Uncertainty quantification is an important research area in machine learning. Many approaches have been developed to improve the representation of uncertainty in deep models to avoid overconfident predictions. Existing ones such as Bayesian neural networks and ensemble methods require modifications to the training procedures and are computationally costly for both training and inference. Motivated by this, we propose mean-field infinitesimal jackknife (mfIJ) -- a simple, efficient, and general-purpose plug-in estimator for uncertainty estimation. The main idea is to use infinitesimal jackknife, a classical tool from statistics for uncertainty estimation to construct a pseudo-ensemble that can be described with a closed-form Gaussian distribution, without retraining. We then use this Gaussian distribution for uncertainty estimation. While the standard way is to sample models from this distribution and combine each sample's prediction, we develop a mean-field approximation to the inference where Gaussian random variables need to be integrated with the softmax nonlinear functions to generate probabilities for multinomial variables. The approach has many appealing properties: it functions as an ensemble without requiring multiple models, and it enables closed-form approximate inference using only the first and second moments of Gaussians. Empirically, mfIJ performs competitively when compared to state-of-the-art methods, including deep ensembles, temperature scaling, dropout and Bayesian NNs, on important uncertainty tasks. It especially outperforms many methods on out-of-distribution detection.

Semi-supervised Laplacian regularization, a standard graph-based approach for learning from both labelled and unlabelled data, was recently demonstrated to have an insignificant high dimensional learning efficiency with respect to unlabelled data (Mai and Couillet, 2018), causing it to be outperformed by its unsupervised counterpart, spectral clustering, given sufficient unlabelled data. Following a detailed discussion on the origin of this inconsistency problem, a novel regularization approach involving centering operation is proposed as solution, supported by both theoretical analysis and empirical results.

Bayesian optimisation (BO) has been widely used for hyperparameter optimisation but its application in neural architecture search (NAS) is limited due to the non-continuous, high-dimensional and graph-like search spaces. Current approaches either rely on encoding schemes, which are not scalable to large architectures and ignore the implicit topological structure of architectures, or use graph neural networks, which require additional hyperparameter tuning and a large amount of observed data, which is particularly expensive to obtain in NAS. We propose a neat BO approach for NAS, which combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate to capture the topological structure of architectures, without having to explicitly define a Gaussian process over high-dimensional vector spaces. We also harness the interpretable features learnt via the graph kernel to guide the generation of new architectures. We demonstrate empirically that our surrogate model is scalable to large architectures and highly data-efficient; competing methods require 3 to 20 times more observations to achieve equally good prediction performance as ours. We finally show that our method outperforms existing NAS approaches to achieve state-of-the-art results on NAS datasets.

Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision-making problem where, at every time step, it observes its state, performs an action, receives a reward and moves to a new state. An RL agent learns by trial and error a good policy (or controller) based on observations and numeric reward feedback on the previously performed action. In this chapter, we present the basic framework of RL and recall the two main families of approaches that have been developed to learn a good policy. The first one, which is value-based, consists in estimating the value of an optimal policy, value from which a policy can be recovered, while the other, called policy search, directly works in a policy space. Actor-critic methods can be seen as a policy search technique where the policy value that is learned guides the policy improvement. Besides, we give an overview of some extensions of the standard RL framework, notably when risk-averse behavior needs to be taken into account or when rewards are not available or not known.

Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge discovery. We study the problem of learning the sparse DAG structure of a BN from continuous observational data. The central problem can be modeled as a mixed-integer program with an objective function composed of a convex quadratic loss function and a regularization penalty subject to linear constraints. The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions. However, the state-of-the-art optimization solvers are not able to obtain provably optimal solutions to the existing mathematical formulations for medium-size problems within reasonable computational times. To address this difficulty, we tackle the problem from both computational and statistical perspectives. On the one hand, we propose a concrete early stopping criterion to terminate the branch-and-bound process in order to obtain a near-optimal solution to the mixed-integer program, and establish the consistency of this approximate solution. On the other hand, we improve the existing formulations by replacing the linear "big-$M$" constraints that represent the relationship between the continuous and binary indicator variables with second-order conic constraints. Our numerical results demonstrate the effectiveness of the proposed approaches.

We conduct a thorough analysis of the relationship between the out-of-sample performance and the Bayesian evidence (marginal likelihood) of Bayesian neural networks (BNNs), as well as looking at the performance of ensembles of BNNs, both using the Boston housing dataset. Using the state-of-the-art in nested sampling, we numerically sample the full (non-Gaussian and multimodal) network posterior and obtain numerical estimates of the Bayesian evidence, considering network models with up to 156 trainable parameters. The networks have between zero and four hidden layers, either $\tanh$ or $ReLU$ activation functions, and with and without hierarchical priors. The ensembles of BNNs are obtained by determining the posterior distribution over networks, from the posterior samples of individual BNNs re-weighted by the associated Bayesian evidence values. There is good correlation between out-of-sample performance and evidence, as well as a remarkable symmetry between the evidence versus model size and out-of-sample performance versus model size planes. Networks with $ReLU$ activation functions have consistently higher evidences than those with $\tanh$ functions, and this is reflected in their out-of-sample performance. Ensembling over architectures acts to further improve performance relative to the individual BNNs.

We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.

Sony has officially unveiled the PlayStation 5, the company's next generation gaming console planned for launch'later this year.' In a new video presentation titled'The Future of Gaming,' Sony also revealed the PlayStation 5 will be available in two models. One model will include a standard a disc drive, while a second option, called'PlayStation 5 Digital Edition,' will lack a disc drive and likely only play games downloaded over the internet but not physical discs bought in stores. Sony revealed the full design for the PlayStation 5, which will be available in two different versions, one with a disc drive and one without, called'PlayStation 5 Digital Edition' In a new video presentation titled'The Future of Gaming,' Sony also revealed the PlayStation 5 will be available in two models. One model will include a standard a disc drive, while a second option, called'PlayStation 5 Digital Edition,' will be disc free Sony did not announce a price point or launch date, but said it still plans to release the PlayStation 5 before the end of the year.

Google's new drone delivery service Wing will help bring library books to school children in Christiansburg, Virginia to help make up for the city's library closures during the COVID-19 pandemic. The new initiative is being overseen by Kelly Passek, a librarian for Montgomery County Public Schools, who first pitched the idea to Wing. Students in Christiansburg can submit a request for books in the school district's library system and Passek will pull the book from the stacks and send it out in one of Wing's custom delivery containers. Google's Wing drone delivery service will now bring library books to school children in Christiansburg, Virginia'I think kids are going to be just thrilled to learn that they are going to be the first in the world to receive a library book by drone,' Passek told The Washington Post. Passek initially got the idea after wondering about how the 600-plus students in the school district were fairing after the county closed school campuses and libraries.

Falaah Arif Khan is the creator of "Meet AI" – a scientific comic strip about the human-AI story. She currently works as a Research Engineer at Dell EMC, Bangalore, but will shortly will be heading to New York University's Center for Data Science to pursue a Master's in Data Science. We talked about some of the machine learning projects she's worked on, her comic book creations, and the need for clear and accurate communication in the field of AI. I like to describe my research area as meta-security. When customers come to us it is to enhance the security of their product through access management, service authorization, session management and/or authentication. My role within the team is to use data-driven insights to build features that will bolster the security of our Identity and Access Management (IAM) product.