Cost-Effective Incentive Allocation via Structured Counterfactual Inference

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets

RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions

Efficient advert assignment

We develop a framework for the analysis of large-scale ad auctions where adverts are assigned over a continuum of search types. For this pay-per-click market, we provide an efficient mechanism that maximizes social welfare. In particular, we show that the social welfare optimization can be solved in separate optimizations conducted on the time scales relevant to the search platform and advertisers. Here, on each search occurrence, the platform solves an assignment problem and, on a slower time scale, each advertiser submits a bid that matches its demand for click-throughs with supply. Importantly, knowledge of global parameters, such as the distribution of search terms, is not required when separating the problem in this way. Exploiting the information asymmetry between the platform and advertiser, we describe a simple mechanism that incentivizes truthful bidding, has a unique Nash equilibrium that is socially optimal, and thus implements our decomposition. Further, we consider models where advertisers adapt their bids smoothly over time and prove convergence to the solution that maximizes social welfare. Finally, we describe several extensions that illustrate the flexibility and tractability of our framework.The research of Neil Walton was funded by the VENI research programme, which is financed by the Netherlands Organisation for Scientific Research (NWO)

An integrated model for warehouse and inventory planning

We propose a tactical model which integrates the replenishment decision in inventory management, the allocation of products to warehousing systems and the assignment of products to storage locations in warehousing management. The purpose of this article is to analyse the value of integrating warehouse and inventory decisions. This is achieved by proposing two methods for solving this tactical integrated model which differ in the level of integration of the inventory and warehousing decisions. A computational analysis is performed on a real world database and using multiple scenarios differing by the warehouse capacity limits. Our observation is that the total cost of the inventory and warehousing systems can be reduced drastically by taking into account the warehouse capacity restrictions in the inventory planning decisions, in an aggregate way. Moreover additional inventory and warehouse savings can be achieved by using more sophisticated integration methods for inventory and warehousing decisions

Competitive and Cooperative Approaches to the Balancing Market in Distribution Grids

The electrical grid has been changing in the last decade due to the presence, at the distribution level, of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid\u2019s system opens new challenges for the activities of companies, and the development of new energy management systems, models, and methods. In order to face this revolution, new market structures are being defined as well as new technologies and optimization and control algorithms for the management of distributed resources and the coordination of local users to contribute to active power reserve and ancillary services. One of the main problems for an electricity market operator that also owns the distribution grid is to avoid congestions and maximize the quality of the service provided. The thesis concerns the development and application of new methods for the optimization of network systems (with multi-decision makers) with particular attention to the case of power distribution networks This Ph.D. thesis aims to address the current lack of properly defined market structures for the determination of balancing services in distribution networks. As a first study, to be able to handle the power flow equation in a computationally better way, a new convex relaxation has been proposed. Thereafter, two opposite types of market structure have been developed: competitive and cooperative. The first structure presents a two-tier mechanism where the market operator is in a predominant position compared to other market players. Vice versa in the cooperative mechanism (solved through distributed optimization techniques ) all actors are on the same level and work together for social welfare. The main methodological novelties of the proposed work are to solve complex problems with formally correct and computationally efficient techniques

Variational Fair Clustering

We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. Fundamentally different from the existing combinatorial and spectral solutions, our variational multi-term approach enables to control the trade-off levels between the fairness and clustering objectives. We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker's inequality. Our tight upper bound can be jointly optimized with various clustering objectives, while yielding a scalable solution, with convergence guarantee. Interestingly, at each iteration, it performs an independent update for each assignment variable. Therefore, it can be easily distributed for large-scale datasets. This scalability is important as it enables to explore different trade-off levels between the fairness and clustering objectives. Unlike spectral relaxation, our formulation does not require computing its eigenvalue decomposition. We report comprehensive evaluations and comparisons with state-of-the-art methods over various fair-clustering benchmarks, which show that our variational formulation can yield highly competitive solutions in terms of fairness and clustering objectives.Comment: Accepted to be published in AAAI 2021. The Code is available at: https://github.com/imtiazziko/Variational-Fair-Clusterin

A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.

The revolution in information technology and the explosion in the use of computing devices in people\u27s everyday activities has forever changed the perspective of the data mining and machine learning fields. The enormous amounts of easily accessible, information rich data is pushing the data analysis community in general towards a shift of paradigm. In the new paradigm, data comes in the form a stream of billions of records received everyday. The dynamic nature of the data and its sheer size makes it impossible to use the traditional notion of offline learning where the whole data is accessible at any time point. Moreover, no amount of human resources is enough to get expert feedback on the data. In this work we have developed a unified optimization based learning framework that approaches many of the challenges mentioned earlier. Specifically, we developed a Never-Ending Learning framework which combines incremental/online, semi-supervised, and active learning under a unified optimization framework. The established framework is based on the class of submodular optimization methods. At the core of this work we provide a novel formulation of the Semi-Supervised Support Vector Machines (S3VM) in terms of submodular set functions. The new formulation overcomes the non-convexity issues of the S3VM and provides a state of the art solution that is orders of magnitude faster than the cutting edge algorithms in the literature. Next, we provide a stream summarization technique via exemplar selection. This technique makes it possible to keep a fixed size exemplar representation of a data stream that can be used by any label propagation based semi-supervised learning technique. The compact data steam representation allows a wide range of algorithms to be extended to incremental/online learning scenario. Under the same optimization framework, we provide an active learning algorithm that constitute the feedback between the learning machine and an oracle. Finally, the developed Never-Ending Learning framework is essentially transductive in nature. Therefore, our last contribution is an inductive incremental learning technique for incremental training of SVM using the properties of local kernels. We demonstrated through this work the importance and wide applicability of the proposed methodologies

Competition and Yield Optimization in Ad Exchanges

Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. The presence of such channels presents a host of new strategic and tactical questions for publishers. How should the supply of impressions be divided between bilateral contracts and exchanges? How should auctions be designed to maximize profits? What is the role of user information and to what extent should it be disclosed? In this thesis, we develop a novel framework to address some of these questions. We first study how publishers should allocate their inventory in the presence of these new markets when traditional reservation-based ad contracts are available. We then study the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions. Traditionally, an advertiser would buy display ad placements by negotiating deals directly with a publisher, and signing an agreement, called a guaranteed contract. These deals usually take the form of a specific number of ad impressions reserved over a particular time horizon. In light of the growing market of Ad Exchanges, publishers face new challenges in choosing between the allocation of contract-based reservation ads and spot market ads. In this setting, the publisher should take into account the tradeoff between short-term revenue from an Ad Exchange and the long-term impact of assigning high quality impressions to the reservations (typically measured by the click-through rate). In the first part of this thesis, we formalize this combined optimization problem as a stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange bids, where the exchange bids are assumed to be exogenous and independent of the decisions of the publishers. We prove asymptotic optimality of this policy in terms of any arbitrary trade-off between quality of delivered reservation ads and revenue from the exchange, and provide a bound for its convergence rate to the optimal policy. We also give experimental results on data derived from real publisher inventory, showing that our policy can achieve any Pareto-optimal point on the quality vs. revenue curve. In the second part of this thesis, we relax the assumption of exogenous bids in the Ad Exchange and study in more detail the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions. Typically, advertisers join these markets with a pre-specified budget and participate in multiple second-price auctions over the length of a campaign. We introduce the novel notion of a Fluid Mean Field Equilibrium (FMFE) to study the dynamic bidding strategies of budget-constrained advertisers in these repeated auctions. This concept is based on a mean field approximation to relax the advertisers' informational requirements, together with a fluid approximation to handle the complex dynamics of the advertisers' control problems. Notably, we are able to derive a closed-form characterization of FMFE, which we use to study the auction design problem from the publisher's perspective focusing on three design decisions: (1) the reserve price; (2) the supply of impressions to the Exchange versus an alternative channel such as bilateral contracts; and (3) the disclosure of viewers' information. Our results provide novel insights with regard to key auction design decisions that publishers face in these markets. In the third part of this thesis, we justify the use of the FMFE as an equilibrium concept in this setting by proving that the FMFE provides a good approximation to the rational behavior of agents in large markets. To do so, we consider a sequence of scaled systems with increasing market size;. In this regime we show that, when all advertisers implement the FMFE strategy, the relative profit obtained from any unilateral deviation that keeps track of all available information in the market becomes negligible as the scale of the market increases. Hence, a FMFE strategy indeed becomes a best response in large markets