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Found 9 papers, 4 papers with code
Error-Driven Uncertainty Aware Training
In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural models to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate.
CURE: Simulation-Augmented Auto-Tuning in Robotics
This paper proposes CURE -- a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance.
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations
The adoption of cyber-physical systems (CPS) is on the rise in complex physical environments, encompassing domains such as autonomous vehicles, the Internet of Things (IoT), and smart cities.
Hyper-parameter Tuning for Adversarially Robust Models
This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i. e., adversarially trained) models, shedding light on the new challenges and opportunities arising during the HPT process for robust models.
CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots
Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance.
HyperJump: Accelerating HyperBand via Risk Modelling
In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e. g., training with sub-sampled datasets) in order to efficiently identify promising configurations to be then tested via high-fidelity observations (e. g., using the full dataset).
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process while keeping into account user-specified constraints.
Tradeoff-Focused Contrastive Explanation for MDP Planning
In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Modern cyber-physical systems (e. g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.
