The code is available on GitHub.

Answers to these questions must begin by formalizing the specification for what a generative algorithm for language should be doing.

We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These demonstrations can be viewed as solving related but slightly different problems than what the learner faces. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information, which is not recorded in the data available to the learning agent. We model the problem as zero-shot meta-reinforcement learning with an unknown distribution over the unobserved contextual variables and a Bayesian regret minimization objective, where the unobserved variables are encoded as parameters with an unknown prior. We propose the Experts-as-Priors algorithm (ExPerior), an empirical Bayes approach that utilizes expert data to establish an informative prior distribution over the learner's decision-making problem. This prior distribution enables the application of any Bayesian approach for online decision-making, such as posterior sampling. We demonstrate that our strategy surpasses existing behaviour cloning, online, and online-offline baselines for multi-armed bandits, Markov decision processes (MDPs), and partially observable MDPs, showcasing the broad reach and utility of ExPerior in using expert demonstrations across different decision-making setups.

Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (V AD) for robotic power line inspection.

Top-down Bird's Eye View (BEV) maps are a popular perceptual representation for ground robot navigation due to their richness and flexibility for downstream

Detecting and handling misspecified objectives, such as reward functions, has been widely recognized as one of the central challenges within the domain of Artificial Intelligence (AI) safety research.

Overnight Russian missile and drone strikes have caused power cuts in large parts of Ukraine's capital, Kyiv. Nine people were injured while residents in eastern districts were plunged into darkness and faced disruption to water supplies, the city's mayor Vitali Klitschko said. Meanwhile, a seven-year-old child was killed in a separate Russian drone strike in the Zaporizhzhia area in the country's south-east, according to the Ukrainian regional head. Moscow has escalated attacks on energy facilities over recent weeks, while Ukrainian President Volodymyr Zelensky has accused Russia of attempting to create chaos and apply psychological pressure. Ukraine's Energy Minister Svitlana Hrynchuk said Russia was inflicting a massive strike on facilities around the country overnight on Thursday, adding that repair crews were working to restore power.