We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction setup for user profiling. A common feature of online social networks (OSNs) is the possibility of individuals to continuously interact among themselves, by sharing contents and expressing opinions or ratings on different topics [1], [2].

The \textit{sampling policy} of stage $t$, formally expressed as a probability density function $q_t$, stands for the distribution of the sample $(x_{t,1},\ldots, x_{t,n_t})$ generated at $t$. From the past samples, some information depending on some \textit{objective} is derived leading eventually to update the sampling policy to $q_{t+1}$. This generic approach characterizes \textit{adaptive importance sampling} (AIS) schemes. Each stage $t$ is formed with two steps : (i) to explore the space with $n_t$ points according to $q_t$ and (ii) to exploit the current amount of information to update the sampling policy. The very fundamental question raised in the paper concerns the behavior of empirical sums based on AIS. Without making any assumption on the \textit{allocation policy} $n_t$, the theory developed involves no restriction on the split of computational resources between the explore (i) and the exploit (ii) step. It is shown that AIS is efficient : the asymptotic behavior of AIS is the same as some "oracle" strategy that knows the optimal sampling policy from the beginning. From a practical perspective, weighted AIS is introduced, a new method that allows to forget poor samples from early stages.

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the datasets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP graph inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.

We extend the decomposition approach for learning Bayesian networks (BN) proposed by (Xie et al., 2006) to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduces complexity and increased power of computational independence tests. Moreover, latent (hidden) variables can be represented in MVR CGs by using bidirected edges, and our algorithm correctly recovers any independence structure that is faithful to a MVR CG, thus greatly extending the range of applications of decomposition-based model selection techniques. While our new algorithm has the same complexity as the one in (Xie et al., 2006) for BNs, it requires larger components for general MVR CGs, to insure that sufficient data is present to estimate parameters.

The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment. We propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN by using good one such as NEC at the beginning of learning. We show it is able to learn faster than Double DQN or N-step DQN in the experiments of Pong.

A value learning system has incentives to follow shutdown instructions, assuming the shutdown instruction provides information (in the technical sense) about which actions lead to valuable outcomes. However, this assumption is not robust to model mis-specification (e.g., in the case of programmer errors). We demonstrate this by presenting some Supervised POMDP scenarios in which errors in the parameterized reward function remove the incentive to follow shutdown commands. These difficulties parallel those discussed by Soares et al. (2015) in their paper on corrigibility. We argue that it is important to consider systems that follow shutdown commands under some weaker set of assumptions (e.g., that one small verified module is correctly implemented; as opposed to an entire prior probability distribution and/or parameterized reward function). We discuss some difficulties with simple ways to attempt to attain these sorts of guarantees in a value learning framework.

In classification applications such as severe disease diagnosis and fraud detection, people have clear priorities over the two types of classification errors. For instance, diagnosing a patient with cancer to be healthy may lead to loss of life, which incurs a much higher cost than the other way around. The classical binary classification paradigm does not take into account such priorities, as it aims to minimize the overall classification error. In contrast, the Neyman-Pearson (NP) paradigm seeks classifiers with a minimal type II error while having the prioritized type I error constrained under a user-specified level, addressing asymmetric type I/II error priorities in the previously mentioned scenarios. Despite recent advances in the NP classification literature, two essential issues pose challenges: i) current theoretical framework assumes bounded feature support, which does not admit parametric settings; ii) in practice, existing NP classifiers involve splitting class 0 samples into two parts using a pre-fixed split proportion. To address the first challenge, we present NP-sLDA that adapts the popular sparse linear discriminant analysis (sLDA, Mai et al. (2012)) to the NP paradigm. On the theoretical front, this is the first theoretically justified NP classifier that takes parametric assumptions and unbounded feature support. We formulate a new conditional margin assumption and a new conditional detection condition to accommodate unbounded feature support and show that NP-sLDA satisfies the NP oracle inequalities. Numerical results show that NP-sLDA is a valuable addition to the existing NP classifiers. To address the second challenge, we construct a general data-adaptive sample splitting scheme that improves the classification performance upon the default half-half class 0 split used in Tong et al. (2018).

Logistic Regression is one of the most used Machine Learning algorithms for binary classification. It is a simple Algorithm that you can use as a performance baseline, it is easy to implement and it will do well enough in many tasks. Therefore every Machine Learning engineer should be familiar with its concepts. The building block concepts of Logistic Regression can also be helpful in deep learning while building neural networks. In this post, you will learn what Logistic Regression is, how it works, what are advantages and disadvantages and much more.

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.

This note considers softmax parameter estimation when little/no labeled training data is available, but a priori information about the relative geometry of class label log-odds boundaries is available. It is shown that `data-free' softmax model synthesis corresponds to solving a linear system of parameter equations, wherein desired dominant class log-odds boundaries are encoded via convex polytopes that decompose the input feature space. When solvable, the linear equations yield closed-form softmax parameter solution families using class boundary polytope specifications only. This allows softmax parameter learning to be implemented without expensive brute force data sampling and numerical optimization. The linear equations can also be adapted to constrained maximum likelihood estimation in data-sparse settings. Since solutions may also fail to exist for the linear parameter equations derived from certain polytope specifications, it is thus also shown that there exist probabilistic classification problems over m convexly separable classes for which the log-odds boundaries cannot be learned using an m-class softmax model.