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Found 11 papers, 4 papers with code
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Autonomous systems are increasingly implemented using end-to-end learning-based controllers.
Memory-Consistent Neural Networks for Imitation Learning
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations.
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems.
Using Semantic Information for Defining and Detecting OOD Inputs
This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information (e. g. training class labels).
Take Me Home: Reversing Distribution Shifts using Reinforcement Learning
To aid in our estimates of Wasserstein distance, we employ dimensionality reduction through orthonormal projection.
Credal Bayesian Deep Learning
We show that CBDL is better at quantifying and disentangling different types of uncertainties than single BNNs, ensemble of BNNs, and Bayesian Model Averaging.
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Next, using these memories we partition the state space into disjoint subsets and compute bounds that should be respected by the neural network in each subset.
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Algorithm design of AT and its variants are focused on training models at a specified perturbation strength $\epsilon$ and only using the feedback from the performance of that $\epsilon$-robust model to improve the algorithm.
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning
We propose a new method for classification which can improve robustness to distribution shifts, by combining expert knowledge about the ``high-level" structure of the data with standard classifiers.
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
We formulate expert intervention as allowing the agent to execute option templates before learning an implementation.
Output Range Analysis for Deep Neural Networks
We demonstrate the effectiveness of the proposed approach for verification of NNs used in automated control as well as those used in classification.
