A new form of ML knowledge representation with high generalization power is developed and implemented numerically. Initial $\mathit{IN}$ attributes and $\mathit{OUT}$ class label are transformed into the corresponding Hilbert spaces by considering localized wavefunctions. A partially unitary operator optimally converting a state from $\mathit{IN}$ Hilbert space into $\mathit{OUT}$ Hilbert space is then built from an optimization problem of transferring maximal possible probability from $\mathit{IN}$ to $\mathit{OUT}$, this leads to the formulation of a new algebraic problem. Constructed Knowledge Generalizing Operator $\mathcal{U}$ can be considered as a $\mathit{IN}$ to $\mathit{OUT}$ quantum channel; it is a partially unitary rectangular matrix of the dimension $\mathrm{dim}(\mathit{OUT}) \times \mathrm{dim}(\mathit{IN})$ transforming operators as $A^{\mathit{OUT}}=\mathcal{U} A^{\mathit{IN}} \mathcal{U}^{\dagger}$. Whereas only operator $\mathcal{U}$ projections squared are observable $\left\langle\mathit{OUT}|\mathcal{U}|\mathit{IN}\right\rangle^2$ (probabilities), the fundamental equation is formulated for the operator $\mathcal{U}$ itself. This is the reason of high generalizing power of the approach; the situation is the same as for the Schr\"{o}dinger equation: we can only measure $\psi^2$, but the equation is written for $\psi$ itself.

This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback. A major part of this thesis focuses on the unsupervised sequential selection problem, where one can not infer the loss incurred for selecting an action from observed feedback. We also introduce a new setup named Censored Semi Bandits, where the loss incurred for selecting an action can be observed under certain conditions. Finally, we study the channel selection problem in the communication networks, where the reward for an action is only observed when no other player selects that action to play in the round. These problems find applications in many fields like healthcare, crowd-sourcing, security, adaptive resource allocation, among many others. This thesis aims to address the above-described sequential decision problems by exploiting specific structures these problems exhibit. We develop provably optimal algorithms for each of these setups with weak feedback and validate their empirical performance on different problem instances derived from synthetic and real datasets.

Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting the likelihood $\log p(x)$ for the training data, we instead fit the score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the partition function. Though this estimator is known to be consistent, its unclear whether (and when) its statistical efficiency is comparable to that of maximum likelihood -- which is known to be (asymptotically) optimal. We initiate this line of inquiry in this paper, and show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated -- i.e. the Poincar\'e, log-Sobolev and isoperimetric constant -- quantities which govern the mixing time of Markov processes like Langevin dynamics. Roughly, we show that the score matching estimator is statistically comparable to the maximum likelihood when the distribution has a small isoperimetric constant. Conversely, if the distribution has a large isoperimetric constant -- even for simple families of distributions like exponential families with rich enough sufficient statistics -- score matching will be substantially less efficient than maximum likelihood. We suitably formalize these results both in the finite sample regime, and in the asymptotic regime. Finally, we identify a direct parallel in the discrete setting, where we connect the statistical properties of pseudolikelihood estimation with approximate tensorization of entropy and the Glauber dynamics.

The goal of co-clustering is to jointly perform a clustering of rows and a clustering of columns of a data table. Proposed by [Good, 1965] then by [Hartigan, 1975], co-clustering is an extension of the standard clustering that extracts the underlying structure in the data in the form of clusters of row and clusters of columns. The advantage of this technique, over the standard clustering, lies in the joint (simultaneous) analysis of the rows and columns which enables extracting the maximum of information about the interdependence between the two entities. The utility of co-clustering lies in its capacity to create easily interpretable clusters and its capability to reduce a large data table into a significantly smaller matrix having the same structure as the orig-Aichetou Bouchareb, Marc Boullé and Fabrice Clérot: Orange Labs, 2 Avenue Pierre Marzin 22300 Lannion - France, e-mail: firstname.

In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.

An ML model is a collection of rules and preferences applied to a dataset, which enables computers to make predictions. Learning includes collecting data, cleaning it, and training the model using more powerful algorithms and/or new datasets. Once trained, your computer can make predictions with high accuracy over many cases. While there are techniques like gradient descent, transfer learning, batch normalisation, etc, for enhancing models, there are various algorithms that are useful for solving different types of problems and training a model. This article covers algorithms for training machine learning models, including neural networks, bayesian inference, and probabilistic inference.

My client, a world leader in the defence sector, requires an Machine Learning Algorithm Developer to join them in Bristol and work as part of a team on the development and evaluation of state-of-the-art algorithms for the guidance, control and navigation of their missile and weapon systems.
 
The Machine Learning Algorithm Developer will work within a team of Intelligent Systems, Autonomous Systems and Command and Control Engineers to develop and evaluate state-of-the-art algorithms across a range of domains from on-board, autonomous decision making to off-board algorithms. The work will involve the research, development, test, evaluation and implementation of algorithms that integrate into complex guided weapon systems products.
 
Algorithms are central to the design of sophisticated guided weapon systems products. These algorithms are developed throughout the lifecycle of the product and include research studies to investigate algorithms for future developments.

Machine Learning Algorithm Developers are involved in the lifecycle of projects, playing a pivotal role in our product developments including:
 
Technical development of specific algorithms or studies for key programmes.Feasibility studies, algorithm design and trade-off studies, preparing trials, trials analysis and reporting, defining architecture, validating algorithms and models.Technical assessments and investigations into a full range of issues and problems and prepare and develop solutions either solely or as a member of a project team.Engaging with the algorithm users, understanding and responding to their needs and ensure that the algorithms are fit for purpose. 
You will gain exposure to a range of other related subject areas e.g. Simulation and Modelling, Software, Hardware-in-the Loop, Systems Design & Validation, Seekers & Sensors, Datalinks and Technical Quality and will be exposed to cutting-edge technological innovations, playing a meaningful role through the development of complex weapon systems.
 
To be considered for this role, applicants will ideally have completed (or be soon to complete) a PhD level in a related area with a good degree in a subject with strong mathematical content and programming skills e.g. Engineering, Mathematics, Physics, Computer Science, Information Engineering. 
 
You will have previous experience in the development and practical application of algorithms, with experience in some of the following:
 
Robotics, data fusion, tracking/estimation, pattern discovery & recognition, statistical inference, optimisation and machine/deep learning algorithms along with real-time implementation, and/or validation & verification.
 
You will also have experience in some of the following: Matlab, Simulink, Stateflow, Python including PyTorch, TensorFlow, Open AI-Gym/Universe, Model Based Design.
 
Specific knowledge or experience in any of these areas would also be ideal:
 
Robotics, guidance and autonomous decision making, e.g. Routing and motion/trajectory planning, optimisation, co-ordinated guidance and control, decision theory, MDPs/POMDPs, specialist systems, game theory, decision support systems, multi-agent systemsData fusion and state estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster-Shafer theory of evidenceMachine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clusteringDeep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent Networks, natural language processingComputer Vision algorithms e.g. Structure from motion, image Based navigation, SLAM, pose estimation/recovery 
Machine Learning Algorithm Developer
Bristol
Salary £35-50k plus benefits DOE
 
Key Skills: Intelligent Systems Engineer, Intelligent Autonomous Systems Engineer, IAS Engineer, PhD, Mathematics, Algorithms, Programming, Robotics, Autonomous Decision Making, Machine Learning, Deep Learning, Data Fusion, Pattern Discovery, Pattern Recognition, Computer Vision, Machine Vision, Matlab, Simulink, Stateflow, Python, PyTorch
   
Due to the nature of work undertaken at our client's site, incumbents of these positions are required to meet special nationality rules and therefore these vacancies are only open to sole British Citizens. Applicants who meet these criteria will also be required to undergo security clearance vetting, if not already security cleared to a minimum SC level.
 
Electus Recruitment Solutions provides specialist engineering and technical recruitment solutions to a number of high technology industries. We thank you for your interest in this vacancy. If you don't hear from us within 7 working days please presume your application has been unsuccessful on this occasion. You are of course free to resubmit your CV/details in the future and we shall assess your suitability at that time. 
   
This role is a PERMANENT position

We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection-diffusion equation. We provide a new information theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one parameter family of probability distributions solving an integro-differential equation derived from Bayes' law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation which we dub Bayesian Diffusion. Identifying the features that define Bayesian Diffusion, and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.

In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable $Y$. We study under the context of binary generalized linear models (BGLMs) with a succinct parametric representation of the causal models. We present the algorithm BGLM-OFU for Markovian BGLMs (i.e. no hidden variables) based on the maximum likelihood estimation method, and show that it achieves $O(\sqrt{T}\log T)$ regret, where $T$ is the time horizon. For the special case of linear models with hidden variables, we apply causal inference techniques such as the do-calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables. Our novelty includes (a) considering the combinatorial intervention action space and the general causal models including ones with hidden variables, (b) integrating and adapting techniques from diverse studies such as generalized linear bandits and online influence maximization, and (c) avoiding unrealistic assumptions (such as knowing the joint distribution of the parents of $Y$ under all interventions) and regret factors exponential to causal graph size in prior studies.