Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the training dataset, introduced upon collecting data or due to the practioner rebalancing the training batches. This paper lays the theoretical and computational framework for training models, and for prediction, in the presence of prevalence bias. Concretely a bias-corrected loss function, as well as bias-corrected predictive rules, are derived under the principles of Bayesian risk minimization. The loss exhibits a direct connection to the information gain. It offers a principled alternative to heuristic training losses and complements test-time procedures based on selecting an operating point from summary curves. It integrates seamlessly in the current paradigm of (deep) learning using stochastic backpropagation and naturally with Bayesian models.

Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

The prevalence of volatility in the stock market, makes predicting stock prices anything but simple. Before investing, investors perform two kinds of analysis [patel2015predicting]. The first of this is fundamental analysis, where investors look into the value of stocks, the industry performance, economical factors, etc. and decide whether or not to invest. Technical analysis is the second, more advanced, analysis which involves evaluating those stocks through the use of statistics and activity in the current market, such as volume traded and previous price levels [patel2015predicting]. Technical analysts use charts to recognise patterns and try to predict how a stock price will change. Malkiel and Fama's Efficient market hypothesis states that predicting values of stocks considering financial information is possible, because the prices are informationally efficient [malkiel1970efficient].

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.

Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods with the computational benefits of variational inference. In practice, SVGD relies on the choice of an appropriate kernel function, which impacts its ability to model the target distribution -- a challenging problem with only heuristic solutions. We propose Neural Variational Gradient Descent (NVGD), which is based on parameterizing the witness function of the Stein discrepancy by a deep neural network whose parameters are learned in parallel to the inference, mitigating the necessity to make any kernel choices whatsoever. We empirically evaluate our method on popular synthetic inference problems, real-world Bayesian linear regression, and Bayesian neural network inference.

Reinforcement learning (RL) has shown a promising performance in learning optimal policies for a variety of sequential decision-making tasks. However, in many real-world RL problems, besides optimizing the main objectives, the agent is expected to satisfy a certain level of safety (e.g., avoiding collisions in autonomous driving). While RL problems are commonly formalized as Markov decision processes (MDPs), safety constraints are incorporated via constrained Markov decision processes (CMDPs). Although recent advances in safe RL have enabled learning safe policies in CMDPs, these safety requirements should be satisfied during both training and in the deployment process. Furthermore, it is shown that in memory-based and partially observable environments, these methods fail to maintain safety over unseen out-of-distribution observations. To address these limitations, we propose a Lyapunov-based uncertainty-aware safe RL model. The introduced model adopts a Lyapunov function that converts trajectory-based constraints to a set of local linear constraints. Furthermore, to ensure the safety of the agent in highly uncertain environments, an uncertainty quantification method is developed that enables identifying risk-averse actions through estimating the probability of constraint violations. Moreover, a Transformers model is integrated to provide the agent with memory to process long time horizons of information via the self-attention mechanism. The proposed model is evaluated in grid-world navigation tasks where safety is defined as avoiding static and dynamic obstacles in fully and partially observable environments. The results of these experiments show a significant improvement in the performance of the agent both in achieving optimality and satisfying safety constraints.

MCMC methods are a family of algorithms that uses Markov Chains to perform Monte Carlo estimate. The name gives us a hint, that it is composed of two components -- Monte Carlo and Markov Chain. Let us understand them separately and in their combined form. Monte Carlo method derives its name from a Monte Carlo casino in Monaco. It is a technique for sampling from a probability distribution and using those samples to approximate desired quantity. In other words, it uses randomness to estimate some deterministic quantity of interest.

We propose a models with lower latency and power consumption while Bayesian approach to trend detection in which also ensuring privacy. However, as there is no access to the probability of a keyword being trendy, given actual data from participating devices, it poses a problem a dataset, is computed via Bayes' Theorem; the for the analysis of federated learning models. Federated analytics probability of a dataset, given that a keyword (Ramage & Mazzocchi) is a practice introduced to is trendy, is computed through secure aggregation solve this problem. It uses the same infrastructure as federated of such conditional probabilities over local learning to aggregate the computed metric by each datasets of users. We propose a protocol, named participating device using local data and shared models. SAFE, for Bayesian federated analytics that offers Federated analytics has already gone beyond just measuring sufficient privacy for production-grade use the quality metric to computing descriptive statistics cases and reduces the computational burden of (Ramage & Mazzocchi; Zhu et al., 2020), generating synthetic users and an aggregator. We illustrate this approach data (Xin et al., 2020; Chaulwar, 2020) and learning with a trend detection experiment and discuss new insights (Chen et al., 2019). These methods are generally how this approach could be extended further combined with secure aggregation protocols to ensure to make it production-ready.

Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models. Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence. Explicitly, assessing uncertainties associated with DNNs predictions is critical in real-world card fraud detection settings for characteristic reasons, including (a) fraudsters constantly change their strategies, and accordingly, DNNs encounter observations that are not generated by the same process as the training distribution, (b) owing to the time-consuming process, very few transactions are timely checked by professional experts to update DNNs. Therefore, this study proposes three uncertainty quantification (UQ) techniques named Monte Carlo dropout, ensemble, and ensemble Monte Carlo dropout for card fraud detection applied on transaction data. Moreover, to evaluate the predictive uncertainty estimates, UQ confusion matrix and several performance metrics are utilized. Through experimental results, we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions. Additionally, we demonstrate that the proposed UQ methods provide extra insight to the point predictions, leading to elevate the fraud prevention process.

We perform approximate inference in state-space models that allow for nonlinear higher-order Markov chains in latent space. The conditional independencies of the generative model enable us to parameterize only an inference model, which learns to estimate clean states in a self-supervised manner using maximum likelihood. First, we propose a recurrent method that is trained directly on noisy observations. Afterward, we cast the model such that the optimization problem leads to an update scheme that backpropagates through a recursion similar to the classical Kalman filter and smoother. In scientific applications, domain knowledge can give a linear approximation of the latent transition maps. We can easily incorporate this knowledge into our model, leading to a hybrid inference approach. In contrast to other methods, experiments show that the hybrid method makes the inferred latent states physically more interpretable and accurate, especially in low-data regimes. Furthermore, we do not rely on an additional parameterization of the generative model or supervision via uncorrupted observations or ground truth latent states. Despite our model's simplicity, we obtain competitive results on the chaotic Lorenz system compared to a fully supervised approach and outperform a method based on variational inference.