https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Bay08.pdf

Speeded-Up Robust Features (SURF)

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

/pdf/maximizing-the-spread-of-influence-through-a-social-network-47y1ehuvwi.pdf

Maximizing the Spread of Influence through a Social Network

/pdf/tight-bounds-for-approximate-caratheodory-and-beyond-yey3h8flkw.pdf

Tight Bounds for Approximate Carathéodory and Beyond.

As a fundamental tool in modeling and analyzing social, and information networks, large-scale graph mining is an important component of any tool set for big data analysis. Processing graphs with hundreds of billions of edges is only possible via developing distributed algorithms under distributed graph mining frameworks such as MapReduce, Pregel, Gigraph, and alike. For these distributed algorithms to work well in practice, we need to take into account several metrics such as the number of rounds of computation and the communication complexity of each round. For example, given the popularity and ease-of-use of MapReduce framework, developing practical algorithms with good theoretical guarantees for basic graph algorithms is a problem of great importance.In this tutorial, we first discuss how to design and implement algorithms based on traditional MapReduce architecture. In this regard, we discuss various basic graph theoretic problems such as computing connected components, maximum matching, MST, counting triangle and overlapping or balanced clustering. We discuss a computation model for MapReduce and describe the sampling, filtering, local random walk, and core-set techniques to develop efficient algorithms in this framework. At the end, we explore the possibility of employing other distributed graph processing frameworks. In particular, we study the effect of augmenting MapReduce with a distributed hash table (DHT) service and also discuss the use of a new graph processing framework called ASYMP based on asynchronous message-passing method. In particular, we will show that using ASyMP, one can improve the CPU usage, and achieve significantly improved running time.

Distributed Graph Algorithmics: Theory and Practice

We study permutation betting markets, introduced by Chen, Fortnow, Nikolova, and Pennock [3]. For these markets, we consider subset bettings in which each trader can bet on a subset of candidates ending up in a subset of positions. We consider the revenue maximization problem for the auctioneer in two main frameworks: the risk-free revenue maximization (studied in [3]), and the probabilistic revenue maximization. We also explore the use of some certain knowledge or extra information about the possible outcomes of the market. We first show that finding the optimal revenue in the risk-free model for the subset betting problem is inapproximable. This resolves an open question posed by Chen et al. [3]. In order to identify solvable variants of the problem, we propose the singleton betting language which allows traders to bet an arbitrary value on one candidate for one position. For singleton bettings, we first provide a linear-time implementable necessary and sufficient condition for existence of a solution with positive revenue for any possible outcome. Furthermore, we develop an LP-based polynomial-time algorithm to find the optimum solution of this problem. In addition, we show how to extend this LP-based method to handle some extra information about the possible outcomes. Finally, we consider the revenue maximization problem in a probabilistic setting. For this variant, we observe that the problem of maximizing the expected revenue is polynomial-time solvable, but we show that maximizing the probability of achieving a pre-specified revenue is #P-Complete.

/pdf/permutation-betting-markets-singleton-betting-with-extra-4onndbtoij.pdf

Permutation betting markets: singleton betting with extra information

Constraints on agent's ability to pay play a major role in auction design for any setting where the magnitude of financial transactions is sufficiently large. Those constraints have been traditionally modeled in mechanism design as \emph{hard budget}, i.e., mechanism is not allowed to charge agents more than a certain amount. Yet, real auction systems (such as Google AdWords) allow more sophisticated constraints on agents' ability to pay, such as \emph{average budgets}. In this work, we investigate the design of Pareto optimal and incentive compatible auctions for agents with \emph{constrained quasi-linear utilities}, which captures more realistic models of liquidity constraints that the agents may have. Our result applies to a very general class of allocation constraints known as polymatroidal environments, encompassing many settings of interest such as multi-unit auctions, matching markets, video-on-demand and advertisement systems. 
Our design is based Ausubel's \emph{clinching framework}. Incentive compatibility and feasibility with respect to ability-to-pay constraints are direct consequences of the clinching framework. Pareto-optimality, on the other hand, is considerably more challenging, since the no-trade condition that characterizes it depends not only on whether agents have their budgets exhausted or not, but also on prices {at} which the goods are allocated. In order to get a handle on those prices, we introduce novel concepts of dropping prices and saturation. These concepts lead to our main structural result which is a characterization of the tight sets in the clinching auction outcome and its relation to dropping prices.

Clinching Auctions Beyond Hard Budget Constraints

We study revenue optimization in a repeated auction between a single seller anda single buyer. Traditionally, the design of repeated auctions requires strong modeling assumptions about the bidder behavior, such as it being myopic, infinite lookahead, or some specific form of learning behavior. Is it possible to design mechanisms which are simultaneously optimal against a multitude of possible buyer behaviors? We answer this question by designing a simple state-based mechanism that is simultaneously approximately optimal against a k -lookahead buyer for all k , a buyer who is a no-regret learner, and a buyer who is a policy-regret learner. Against each type of buyer our mechanism attains a constant fraction of the optimal revenue attainable against that type of buyer. We complement our positive results with almost tight impossibility results, showing that the revenue approximation tradeoffs achieved by our mechanism for different lookahead attitudes are near-optimal.

/pdf/robust-repeated-auctions-under-heterogeneous-buyer-behavior-1u6j23hpoy.pdf

Vahab Mirrokni

Papers

Tight Bounds for Approximate Carathéodory and Beyond.

Distributed Graph Algorithmics: Theory and Practice

Permutation betting markets: singleton betting with extra information

Clinching Auctions Beyond Hard Budget Constraints

Robust Repeated Auctions under Heterogeneous Buyer Behavior