In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

Despite the success of Random Network Distillation (RND) in various domains, it was shown
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …

Uncertainty quantification (UQ) is essential for creating trustworthy machine learning
models. Recent years have seen a steep rise in UQ methods that can flag suspicious …

Human curiosity has been interpreted as a drive for exploration and modeled by intrinsically
motivated reinforcement learning algorithms. An unresolved challenge in machine learning …

The ability to perform different skills can encourage agents to explore. In this work, we aim to
construct a set of diverse skills which uniformly cover the state space. We propose a …

Self-supervised learning, dubbed “the dark matter of intelligence” 1, is a promising path to
advance machine learning. As opposed to supervised learning, which is limited by the …

This paper presents a comprehensive approach to designing and optimizing a Hierarchical
Rule-Base Reduction (HRBR) based Adaptive-Network-Based Fuzzy Inference System …

A prevalent limitation of optimizing over a single objective is that it can be misguided,
becoming trapped in local optimum. Quality-Diversity (QD) algorithms overcome this …

On the level of decision support, most algorithmic problems encountered in machine
learning are instances of pure prediction or pure automation tasks. This dissertation takes a …

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the
prediction error is an important component that compares the expected and actual rewards …