2024
Uncertainty-aware autonomous sensing with deep reinforcement learning.
Future Gener. Comput. Syst., 2024
Dynamic Contrastive Learning for Time Series Representation.
CoRR, 2024
2023
The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022.
Syst. Eng., November, 2023
2022
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.
CoRR, 2022
Robust Optimization as Data Augmentation for Large-scale Graphs.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
2021
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting.
Sensors, 2021
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.
Proceedings of the 9th International Conference on Learning Representations, 2021
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
FLAG: Adversarial Data Augmentation for Graph Neural Networks.
CoRR, 2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Information-driven adaptive sensing based on deep reinforcement learning.
Proceedings of the IoT '20: 10th International Conference on the Internet of Things, 2020
2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.
CoRR, 2019
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning.
Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2019
Adversarial training for free!
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning.
Proceedings of the 9th International Conference on the Internet of Things, 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
Visualizing the Loss Landscape of Neural Nets.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
Hoaxing statistical features of the Voynich Manuscript.
Cryptologia, 2017
Visualizing the Loss Landscape of Neural Nets.
CoRR, 2017
Adaptive Consensus ADMM for Distributed Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017
Scalable Classifiers with ADMM and Transpose Reduction.
Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017
2016
Training Neural Networks Without Gradients: A Scalable ADMM Approach.
Proceedings of the 33nd International Conference on Machine Learning, 2016
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016
Introduction to the Symposium on AI and the Mitigation of Human Error.
Proceedings of the 2016 AAAI Spring Symposia, 2016
2015
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.
CoRR, 2015
Variance Reduction for Distributed Stochastic Gradient Descent.
CoRR, 2015
Reports on the 2015 AAAI Spring Symposium Series.
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AI Mag., 2015
Layer-Specific Adaptive Learning Rates for Deep Networks.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015
2014
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy.
Proceedings of the 31th International Conference on Machine Learning, 2014
Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations.
Proceedings of the 2014 AAAI Spring Symposia, 2014
2012
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012
2010
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
An Intensive Introductory Robotics Course Without Prerequisites.
Proceedings of the Enabling Intelligence through Middleware, 2010
2009
Kernelized value function approximation for reinforcement learning.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009
2008
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning.
Proceedings of the Machine Learning, 2008