• Publications
  • Influence
On the approximability of influence in social networks
  • N. Chen
  • Mathematics, Computer Science
  • SODA '08
  • 20 January 2008
TLDR
In this paper, we study the spread of influence through a social network, in a model initially studied by Kempe, Kleinberg and Tardos [14, 15]: We are given a graph modeling a network, where each node v has a (fixed) threshold tv, such that the node will adopt a new product if tv of its neighbors adopt it. Expand
  • 345
  • 61
  • PDF
AR-miner: mining informative reviews for developers from mobile app marketplace
TLDR
We present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. Expand
  • 352
  • 48
  • PDF
Improving Adversarial Robustness via Promoting Ensemble Diversity
TLDR
This paper presents a new method that explores the interaction among individual networks to improve robustness for ensemble models. Expand
  • 103
  • 20
  • PDF
On the approximability of budget feasible mechanisms
TLDR
We show a lower bound of 1 + √2 for the approximation ratio of deterministic mechanisms and 2 for randomized mechanisms for knapsack, as well as the general monotone submodular functions. Expand
  • 88
  • 14
  • PDF
STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution
  • N. Chen, S. Kim
  • Computer Science
  • IEEE Transactions on Software Engineering
  • 1 February 2015
TLDR
This paper proposes a Stack-Trace-based Automatic crash Reproduction framework (STAR), which automatically reproduces crashes using crash stack traces. Expand
  • 42
  • 13
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Bayesian inference with posterior regularization and applications to infinite latent SVMs
TLDR
We present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation. Expand
  • 126
  • 11
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Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
TLDR
In this paper, we present a large-margin learning framework to discover a predictive latent subspace representation shared by multiple views. Expand
  • 111
  • 11
  • PDF
Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis
TLDR
This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Expand
  • 83
  • 10
  • PDF
Budget feasible mechanism design: from prior-free to bayesian
TLDR
Budget feasible mechanism design studies procurement combinatorial auctions in which the sellers have private costs to produce items, and the buyer (auctioneer) aims to maximize a social valuation function on subsets of items, under the budget constraint. Expand
  • 52
  • 9
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Approximating Matches Made in Heaven
TLDR
We study a stochastic matching problem in which we have a random graph G given by a node set V and probabilities p (i ,j ) on all pairs i ,j *** V representing the probability that edge (i,j ) exists. Expand
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  • 8
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