The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

Learning and Robustness With Applications To Mechanism Design

The design of economic mechanisms, especially auctions, is an increasingly important part of the modern economy. A particularly important property for a mechanism is strategyproofness -- the mechanism must be robust to strategic manipulations so that the participants in the mechanism have no incentive to lie. Yet in the important case when the mechanism designer's goal is to maximize their own revenue, the design of optimal strategyproof mechanisms has proved immensely difficult, with very little progress after decades of research. Recently, to escape this impasse, a number of works have parameterized auction mechanisms as deep neural networks, and used gradient descent to successfully learn approximately optimal and approximately strategyproof mechanisms. We present several improvements on these techniques. When an auction mechanism is represented as a neural network mapping bids from outcomes, strategyproofness can be thought of as a type of adversarial robustness. Making this connection explicit, we design a modified architecture for learning auctions which is amenable to integer-programming-based certification techniques from the adversarial robustness literature. Existing baselines are empirically strategyproof, but with no way to be certain how strong that guarantee really is. By contrast, we are able to provide perfectly tight bounds on the degree to which strategyproofness is violated at any given point. Existing neural networks for auctions learn to maximize revenue subject to strategyproofness. Yet in many auctions, fairness is also an important concern -- in particular, fairness with respect to the items in the auction, which may represent, for instance, ad impressions for different protected demographic groups. With our new architecture, ProportionNet, we impose fairness constraints in addition to the strategyproofness constraints, and find approximately fair, approximately optimal mechanisms which outperform baselines. With PreferenceNet, we extend this approach to notions of fairness that are learned from possibly vague human preferences. Existing network architectures can represent additive and unit-demand auctions, but are unable to imposing more complex exactly-k constraints on the allocations made to the bidders. By using the Sinkhorn algorithm to add differentiable matching constraints, we produce a network which can represent valid allocations in such settings. Finally, we present a new auction architecture which is a differentiable version of affine maximizer auctions, modified to offer lotteries in order to potentially increase revenue. This architecture is always perfectly strategyproof (avoiding the Lagrangian-based constrained optimization of RegretNet) -- to achieve this goal, however, we need to accept that we cannot in general represent the optimal auction

Personalized Treatment Selection via Product Partition Models with Covariates

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model-based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the Normalized Generalized Gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model-based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.Comment: 31 pages, 7 figure

Behavioral Economics & Machine Learning Expanding the Field Through a New Lens

In this thesis, I investigate central questions in behavioral economics as well as law and economics. I examine well-studied problems through a new methodological lens. The aim is to generate new insights and thus point behavioral scientists to novel analytical tools. To this end, I show how machine learning may be used to build new theories by reducing complexity in experimental economic data. Moreover, I use natural language processing to show how supervised learning can enable the scientific community to expand limited datasets. I also investigate the normative impact of the use of such tools in social science research or decision-making as well as their deficiencies

Understanding what consumers value about brands : an extension of the value hierarchy framework

This dissertation uses data from 60 personal, in-depth interviews to test a conceptual framework (called the Extended Value Hierarchy) for understanding the potential of branding to create consumer value. The overall purposes of this research are (1) to clarify and extend concepts presented in the literature related to specific aspects of consumer value that may be affected by branding and (2) to empirically compare the potential of two specific branding strategies (private label and national branding) to contribute to consumer value.The study evaluates the merit of the Extended Value Hierarchy framework and compares consumer thoughts about national and private label brands in the same productcategory. The specific context examined is the most important aspects of the brand (chosen by the consumer) that are considered during the choice situation (e.g., decision to purchase a specific brand).Participants in the study are loyal users of specified private label and national brands.Two different product categories were examined, which provided an opportunity for replication of findings. This dissertation also demonstrates a methodology for collecting consumer value information as it relates to branding. The development and use of this interviewing methodology, which is a variation of the laddering technique, are discussed.The findings from this dissertation provide support for the constructs that differentiate the Extended Value Hierarchy framework from the traditional value hierarchy. The general structure of the traditional value hierarchy framework is also supported.Several differences in terms of the content of what is valued between national and private label brand buyers were indicated. Specifically, the findings suggest the importance of price (in relation to brand performance on key product attributes) in creating value for private label buyers. In addition, the findings related to national brand buyers suggest that at least one segment of consumers finds value in the symbolic aspects of the brand (created in part by marketing communications).Very limited statistical differences in the overall structure of thoughts expressed by national and private label brand buyers were evident. This is in contrast to some of the marketing literature related to national and private label brands. This finding suggests opportunities for further clarification within marketing thought regarding the ways in which national and private buyers may differ in terms of the value dimensions they associate with a preferred brand