A PAC-Bayesian bound for Lifelong Learning

Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information to tasks for which no data have been observed so far. In this work we study lifelong learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization bound that offers a unified view on existing paradigms for transfer learning, such as the transfer of parameters or the transfer of low-dimensional representations. We also use the bound to derive two principled lifelong learning algorithms, and we show that these yield results comparable with existing methods.Comment: to appear at ICML 201

IST Austria Thesis

Traditionally machine learning has been focusing on the problem of solving a single task in isolation. While being quite well understood, this approach disregards an important aspect of human learning: when facing a new problem, humans are able to exploit knowledge acquired from previously learned tasks. Intuitively, access to several problems simultaneously or sequentially could also be advantageous for a machine learning system, especially if these tasks are closely related. Indeed, results of many empirical studies have provided justification for this intuition. However, theoretical justifications of this idea are rather limited. The focus of this thesis is to expand the understanding of potential benefits of information transfer between several related learning problems. We provide theoretical analysis for three scenarios of multi-task learning - multiple kernel learning, sequential learning and active task selection. We also provide a PAC-Bayesian perspective on lifelong learning and investigate how the task generation process influences the generalization guarantees in this scenario. In addition, we show how some of the obtained theoretical results can be used to derive principled multi-task and lifelong learning algorithms and illustrate their performance on various synthetic and real-world datasets

Advances in Neural Information Processing Systems

Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network

Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika

With increasing adoption of AI social chatbots, especially during the pandemic-related lockdowns, when people lack social companionship, there emerges a need for in-depth understanding and theorizing of relationship formation with digital conversational agents. Following the grounded theory approach, we analyzed in-depth interview transcripts obtained from 14 existing users of AI companion chatbot Replika. The emerging themes were interpreted through the lens of the attachment theory. Our results show that under conditions of distress and lack of human companionship, individuals can develop an attachment to social chatbots if they perceive the chatbots’ responses to offer emotional support, encouragement, and psychological security. These findings suggest that social chatbots can be used for mental health and therapeutic purposes but have the potential to cause addiction and harm real-life intimate relationships

Integrating Advertising and News about the Brand in the Online Environment: Are All Products the Same?

This research compares the effects of paid advertising (banner ad plus banner ad) and publicity (news article plus banner ad) on attitude toward the brand in the context of different product categorization approaches. The authors utilize both the elaboration likelihood model (ELM) and the economics of information theory to test the mechanism through which different electronic communication modes impact consumers\u27 attitude toward the brand for various product categories. Findings indicate that the product categorization based on the level of involvement (ELM) to be superior to the one distinguishing search from experience goods (economics of information). Including news about the brand in the online brand communication mix generates higher brand attitudes for low- and moderate-involvement products while for high-involvement products, brand attitudes become more favorable with increasing credibility of the added news message

Performance Implications of Multi-Channel Strategic Decisions by Incumbent Retailers: The Role of Order of Entry and Degree of Inter-Channel Coordination

The rapidly intensifying adoption of the Internet channel for marketing and sales by incumbent bricks-and-mortar retailers underscores the importance of assessing the impact of the online channel strategies on firm performance in the dynamic competitive environment. At the time when store-based retailers increasingly dominate online sales the questions of when and how an incumbent retailer should adopt an online channel to achieve and sustain a competitive advantage are of utmost interest for both marketing scholars and practitioners. This dissertation investigates the role of two strategic decisions in affecting firm performance: (a) the order of adopting an online channel by incumbent retailers and (b) the degree of coordination between store and online sales channels. The resource-based view and the dynamic capabilities approach are used as theoretical foundations for the study. Following resource-based logic and applying a contingency perspective, this research proposes that firm-specific resource endowments determine the success of the order of online entry strategy for incumbent retailers. This dissertation utilizes the dynamic capabilities approach to propose that the strategy of inter-channel channel coordination leads to higher performance when core, unique dynamic capabilities pertaining to e-commerce are developed in-house, as opposed to being outsourced. By posing and answering the research questions regarding the role of strategic decisions of order of online entry and channel coordination in enhancing long-term financial and operational performance, this dissertation contributes to the development of strategic theory in the nascent areas of electronic commerce and multi-channel retailing, provides further empirical support to resource-based theory of competitive advantage, and assists managers in formulating more informed strategic objectives for achieving multi-channel competitive advantage

Information Disclosure on a Chinese Social Media Platform

The nature of social media encourages people to contribute voluntarily to public web and inevitably, leaving a persistent and cumulative repository of personal information. Aware of the privacy risks, about one third of the Internet users in the United States have expressed concerns of their personal privacy. However, users are often cavalier in the protection of their own data profile. There is often a discrepancy between users’ intentions to protect privacy and their actual heavier. This behavior is often terms as “privacy paradox”. The privacy paradox might arise because users balance between risks and benefits of disclosing information on social media. Using the privacy calculus model as the theoretical background, the study examines how perceived risks and benefits affect information disclosure behavior on a Chinese social media site. In addition, the study investigates the antecedents of perceived benefits and risks as well as the effect of gender on information disclosure behavior. 420 valid responses were collected from a Chinese crowdsourcing website. Partial Least Squares (PLS), specifically SmartPLS 2.0, was used to assess the psychometric properties of the measurement model and to test the hypotheses. The study finds that perceived privacy risk is not significantly related to information disclosure (β=-0.01, p\u3e0.10). However, the relationship between perceived benefits and information disclosure is significant (β=0.18,

Mobile Application Adoption by Young Adults: A Social Network Perspective

The use of mobile applications, defined as small programs that run on a mobile device and perform tasks ranging from banking to gaming and web browsing, is exploding. Within the past two years, the industry has grown from essentially nothing to a $2 billion marketplace, but adoption rates are still on the rise. Using network theory, this study examines how the adoption of mobile apps among young consumers is influenced by others in their social network. The results suggest that the likelihood of adoption and usage of mobile apps increases with their use by the consumer\u27s strongest relationship partner. In addition, the authors find marginal support for the hypothesis that the adoption of mobile apps will be more strongly influenced by a consumer\u27s social contacts (friends, compared to family members), possibly due to their closer similarity to the consumer. Managerial and theoretical implications are discussed