The goal of the player is to cumulate as much reward as possible over time.

This study was started by Kong et al. [2021]: they gave the first approximation regret analysis of

This study was started by Kong et al. [2021]: they gave the first approximation regret analysis of

The agent's goal is to minimize the expected regret,

A new Le vy process prior is proposed for an uncountable collection of covariate- dependent feature-learning measures; the model is called the kernel beta process (KBP). Available covariates are handled efficiently via the kernel construction, with covariates assumed observed with each data sample ("customer"), and latent covariates learned for each feature ("dish"). Each customer selects dishes from an infinite buffet, in a manner analogous to the beta process, with the added constraint that a customer first decides probabilistically whether to "consider" a dish, based on the distance in covariate space between the customer and dish. If a customer does consider a particular dish, that dish is then selected probabilistically as in the beta process. The beta process is recovered as a limiting case of the KBP.

We study combinatorial multi-armed bandit with probabilistically triggered arms and semi-bandit feedback (CMAB-T).

Users can teach robots complex skills through Learning from Demonstration, which is automatically segmented into primitives and stored in clusters of similar skills. We propose a novel multimodal segmentation method as well as a novel trajectory clustering method. Then, when needed for reuse, we transform primitives into new environments using trajectory editing. We present simulated results for our framework with demonstrations taken on real-world robots.

ML-based systems are used today in a wide range of areas, and increasingly also in safety-critical domains. Their range of application is growing exponentially. At the same time, more and more experts are warning of the uncertainties and risks associated with the uncontrolled and overly rapid development of AI systems Bengio et al. [22.03.2023]. In general, there is a growing need to provide methods and procedures for testing functioning and quality characteristics of these systems. Various methods currently exist to test and verify ML-based systems, be it formal verification, simulation approaches or classical testing Albarghouthi, Jackson et al., Vasu Singh et al., or new analysis methods in the context of XAI Hoyer et al., Guidotti et al.. The methods aim for providing evidence on the robustness and trustworthiness of the ML models or ML-based system (ML - Machine Learning). Similar to the traditional development of complex software systems, testing has also proven to be the most effective method for proving quality and gaining trust in ML.

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.