Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer the task objective, which is not realistic in many practical applications. To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning. We develop a meta-RL algorithm that enables fast policy adaptation with preference-based feedback. The agent can adapt to new tasks by querying human's preference between behavior trajectories instead of using per-step numeric rewards. By extending techniques from information theory, our approach can design query sequences to maximize the information gain from human interactions while tolerating the inherent error of non-expert human oracle. In experiments, we extensively evaluate our method, Adaptation with Noisy OracLE (ANOLE), on a variety of meta-RL benchmark tasks and demonstrate substantial improvement over baseline algorithms in terms of both feedback efficiency and error tolerance.Comment: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

User-Centric Active Learning for Outlier Detection

Outlier detection searches for unusual, rare observations in large, often high-dimensional data sets. One of the fundamental challenges of outlier detection is that ``unusual\u27\u27 typically depends on the perception of a user, the recipient of the detection result. This makes finding a formal definition of ``unusual\u27\u27 that matches with user expectations difficult. One way to deal with this issue is active learning, i.e., methods that ask users to provide auxiliary information, such as class label annotations, to return algorithmic results that are more in line with the user input. Active learning is well-suited for outlier detection, and many respective methods have been proposed over the last years. However, existing methods build upon strong assumptions. One example is the assumption that users can always provide accurate feedback, regardless of how algorithmic results are presented to them -- an assumption which is unlikely to hold when data is high-dimensional. It is an open question to which extent existing assumptions are in the way of realizing active learning in practice. In this thesis, we study this question from different perspectives with a differentiated, user-centric view on active learning. In the beginning, we structure and unify the research area on active learning for outlier detection. Specifically, we present a rigorous specification of the learning setup, structure the basic building blocks, and propose novel evaluation standards. Throughout our work, this structure has turned out to be essential to select a suitable active learning method, and to assess novel contributions in this field. We then present two algorithmic contributions to make active learning for outlier detection user-centric. First, we bring together two research areas that have been looked at independently so far: outlier detection in subspaces and active learning. Subspace outlier detection are methods to improve outlier detection quality in high-dimensional data, and to make detection results more easy to interpret. Our approach combines them with active learning such that one can balance between detection quality and annotation effort. Second, we address one of the fundamental difficulties with adapting active learning to specific applications: selecting good hyperparameter values. Existing methods to estimate hyperparameter values are heuristics, and it is unclear in which settings they work well. In this thesis, we therefore propose the first principled method to estimate hyperparameter values. Our approach relies on active learning to estimate hyperparameter values, and returns a quality estimate of the values selected. In the last part of the thesis, we look at validating active learning for outlier detection practically. There, we have identified several technical and conceptual challenges which we have experienced firsthand in our research. We structure and document them, and finally derive a roadmap towards validating active learning for outlier detection with user studies

Generalized Probabilistic Bisection for Stochastic Root-Finding

This thesis studies the stochastic root-finding problem, which consists of estimating the point x∗ that solves the equation h(x∗) = 0, where the function h : (0,1) → R is learned via a stochastic simulator (oracle). Instead of focusing on modeling h(·), we develop statistical methodologies that directly infer x∗ following a fully Bayesian approach. To do so, we investigate procedures that generalize the Probabilistic Bisection Algorithm (PBA) first introduced in Horstein (1963). The PBA is a one-dimensional stochastic root-finding routine which builds an explicit Bayesian representation (i.e., a posterior density) for x∗ based on the history of noisy function evaluations and sampling locations. The PBA starts by assuming that x∗ is the realized value of an absolutely continuous random variable, X∗ ∼ g0, with prior density g0. Then, it recursively updates a posterior, gn, leveraging the information provided by the signs (positive/negative) of the noisy function evaluations — which inform the direction where x∗ is located with respect to a given location, x—. Due to observational noise, the oracle responses are correct only with probability p(x). Waeber et al. (2013) showed that sampling at the median of gn is an optimal sampling strategy and established exponential convergence of the posterior gn to a Dirac mass at the true x∗ under the very restrictive assumption that the probability of correct response p(x) is known and constant for all x; however, in the most general and practical settings the latter condition no longer holds and the only way to implement the PBA is to estimate p(·).In the first part of this thesis, we state the Generalized PBA (G-PBA), where the above assumption is relaxed to the case where the sampling distribution of the oracle is unknown and location-dependent. Namely, as in standard PBA, we rely on a knowledge state to approximate the posterior of the root location. To implement the corresponding Bayesian updating, we also carry out inference of p(·). To this end we utilize batched querying in combination with a variety of frequentist and Bayesian estimators based on majority vote, as well as the underlying functional responses, if available. For guiding sampling selection we propose two families of sampling policies: batched Information Di- rected Sampling and Randomized Quantile Sampling, which is a reminiscent of Thompson Sampling and a generalization of the median sampling as in classical PBA. The latter leads to the first main conclusion: the G-PBA is able to efficiently learn p(·) and X∗ simultaneously.In the second part of this thesis, we propose to leverage the spatial structure of a typical oracle by constructing a non-parametric statistical surrogate for p(·) based on binomial regression. The latter leads to the second main conclusion: surrogate modeling allows to determine the batch size for querying the oracle adaptively as a function of the estimated predictive uncertainty of p(·).In the last part of this thesis, we present extensive numerical experiments in order to evaluate our sampling strategies (information-based or randomized). In particular we demonstrate the efficiency of randomized quantile sampling for balancing the ex- ploration/exploitation component; moreover, we show that spatial surrogate modeling results in significant gains relative to the local estimators, as quantified by the improved quality of the resulting root estimates (namely lower absolute residuals, narrower credible intervals and dramatically higher probability coverage). Our work is motivated by the root-finding sub-routine in pricing of Bermudan financial derivatives, illustrated in the last section of this thesis

Yedalog: Exploring Knowledge at Scale

With huge progress on data processing frameworks, human programmers are frequently the bottleneck when analyzing large repositories of data. We introduce Yedalog, a declarative programming language that allows programmers to mix data-parallel pipelines and computation seamlessly in a single language. By contrast, most existing tools for data-parallel computation embed a sublanguage of data-parallel pipelines in a general-purpose language, or vice versa. Yedalog extends Datalog, incorporating not only computational features from logic programming, but also features for working with data structured as nested records. Yedalog programs can run both on a single machine, and distributed across a cluster in batch and interactive modes, allowing programmers to mix different modes of execution easily

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Implicit Incremental Model Analyses and Transformations

When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC)

An Approach for Guiding Developers to Performance and Scalability Solutions

This thesis proposes an approach that enables developers who are novices in software performance engineering to solve software performance and scalability problems without the assistance of a software performance expert. The contribution of this thesis is the explicit consideration of the implementation level to recommend solutions for software performance and scalability problems. This includes a set of description languages for data representation and human computer interaction and a workflow