We develop a regression based primal-dual martingale approach for solving finite time horizon MDPs with general state and action space. As a result, our method allows for the construction of tight upper and lower biased approximations of the value functions, and, provides tight approximations to the optimal policy. In particular, we prove tight error bounds for the estimated duality gap featuring polynomial dependence on the time horizon, and sublinear dependence on the cardinality/dimension of the possibly infinite state and action space. From a computational point of view the proposed method is efficient since, in contrast to usual duality-based methods for optimal control problems in the literature, the Monte Carlo procedures here involved do not require nested simulations.

$k$-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering $k$-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, with the risk of introducing high bias and high variance. In this work, we fall back to discrete $k$-subset sampling on the forward pass. This is coupled with using the gradient with respect to the exact marginals, computed efficiently, as a proxy for the true gradient. We show that our gradient estimator, SIMPLE, exhibits lower bias and variance compared to state-of-the-art estimators, including the straight-through Gumbel estimator when $k = 1$. Empirical results show improved performance on learning to explain and sparse linear regression. We provide an algorithm for computing the exact ELBO for the $k$-subset distribution, obtaining significantly lower loss compared to SOTA.

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Due to the privacy protection or the difficulty of data collection, we cannot observe individual outputs for each instance, but we can observe aggregated outputs that are summed over multiple instances in a set in some real-world applications. To reduce the labeling cost for training regression models for such aggregated data, we propose an active learning method that sequentially selects sets to be labeled to improve the predictive performance with fewer labeled sets. For the selection measurement, the proposed method uses the mutual information, which quantifies the reduction of the uncertainty of the model parameters by observing the aggregated output. With Bayesian linear basis functions for modeling outputs given an input, which include approximated Gaussian processes and neural networks, we can efficiently calculate the mutual information in a closed form. With the experiments using various datasets, we demonstrate that the proposed method achieves better predictive performance with fewer labeled sets than existing methods.

The notion of smart cities is being adapted globally to provide a better quality of living. A smart city's smart mobility component focuses on providing smooth and safe commuting for its residents and promotes eco-friendly and sustainable alternatives such as public transit (bus). Among several smart applications, a system that provides up-to-the-minute information like bus arrival, travel duration, schedule, etc., improves the reliability of public transit services. Still, this application needs live information on traffic flow, accidents, events, and the location of the buses. Most cities lack the infrastructure to provide these data. In this context, a bus arrival prediction model is proposed for forecasting the arrival time using limited data sets. The location data of public transit buses and spatial characteristics are used for the study. One of the routes of Tumakuru city service, Tumakuru, India, is selected and divided into two spatial patterns: sections with intersections and sections without intersections. The machine learning model XGBoost is modeled for both spatial patterns individually. A model to dynamically predict bus arrival time is developed using the preceding trip information and the machine learning model to estimate the arrival time at a downstream bus stop. The performance of models is compared based on the R-squared values of the predictions made, and the proposed model established superior results. It is suggested to predict bus arrival in the study area. The proposed model can also be extended to other similar cities with limited traffic-related infrastructure.

As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a massive amount of devices with infrequent communication. This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth loss and a non-smooth regularizer. Examples include sparse linear regression using Lasso, low-rank matrix recovery using nuclear norm regularization, etc. In the existing literature, federated composite optimization algorithms are designed only from an optimization perspective without any statistical guarantees. In addition, they do not consider commonly used (restricted) strong convexity in statistical recovery problems. We advance the frontiers of this problem from both optimization and statistical perspectives. From optimization upfront, we propose a new algorithm named \textit{Fast Federated Dual Averaging} for strongly convex and smooth loss and establish state-of-the-art iteration and communication complexity in the composite setting. In particular, we prove that it enjoys a fast rate, linear speedup, and reduced communication rounds. From statistical upfront, for restricted strongly convex and smooth loss, we design another algorithm, namely \textit{Multi-stage Federated Dual Averaging}, and prove a high probability complexity bound with linear speedup up to optimal statistical precision. Experiments in both synthetic and real data demonstrate that our methods perform better than other baselines. To the best of our knowledge, this is the first work providing fast optimization algorithms and statistical recovery guarantees for composite problems in FL.

Accurate and reliable prediction of Photovoltaic (PV) power output is critical to electricity grid stability and power dispatching capabilities. However, Photovoltaic (PV) power generation is highly volatile and unstable due to different reasons. The Wavelet Transform (WT) has been utilised in time series applications, such as Photovoltaic (PV) power prediction, to model the stochastic volatility and reduce prediction errors. Yet the existing Wavelet Transform (WT) approach has a limitation in terms of time complexity. It requires reconstructing the decomposed components and modelling them separately and thus needs more time for reconstruction, model configuration and training. The aim of this study is to improve the efficiency of applying Wavelet Transform (WT) by proposing a new method that uses a single simplified model. Given a time series and its Wavelet Transform (WT) coefficients, it trains one model with the coefficients as features and the original time series as labels. This eliminates the need for component reconstruction and training numerous models. This work contributes to the day-ahead aggregated solar Photovoltaic (PV) power time series prediction problem by proposing and comprehensively evaluating a new approach of employing WT. The proposed approach is evaluated using 17 months of aggregated solar Photovoltaic (PV) power data from two real-world datasets. The evaluation includes the use of a variety of prediction models, including Linear Regression, Random Forest, Support Vector Regression, and Convolutional Neural Networks. The results indicate that using a coefficients-based strategy can give predictions that are comparable to those obtained using the components-based approach while requiring fewer models and less computational time.

The post Boosting in Machine Learning:-A Brief Overview appeared first on Data Science Tutorials What do you have to lose?. Check out Data Science tutorials here Data Science Tutorials. Boosting in Machine Learning, A single predictive model, such as linear regression, logistic regression, ridge regression, etc., is the foundation of the majority of supervised machine learning methods. However, techniques such as bagging and random forests provide a wide range of models from repeated bootstrapped samples of the original dataset. The average of the predictions... Read More “Boosting in Machine Learning:-A Brief Overview” » The post Boosting in Machine Learning:-A Brief Overview appeared first on Data Science Tutorials Learn how to expert in the Data Science field with Data Science Tutorials.

This Free Udemy course has 3 sections. In the first section, you will learn R basics and how to download R and Rstudio. In the next section, you will learn how to code in R programming and understand functions, loops, R datasets, and R dataframes. The last section teaches how to load CSV files in R, how to apply a family of functions, how to test for normality, KNN classification, LDA(Linear Discriminant Analysis), etc. Overall, this is a good course for beginners to learn R programming basics.

This article is part of a series where we walk step by step in solving fintech problems with Machine Learning using "All lending club loan data". In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common "ML model evaluation metrics" for a classification problem in the fintech space. This article will try to "understand" how our model decision works and what packages can help us to answer this question. Machine learning models are frequently named "black boxes". They produce highly accurate predictions.