-11-19","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":"2311.04015","title":"Expressivity of ReLU-Networks under Convex Relaxations","created":"2023-11-07","cited_by_count":8,"area":null},{"id":"2101.02115","title":"Adversarial Robustness by Design through Analog Computing and Synthetic Gradients","created":"2021-01-06","cited_by_count":7,"area":null},{"id":"2104.00322","title":"Domain Invariant Adversarial Learning","created":"2021-04-01","cited_by_count":7,"area":null},{"id":"2106.09121","title":"Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework","created":"2021-06-16","cited_by_count":7,"area":null},{"id":"2111.13841","title":"Adaptive Perturbation for Adversarial Attack","created":"2021-11-27","cited_by_count":7,"area":null},{"id":"2202.01679","title":"Certifying Out-of-Domain Generalization for Blackbox Functions","created":"2022-02-03","cited_by_count":7,"area":null},{"id":"2210.14283","title":"Accelerating Certified Robustness Training via Knowledge Transfer","created":"2022-10-25","cited_by_count":7,"area":null},{"id":"2212.08649","title":"Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification","created":"2022-12-16","cited_by_count":7,"area":null},{"id":"2106.06624","title":"Relaxing Local Robustness","created":"2021-06-11","cited_by_count":6,"area":null},{"id":"2111.06961","title":"Adversarially Robust Learning for Security-Constrained Optimal Power Flow","created":"2021-11-12","cited_by_count":6,"area":null},{"id":"2207.05327","title":"Certified Adversarial Robustness via Anisotropic Randomized Smoothing","created":"2022-07-12","cited_by_count":6,"area":null},{"id":"2211.12624","title":"Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization","created":"2022-11-22","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":"2102.01208","title":"Fast Training of Provably Robust Neural Networks by SingleProp","created":"2021-02-01","cited_by_count":5,"area":null},{"id":"2203.08961","title":"On the Convergence of Certified Robust Training with Interval Bound Propagation","created":"2022-03-16","cited_by_count":5,"area":null},{"id":"2304.02693","title":"A Certified Radius-Guided Attack Framework to Image Segmentation Models","created":"2023-04-05","cited_by_count":5,"area":null},{"id":"2310.02480","title":"Splitting the Difference on Adversarial Training","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2404.13456","title":"Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation","created":"2024-04-20","cited_by_count":5,"area":null},{"id":"2509.22849","title":"Parameterized Hardness of Zonotope Containment and Neural Network Verification","created":"2025-09-26","cited_by_count":5,"area":null},{"id":"2103.00778","title":"Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis","created":"2021-03-01","cited_by_count":4,"area":null},{"id":"2109.00936","title":"Impact of Attention on Adversarial Robustness of Image Classification Models","created":"2021-09-02","cited_by_count":4,"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":"2208.08609","title":"A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables","created":"2022-08-18","cited_by_count":4,"area":null},{"id":"2209.04293","title":"Robust-by-Design Classification via Unitary-Gradient Neural Networks","created":"2022-09-09","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":"2403.07095","title":"Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations","created":"2024-03-11","cited_by_count":4,"area":null},{"id":"2404.17371","title":"Estimating the Robustness Radius for Randomized Smoothing with 100$\\times$ Sample Efficiency","created":"2024-04-26","cited_by_count":4,"area":null},{"id":"2602.16944","title":"Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming","created":"2026-02-18","cited_by_count":4,"area":"safety"},{"id":"2103.00363","title":"Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search","created":"2021-02-28","cited_by_count":3,"area":null},{"id":"2107.03250","title":"Understanding Intrinsic Robustness Using Label Uncertainty","created":"2021-07-07","cited_by_count":3,"area":null},{"id":"2202.11246","title":"Learning Neural Networks under Input-Output Specifications","created":"2022-02-23","cited_by_count":3,"area":null},{"id":"2204.05432","title":"A Simple Approach to Adversarial Robustness in Few-shot Image Classification","created":"2022-04-11","cited_by_count":3,"area":null},{"id":"2207.07209","title":"Sound Randomized Smoothing in Floating-Point Arithmetics","created":"2022-07-14","cited_by_count":3,"area":null},{"id":"2309.06166","title":"Certified Robust Models with Slack Control and Large Lipschitz Constants","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2404.08285","title":"A Survey of Neural Network Robustness Assessment in Image Recognition","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2411.01644","title":"Achieving Domain-Independent Certified Robustness via Knowledge Continuity","created":"2024-11-03","cited_by_count":3,"area":null},{"id":"2103.07595","title":"Learning Defense Transformers for Counterattacking Adversarial Examples","created":"2021-03-13","cited_by_count":2,"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":"2106.04435","title":"Enhancing Robustness of Neural Networks through Fourier Stabilization","created":"2021-06-08","cited_by_count":2,"area":null},{"id":"2108.04217","title":"ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients","created":"2021-07-06","cited_by_count":2,"area":null},{"id":"2202.02626","title":"Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework","created":"2022-02-05","cited_by_count":2,"area":null}],"next_cursor":"100"}