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

We study the price of anarchy of the ﬁrst-price auction in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of the ﬁrst-price auction is 1 / 2, and with both kinds of bidders, the price of anarchy degrades to about 0 . 457 (the precise number is given by an optimization). These results complement the recent result by Jin and Lu [2022] showing that the price of anarchy of the ﬁrst-price auction with traditional bidders only is 1 − 1 /e 2 . We further investigate a setting where the seller can utilize machine-learned advice to improve the eﬃciency of the auctions. There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about 0 . 457 to 1.

Efficiency of the First-Price Auction in the Autobidding World

We study revenue optimization in a repeated auction between a single seller and a 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.

Robust Repeated Auctions under Heterogeneous Buyer Behavior

Handling the lack of coordination while designing efficient algorithms in distributed systems has been a major topic of study in the past decade. Coordination mechanisms have been proposed as a tool to deal with the issue as well as lack of access to global information in settings such as distributed systems. In the context of resource allocation, a coordination mechanism is a set of local policies that assigns a cost to each strategy based on the available local information. For example, in machine scheduling, this cost only depends on the processing times of jobs assigned to the same machine. Although a great tool to study distributed algorithms in the presence of self-interested agents, coordination mechanisms have few deficiencies as an analysis tool for distributed game theoretic environments. For example, in many real-world settings, we do not know the exact processing time of a job before it finishes. Furthermore, in many settings, jobs arrive online, and have different release times. Motivated by these requirements, we propose dynamic coordination mechanisms, in which each job selects a machine by looking at the set of jobs currently on each machine and it can change its decision over time. In other words, jobs can dynamically choose the (best machine dynamically. We study scheduling and resource allocation problems in this framework.

Dynamic Coordination Games

Designing distributed and scalable algorithms to improve network connectivity is a central topic in peer-to-peer networks. In this paper we focus on the following well-known problem: given an n-node d-regular network for d = Ω(log n), we want to design a decentralized, local algorithm that transforms the graph into one that has good connectivity properties (low diameter, expansion, etc.) without affecting the sparsity of the graph. To this end, Mahlmann and Schindelhauer introduced the random "flip" transformation, where in each time step, a random pair of vertices that have an edge decide to 'swap a neighbor'. They conjectured that performing O(nd) such flips at random would convert any connected d-regular graph into a d-regular expander graph, with high probability. However, the best known upper bound for the number of steps is roughly O(n17d23), obtained via a delicate Markov chain comparison argument.Our main result is to prove that a natural instantiation of the random flip produces an expander in at most O(n2d2[EQUATION] n) steps, with high probability. Our argument uses a potential-function analysis based on the matrix exponential, together with the recent beautiful results on the higher-order Cheeger inequality of graphs. We also show that our technique can be used to analyze another well-studied random process known as the 'random switch', and show that it produces an expander in O(nd) steps with high probability.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611974331.ch19

Expanders via local edge flips

The problem of column subset selection has recently attracted a large body of research, with feature selection serving as one obvious and important application. Among the techniques that have been applied to solve this problem, the greedy algorithm has been shown to be quite effective in practice. However, theoretical guarantees on its performance have not been explored thoroughly, especially in a distributed setting. In this paper, we study the greedy algorithm for the column subset selection problem from a theoretical and empirical perspective and show its effectiveness in a distributed setting. In particular, we provide an improved approximation guarantee for the greedy algorithm which we show is tight up to a constant factor, and present the first distributed implementation with provable approximation factors. We use the idea of randomized composable core-sets, developed recently in the context of submodular maximization. Finally, we validate the effectiveness of this distributed algorithm via an empirical study.

Vahab Mirrokni

Papers

Efficiency of the First-Price Auction in the Autobidding World

Robust Repeated Auctions under Heterogeneous Buyer Behavior

Dynamic Coordination Games

Expanders via local edge flips

Greedy Column Subset Selection: New Bounds and Distributed Algorithms