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AI Safety

3,026 papers

Papers per year

5
111
1031
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Papers

Towards Trustable Explainable AI IJCAI 2020
A Gamified Assessment Platform for Predicting the Risk of Dementia +Parkinson’s disease (DPD) Co-Morbidity IJCAI 2020
Adversarial Separation Network for Speaker Recognition INTERSPEECH 2020
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification INTERSPEECH 2020
Branch and Bound for Piecewise Linear Neural Network Verification JMLR 2020
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification L4DC 2020
Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence L4DC 2020
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability L4DC 2020
On the Robustness of Data-Driven Controllers for Linear Systems L4DC 2020
Learning for Safety-Critical Control with Control Barrier Functions L4DC 2020
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees L4DC 2020
Improving Robustness via Risk Averse Distributional Reinforcement Learning L4DC 2020
Safe non-smooth black-box optimization with application to policy search L4DC 2020
Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation MLHC 2020
Network Error Logging: Client-side measurement of end-to-end web service reliability NSDI 2020
Achieving 100Gbps Intrusion Prevention on a Single Server OSDI 2020
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions RSS 2020
Lagrangian Decomposition for Neural Network Verification UAI 2020
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks NIPS 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder NIPS 2019
Likelihood Ratios for Out-of-Distribution Detection NIPS 2019
Unlabeled Data Improves Adversarial Robustness NIPS 2019
Theoretical evidence for adversarial robustness through randomization NIPS 2019
Defending Against Neural Fake News NIPS 2019
Provably robust boosted decision stumps and trees against adversarial attacks NIPS 2019