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Safety

317 directly classified papers

Papers per year

Papers

Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning AAAI 2023
Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers AAAI 2023
Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks AAAI 2023
Rethinking Safe Control in the Presence of Self-Seeking Humans AAAI 2023
Safety Validation of Learning-Based Autonomous Systems: A Multi-Fidelity Approach AAAI 2023
Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe AAAI 2023
Advances in AI for Safety, Equity, and Well-Being on Web and Social Media: Detection, Robustness, Attribution, and Mitigation AAAI 2023
Combining Runtime Monitoring and Machine Learning with Human Feedback AAAI 2023
Towards Safe and Resilient Autonomy in Multi-Robot Systems AAAI 2023
MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning ACL 2023
Text Adversarial Purification as Defense against Adversarial Attacks ACL 2023
Language Detoxification with Attribute-Discriminative Latent Space ACL 2023
TextVerifier: Robustness Verification for Textual Classifiers with Certifiable Guarantees ACL 2023
Defending against Insertion-based Textual Backdoor Attacks via Attribution ACL 2023
Can Large Language Models Safely Address Patient Questions Following Cataract Surgery? ACL 2023
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities NIPS 2023
Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker NIPS 2023
Enhancing Safe Exploration Using Safety State Augmentation NIPS 2022
Provable Defense against Backdoor Policies in Reinforcement Learning NIPS 2022
Shield Decentralization for Safe Multi-Agent Reinforcement Learning NIPS 2022
Increasing Confidence in Adversarial Robustness Evaluations NIPS 2022
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP NIPS 2022
Toward Robust Spiking Neural Network Against Adversarial Perturbation NIPS 2022
Risk-Driven Design of Perception Systems NIPS 2022
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach NIPS 2022