KompAKI EP 3 & 4 Seminar

Abstract: Classical approaches for OLAP assume that the data of all tables is complete. However, in case of incomplete tables with missing tuples, classical approaches fail since the result of a SQL aggregate query might significantly differ from the results computed on the full dataset. Today, the only way to deal with missing data is to manually complete the dataset which causes not only high efforts but also requires good statistical skills to determine when a dataset is actually complete. In this talk, we present an automated approach for relational data completion called ReStore using a new class of (neural) schema-structured completion models that are able to synthesize data which resembles the missing tuples. As we show in our evaluation, this efficiently helps to reduce the relative error of aggregate queries by up to 390% on real-world data compared to using the incomplete data directly for query answering.

Abstract: Automated machine learning (AutoML) supports ML engineers and data scientists by automating tasks like model selection and hyperparameter optimization. A number of AutoML solutions have been developed, open-source and commercial. We propose a concept called OMA-ML (Ontology-based Meta AutoML) that combines the strengths of existing AutoML solutions by integrating them (meta AutoML). OMA-ML is based on a ML ontology that guides the meta AutoML process. It supports multiple user groups, with and without programming skills. By combining the strengths of AutoML solutions, it supports any number of ML tasks and ML libraries.

Abstract: While probabilistic circuits have been extensively explored for tabular data, less attention has been paid to time series. In such scenario, the goal is to estimate joint densities among the entire time series and, in turn, determining, for instance, conditional independence relations between them. To this end, we propose the first probabilistic circuits (PCs) approach for modeling the joint distribution of multivariate time series, called Whittle sum-product networks (WSPNs). WSPNs leverage the Whittle approximation, casting the likelihood in the frequency domain, and place a complex-valued sum-product network, the most prominent PC, over the frequencies. The conditional independence relations among the time series can then be determined efficiently in the spectral domain. Moreover, WSPNs can naturally be placed into the deep neural learning stack for time series, resulting in Whittle Networks, opening the likelihood toolbox for training deep neural models and inspecting their behaviour. Our experiments show that Whittle Networks can indeed capture complex dependencies between time series and provide a useful measure of uncertainty for neural networks.

Abstract: Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality data throughout the entire life-cycle of such applications. Nevertheless, most of the real-world structured data suffer from different types of discrepancies, such as missing values, outliers, duplicates, patter violation, and inconsistencies. Such discrepancies typically emerge while collecting, transferring, storing, and/or integrating the data. To deal with these discrepancies, numerous data cleaning methods have been introduced from the data management community and the ML community. However, the majority of such methods broadly overlook the requirements imposed by downstream ML models. As a result, the potential of utilizing these data cleaning methods in ML pipelines is predominantly unrevealed. In this work, we introduce a comprehensive benchmark to thoroughly investigate the impact of data cleaning methods on various ML models. Through the benchmark, we provide answers to important research questions, e.g., where and whether data cleaning is a necessary step in the ML pipeline. To this end, the benchmark examines several simple and advanced error detection and repair methods. To evaluate these methods, we utilize publicly-available datasets covering different domains together with a wide collection of ML models.

Abstract: Especially in the diverse and fast-paced field of Artificial Intelligence it is imperative to have a clear picture of relevant competencies and how they are distributed within or over organisations. For this purpose, we have developed a generic competency ontology that can be used to describe competencies of people and organisational structures in the Artificial Intelligence domain. The ontology is embedded in an application to create, manage, and utilize a Competency Knowledge Graph. In our presentation we show concrete application scenarios, advantages, and challenges.

Abstract: An important task for enabling the efficient exploration of available data in a data lake is to annotate semantic type information to the available data sources. In order to reduce the manual overhead of annotation, learned approaches for automatic metadata extraction on structured data sources have been proposed recently. While initial results of these learned approaches seem promising, it is still not clear how well these approaches can generalize to new unseen data in real-world data lakes. In this tallk, we aim to tackle this question and as a first contribution show the result of a study when applying Sato — a recent approach based on deep learning — to a real-world data set. In our study we show that Sato without re-training is only able to extract semantic data types for about 10% of the columns of the real-world data set. These results show the general limitation of deep learning approaches which often provide near-perfect performance on available training and testing data but fail in real settings since training data and real data often strongly vary. Hence, as a second contribution we propose a new direction of using weak supervision and present results of an initial prototype we built to generate labeled training data with low manual efforts to improve the performance of learned semantic type extraction approaches on new unseen data sets.

Abstract: AutoML is one of the hottest research-branches in the AI-community. Since ML is being applied on problems across many domains from economy to research, the job of ‘Data Scientists’ has evolved in the last years. Data Scientists are people who gather data, understand business/research objectives and try to extract new knowledge from data, often using ML-techniques. However, Data Scientists are often a bottleneck on the way of knowledge generation because there is still much manual work included in Data Science-tasks. AutoML tackles this problem by automating the Data Science-process with the goal to remove the hurdles from using ML. There are already systems automating parts of the Data Science-process, however more research is needed in order to end up with a system capable of fully automating the Data Science-cycle from data extraction to model evaluation.

Abstract: Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new correlated Dirichlet distribution. This distribution explicitly models positive correlations between marginally Dirichlet-distributed random vectors thereby allowing explicit modeling of correlations between base classifiers or experts. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets. We show that a change in performance of the fused classifier due to uncertainty reduction can be Bayes optimal even for highly correlated base classifiers.

Abstract: We developed models that “understand” images, code, and natural language, and put them to use for driving cars, correcting code, and explaining jokes. However, we have long ignored the relational data that dominates the enterprise data landscape. The top-3 database systems, for example, are all relational database systems, and the dominant file formats (37%) indexed by Google Dataset Search are all tabular. To make the “modern data stack” intelligent towards relational data, we need to shift our attention from Large Language Models to Large Table Models. In this talk, I will discuss the progress in this direction, and present Sherlock; a model for surfacing the semantics of table columns, GitTables; a large-scale corpus of tables extracted from CSV files on GitHub, and close with ongoing efforts.

Abstract: Hyperparameters play a key role in determining many algorithms’ performance. We have seen, however, that choosing a single static value for each hyperparameter may not be optimal. Schedules and heuristics that adapt e.g. learning rates for neural networks or step sizes in evolutionary algorithms show that different timepoints in the algorithm’s runtime have different ideal hyperparameter settings. Dynamic Algorithm Configuration aims to improve upon simple heuristics by learning to control hyperparameters dynamically across different algorithm instances. This talk will cover recent advances in dynamic configuration in Machine Learning as well as open challenges for future research.

Abstract: In this talk, I will cover the challenges of and solutions for scaling interactive visualization systems to large data. The challenges for scalable visualization systems can be grouped into two areas: visual and interactive scalability. A visual representation that shows every record as its own mark quickly overwhelms our visual system; we call this problem perceptual scalability. For very large data, necessary transformations can overwhelm the data processing system; we call this the problem of interactive scalability. In this talk, I will elaborate on these problems and present solutions that enable interactive analysis of billions of records without any negative impacts of latency.
The given talk is an extended version of a section of my job talk recorded at Microsoft Research.

Abstract: In this paper, we present our vision of differentiable ML pipelines called DiffML that truly allows automating the construction of ML pipelines in an end-to-end fashion. Recently there has been a lot of work on automating individual steps of ML pipelines, such as data cleaning. However, while these techniques help with reducing the manual effort, it is not guaranteed that they also improve the performance of the downstream ML model. To this end, with DiffML we propose to jointly train the ML model with the data engineering steps in an automated manner. Our core novel idea is to formulate all steps and their alternatives in a differentiable way such that the entire ML pipeline can be trained end-to-end using backpropagation. However, this is a non-trivial problem and opens up many new research questions. To show the feasibility of this direction, we demonstrate initial ideas and a general principle of how typical data engineering steps can be formulated as differentiable programs and jointly learned with the ML model. Moreover, we discuss a research roadmap and core challenges that have to be systematically tackled to enable fully differentiable ML pipelines.

Abstract: AutoML is a well established research field worked by international researchers, that even has its own dedicated conference since this year. The tedious work machine learning scientists are doing on their day-to-day life inspired researchers to work on the automation of different aspects aiming to get the best performing model. For a long time, this challenge has been tackled with immense computational power resulting in an immense environmental footprint. Due to the ongoing climate change, the consequences of emitted CO2e are becoming more and more perceptible. As a consequence, a shift in the AutoML community towards the consideration of its environmental footprint is of extreme importance and inevitable. In this talk, options for quantifying such a footprint are summarized, as well as existing work as strategies on how to design and benchmark AutoML tools with their footprint in mind are revisited. Furthermore, different aspects on how to be transparent about the environmental footprint and what kind of research incentives could direct the community into a more sustainable AutoML research direction are elaborated on.

Abstract: Hyperparameter Optimization (HPO) has been widely known and applied in both academic and industry. But the process can be costly, especially for tuning hyperparameters for deep learning models, hindering its further adoption. In this talk, we will start with basics of HPO and move to improving its efficiency through transfer learning and review some notable works during the past few years. Finally, we will introduce automatic termination that speeds up the process from an orthogonal direction.

Abstract: AI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation, energy, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many vision applications. In this talk, I will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency in vision tasks. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low computation costs and high task accuracy.

Abstract: With the rise of pre-trained Large Language Models (LLMs), there is now an effective solution to store and use information extracted from massive corpora of text documents. Thus, we envision the use of SQL queries to cover a broad range of data that is not captured by traditional databases (DBs) by tapping the information in LLMs. This ability would enable the hybrid querying of both LLMs and DBs with the SQL interface, which is more expressive and precise than NL prompts. To ground this vision, we present a prototype based on a traditional DB architecture with new physical operators for querying the underlying LLM. For a large class of SQL queries, querying LLMs returns well structured relations, with encouraging qualitative results. We pinpoint several research challenges that must be addressed to build a DBMS that jointly exploits LLMs and DBs. While some challenges call for new contributions from the NLP field, others offer novel research avenues for the DB community.

Abstract: The release of ChatGPT and the fast development of GPTs have resulted in a transformative trend in our daily lives: the utilization of prompts has become increasingly popular. In this talk, I will present a new versatile paradigm, leveraging Large Language Models (LLMs) for time-series forecasting. This paradigm opens up exciting possibilities in domains such as traffic forecasting and energy demand forecasting, where the integration of natural language prompts not only demonstrates excellent forecasting performance but also shows the flexibility of predictive models. Further, I will introduce and present our recent vision paper: Artificial General Intelligence for Human Mobility. This paper outlines a visionary approach to harnessing artificial general intelligence to revolutionize the way to develop AI models for human mobility-related tasks. I will also provide insights into the conceptual framework and potential applications of this groundbreaking concept.

Abstract: Traditional query planners translate SQL queries into query plans to be executed over relational data. However, it is impossible to query other data modalities, such as images, text, or video stored in modern data systems such as data lakes using these query planners. In this paper, we propose Language-Model-Driven Query Planning, a new paradigm of query planning that uses Language Models to translate natural language queries into executable query plans. Different from relational query planners, the resulting query plans can contain complex operators that are able to process arbitrary modalities. As part of this paper, we present a first GPT-4 based prototype called CAESURA and show the general feasibility of this idea on two datasets. Finally, we discuss several ideas to improve the query planning capabilities of today’s Language Models.

Abstract: Data engineering tasks, such as data transformation and data cleaning, currently still require a high amount of manual effort. The field of table representation learning develops methods to automate these tedious tasks. However, existing tabular representation models only learn a representation from individual tables. Despite the fact that most organizations use databases, representation learning for relational databases with multiple tables is still an under-explored area. In this talk, we present our vision of relational representation learning, that not only learns from the full structure of relational databases, but also can scale to larger database sizes that are common in the real world.