This paper addresses the $\epsilon$-close parameter tuning problem for Bayesian Networks (BNs): find a minimal $\epsilon$-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid. Based on the state-of-the-art "region verification" techniques for parametric Markov chains, we propose an algorithm whose capabilities go beyond any existing techniques. Our experiments show that $\epsilon$-close tuning of large BN benchmarks with up to 8 parameters is feasible. In particular, by allowing (i) varied parameters in multiple CPTs and (ii) inter-CPT parameter dependencies, we treat subclasses of parametric BNs that have received scant attention so far.

We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, we infer CoT recipes that work consistently well for a set of training samples. Our method iteratively samples new recipes using previously sampled solutions as parent prompts to solve other training problems. On five Big-Bench Hard tasks that require multi-step reasoning, Reprompting achieves consistently better performance than the zero-shot, few-shot, and human-written CoT baselines. Reprompting can also facilitate transfer of knowledge from a stronger model to a weaker model leading to substantially improved performance of the weaker model. Overall, Reprompting brings up to +17 point improvements over the previous state-of-the-art method that uses human-written CoT prompts.

We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We show that the degree of improvement must depend on the quality of the demonstration data. To generate portable insights, we focus on Thompson sampling (TS) applied to a multi-armed bandit as a prototypical online learning algorithm and model. The demonstration data is generated by an expert with a given competence level, a notion we introduce. We propose an informed TS algorithm that utilizes the demonstration data in a coherent way through Bayes' rule and derive a prior-dependent Bayesian regret bound. This offers insight into how pretraining can greatly improve online performance and how the degree of improvement increases with the expert's competence level. We also develop a practical, approximate informed TS algorithm through Bayesian bootstrapping and show substantial empirical regret reduction through experiments.

In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded.

We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a purely probabilistic model. We show the model's competitiveness on scarce supervision scenarios, handling missing data, and graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries.

Du e to rapid population growth and the need to use artificial intelligence to make quick decisions, developing a machine learning-based disease detection model and abnormality identification system has greatly improved the level of medical diagnosis Since COVID-19 has become one of the most severe diseases in the world, developing an automatic COVID-19 detection framework helps medical doctors in the diagnostic process of disease and provides correct and fast results. In this paper, we propose a machine lear ning based framework for the detection of Covid 19. The proposed model employs a Tsukamoto Neuro Fuzzy Inference network to identify and distinguish Covid 19 disease from normal and pneumonia cases. While the traditional training methods tune the parameters of the neuro-fuzzy model by gradient-based algorithms and recursive least square method, we use an evolutionary-based optimization, the Cat swarm algorithm to update the parameters. In addition, six texture features extracted from chest X-ray images are give n as input to the model. Finally, the proposed model is conducted on the chest X-ray dataset to detect Covid 19. The simulation results indicate that the proposed model achieves an accuracy of 98.51%, sensitivity of 98.35%, specificity of 98.08%, and F1 score of 98.17%.

Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. As a popular approach, probabilistic rotation modeling additionally carries prediction uncertainty information, compared to single-prediction rotation regression. For modeling probabilistic distribution over SO(3), it is natural to use Gaussian-like Bingham distribution and matrix Fisher, however they are shown to be sensitive to outlier predictions, e.g. $180^\circ$ error and thus are unlikely to converge with optimal performance. In this paper, we draw inspiration from multivariate Laplace distribution and propose a novel rotation Laplace distribution on SO(3). Our rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region that it can improve. In addition, we show that our method also exhibits robustness to small noises and thus tolerates imperfect annotations. With this benefit, we demonstrate its advantages in semi-supervised rotation regression, where the pseudo labels are noisy. To further capture the multi-modal rotation solution space for symmetric objects, we extend our distribution to rotation Laplace mixture model and demonstrate its effectiveness. Our extensive experiments show that our proposed distribution and the mixture model achieve state-of-the-art performance in all the rotation regression experiments over both probabilistic and non-probabilistic baselines.

Bayesian networks (BN) are directed acyclic graphical (DAG) models that have been adopted into many fields for their strengths in transparency, interpretability, probabilistic reasoning, and causal modeling. Given a set of data, one hurdle towards using BNs is in building the network graph from the data that properly handles dependencies, whether correlated or causal. In this paper, we propose an initial methodology for discovering network structures using Tsetlin Machines.

In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational bottleneck with exact GP modeling, we employ a combined global-local approach in building the approximation. Our framework uses a subset-of-data approach where the subset is a union of a set of global points designed to capture the global trend in the data, and a set of local points specific to a given testing location to capture the local trend around the testing location. The correlation function is also modeled as a combination of a global, and a local kernel. The performance of our framework, which we refer to as TwinGP, is on par or better than the state-of-the-art GP modeling methods at a fraction of their computational cost.

Accessibility is one of the most important features in the design of robots and their interfaces. This thesis proposes methods that improve the accessibility of robots for three different target audiences: consumers, researchers, and learners. In order for humans and robots to work together effectively, they both must be able to communicate with each other. We tackle the problem of generating route instructions that are readily understandable by novice humans for the navigation of a priori unknown indoor environments. We then move on to the related problem of enabling robots to understand natural language utterances in the context of learning to operate articulated objects (e.g., fridges, drawers) by leveraging kinematic models. Next, we turn our focus to the development of accessible and reproducible robotic platforms for scientific research. We propose a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained "by design" from the beginning of the research and development process. We then propose a framework called SHARC (SHared Autonomy for Remote Collaboration), to improve accessibility for underwater robotic intervention operations. SHARC allows multiple remote scientists to efficiently plan and execute high-level sampling procedures using an underwater manipulator while deferring low-level control to the robot. Lastly, we developed the first hardware-based MOOC in AI and robotics. This course allows learners to study autonomy hands-on by making real robots make their own decisions and accomplish broadly defined tasks. We design a new robotic platform from the ground up to support this new learning experience. A fully browser-based interface, based on leading tools and technologies for code development, testing, validation, and deployment serves to maximize the accessibility of these educational resources.