A common task for recommender systems is to build a pro le of the interests of a user from items in their browsing history and later to recommend items to the user from the same catalog. The users' behavior consists of two parts: the sequence of items that they viewed without intervention (the organic part) and the sequences of items recommended to them and their outcome (the bandit part). In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order to improve the estimation of recommendation quality. The bandit signal is valuable as it gives direct feedback of recommendation performance, but the signal quality is very uneven, as it is highly concentrated on the recommendations deemed optimal by the past version of the recom-mender system. In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task. In order to leverage the organic signal to e ciently learn the bandit signal in a Bayesian model we identify three fundamental types of distances, namely action-history, action-action and history-history distances. We implement a scalable approximation of the full model using variational auto-encoders and the local re-paramerization trick. We show using extensive simulation studies that our method out-performs or matches the value of both state-of-the-art organic-based recommendation algorithms, and of bandit-based methods (both value and policy-based) both in organic and bandit-rich environments.

A KL-divergence objective of the joint distribution of data and labels allows to unify supervised learning, variational autoencoders (VAEs) and semi-supervised learning under one umbrella of variational inference. This viewpoint has several advantages. For VAEs, it clarifies the interpretation of encoder and decoder parts. For supervised learning, it re-iterates that the training procedure approximates the true posterior over labels and can always be viewed as approximate likelihood-free inference. This is typically not discussed, even though the derivation is well-known in the literature. In the context of semi-supervised learning it motivates an extended supervised scheme which allows to calculate a goodness-of-fit p-value using posterior predictive simulations. Flow-based networks with a standard normal base distribution are crucial. We discuss how they allow to rigorously define coverage for arbitrary joint posteriors on $\mathbb{R}^n \times \mathcal{S}^m$, which encompasses posteriors over directions. Finally, systematic uncertainties are naturally included in the variational viewpoint. With the three ingredients of (1) systematics, (2) coverage and (3) goodness-of-fit, flow-based neural networks have the potential to replace a large part of the statistical toolbox of the contemporary high-energy physicist.

One of the most difficult problems for an adaptable agent is gauging how to behave in a nonstationary environment. When conditions are stable, an organism generally pursues a strategy known to provide the best outcome. However, when environmental conditions change, an organism abandons the current action plan and searches for a new best option. The most challenging aspect of this search—calculating the exact time point at which to change strategies—requires the brain to integrate past and present observations and evaluate whether they remain consistent with current environmental conditions. On page 1076 of this issue, Domenech et al. ([ 1 ][1]) report on the modeling of rare direct electrical recordings from the prefrontal cortices (PFCs) of a small group of human epilepsy patients as they flexibly negotiated a nonstationary environment. To understand the brain's mode of navigation, consider for example a sailor at sea (see the figure). The winds and the currents determine the waves that drive the sailor to continuously adjust the rudder so as to stay on course. By observing the wave patterns, he can anticipate the navigational effects of his actions and adapt accordingly. But when the currents or the weather changes, the sailor must adapt his course to reach the next port of call. At that time, the sailor observes essentially the same stimulus (the waves) but has to remap his action plan (rudder adjustments) to the new wind conditions and currents. This difficult-decision problem—how to detect and then adapt to a nonstationary environment—is captured perfectly in the exploration-exploitation dilemma: When should I stop exploiting my current action plan and start exploring different ways to reach my goals? An optimal solution tracks the discounted sum of normalized future rewards. However, this approach applies strictly to stationary environments and thus does not capture the dynamic changes that organisms encounter in their daily lives ([ 2 ][2]). Yet the human brain and those of other species seem to smoothly solve the exploration-exploitation dilemma in nonstationary environments. Decision neuroscience has investigated the flexible adaptation to changing environmental contingencies with diverse experimental paradigms and assorted computational models. The simplest paradigm is probabilistic reversal learning, in which the agent has to search for reward among two options with complementary reward probabilities. This adaptation problem can be solved by hidden Markov models ([ 3 ][3]), which are well-approximated by reinforcement learning (RL) models that also update nonchosen actions ([ 4 ][4]). Extension of this paradigm to include independently changing reward probabilities reveals two distinct neural responses: Expected-value signals, which reflect “exploitative” choices, spur activation of the ventromedial prefrontal cortex (vmPFC); and “explorative” choices (that is, the choosing of a currently lesser valued option) activate the frontopolar cortex ([ 5 ][5]). ![Figure][6] A sailor solves a dilemma at sea As the ship nears bad weather, the sailor's ventromedial prefrontal cortex (vmPFC) evaluates the ongoing (orange) action plan (exploitation) and the prospective (brown, red) plans (exploration). Once the red (calm waters) plan is exploited, the sailor's dorsomedial PFC (dmPFC) uses trial-and-error learning to map the proper rudder adjustments. GRAPHIC: A. KITTERMAN/ SCIENCE Another task with both rapid and slow changes in the reward probabilities of various options was used to develop a hierarchical Bayesian model that estimates the volatility of the environment and adjusts the learning rate accordingly ([ 6 ][7]). This model has found its generalization in the hierarchical Gaussian filter (HGF) framework ([ 7 ][8]), which is widely used in modeling social and nonsocial human decision-making in nonstationary environments. Although these computational modeling frameworks differ, all are trying to solve similar problems: How to infer the latent structure of the world from discrete observations and how to detect transitions between different states of the world. Domenech et al. address the same problems with yet another experimental paradigm, this one carried out with a small group of human epilepsy patients. Electrodes deeply implanted in the patients' PFCs delivered direct electrical recordings from the vmPFC and dorsomedial PFC (dmPFC) while the patients performed a multioption decision task. The participants had to associate three different stimuli with three distinct actions, thus constituting an action plan. The mapping changed every 33 to 57 trials, and participants had to relearn the association of the same stimuli with a different combination of actions, much like our sailor at sea who faces changes in weather and currents that alter wave patterns. The computational model ([ 8 ][9]) generates a reliability value for the ongoing action plan and other concurrently monitored plans. When the ongoing action plan is deemed reliable, the model is in “exploitation” mode and learns the stimulus-action mapping through RL mechanisms. When the ongoing action plan is deemed unreliable, the model switches to “exploration” mode. New provisional action plans are created and evaluated, until one emerges as a reliable predictor for successful stimulus-action mapping (see the figure). Using a state-of-the-art model-based analysis that associates the model-derived variables with the brain activity in various frequency bands of the neural recordings, the authors found a delicate interplay between the vmPFC and dmPFC that supports a predictive coding interpretation for resolution of the exploration-exploitation dilemma. vmPFC monitors and represents the reliability of the ongoing action plan. vmPFC relays the ongoing action plan to the dmPFC as either a “stay” or “switch” trial. A stay trial triggers additional learning through RL mechanisms in the dmPFC. In contrast, the dmPFC responds to a switch trial by suppressing activity related to maintaining the ongoing action plan. These findings resonate with and extend earlier results obtained with functional neuroimaging ([ 5 ][5], [ 9 ][10]). These computational approaches to the problem of behavioral flexibility in a nonstationary environment share one commonality: They are all building a model of the environment and the transition therein, either explicitly (as in the HGF framework) or implicitly (by evaluating the ongoing action plan, as in the Domenech et al. study). Although all of these models strive for generality, each was developed for a specific experimental context. It remains to be seen which of these provides the best account of flexible decision-making in humans and other species, preferably using a unified experimental paradigm. A model-free RL account ([ 10 ][11]) likely will not suffice, as several studies have demonstrated the superiority of more-complex models over this “vanilla” RL model. 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Online Courses Udemy Introduction to Machine Learning & Deep Learning in Python, Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks Created by Holczer Balazs Students also bought Cluster Analysis and Unsupervised Machine Learning in Python Feature Engineering for Machine Learning Data Science 2020: Complete Data Science & Machine Learning Machine Learning A-Z: Become Kaggle Master Python for Time Series Data Analysis Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.

Intelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future conversational AI assistants promise even greater capabilities and a better user experience through a deeper understanding of the domain, the user, or the user's purposes. But what domain and what methods are best suited to researching and realizing this promise? In this article we argue for the domain of voice document editing and for the methods of model-based reinforcement learning. The primary advantages of voice document editing are that the domain is tightly scoped and that it provides something for the conversation to be about (the document) that is delimited and fully accessible to the intelligent assistant. The advantages of reinforcement learning in general are that its methods are designed to learn from interaction without explicit instruction and that it formalizes the purposes of the assistant. Model-based reinforcement learning is needed in order to genuinely understand the domain of discourse and thereby work efficiently with the user to achieve their goals. Together, voice document editing and model-based reinforcement learning comprise a promising research direction for achieving conversational AI.

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.

Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mixture models with Expectation-Maximization (EM) is a popular method due to clear statistical properties. However, existing literature on EM-based semi-supervised learning largely focuses on unstructured prediction, assuming that samples are independent and identically distributed. Studies on EM-based semi-supervised approach in structured prediction is limited. This paper aims to fill the gap through a comparative study between unstructured and structured methods in EM-based semi-supervised learning. Specifically, we compare their theoretical properties and find that both methods can be considered as a generalization of self-training with soft class assignment of unlabeled samples, but the structured method additionally considers structural constraint in soft class assignment. We conducted a case study on real-world flood mapping datasets to compare the two methods. Results show that structured EM is more robust to class confusion caused by noise and obstacles in features in the context of the flood mapping application.