• Publications
  • Influence
Axiomatic Attribution for Deep Networks
TLDR
We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. Expand
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Revenue maximization with a single sample
TLDR
We design and analyze approximately revenue-maximizing auctions in general single-parameter settings. Expand
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Online bipartite matching with random arrivals: an approach based on strongly factor-revealing LPs
TLDR
We study the ranking algorithm in the random arrivals model, and show that it has a competitive ratio of at least 0.696, beating the 1-1/e ≈ 0.632 barrier in the adversarial model. Expand
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Mechanism design via correlation gap
  • Qiqi Yan
  • Mathematics, Computer Science
  • SODA '11
  • 11 August 2010
TLDR
For revenue and welfare maximization in single-dimensional Bayesian settings, Chawla et al. (STOC10) recently showed that sequential posted-price mechanisms (SPMs), though simple in form, can perform surprisingly well compared to the optimal mechanisms. Expand
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Revenue maximization with a single sample
This paper pursues auctions that are prior-independent. The goal is to design an auction such that, whatever the underlying valuation distribution, its expected revenue is almost as large as that ofExpand
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Lower Bounds for Complementation of omega-Automata Via the Full Automata Technique
  • Qiqi Yan
  • Computer Science, Mathematics
  • Log. Methods Comput. Sci.
  • 8 February 2008
TLDR
In this paper, we first introduce a lower bound technique for the state complexity of transformations of automata. Expand
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Gradients of Counterfactuals
TLDR
We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. Expand
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How Important Is a Neuron?
TLDR
The problem of attributing a deep network's prediction to its \emph{input/base} features is well-studied. Expand
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From convex optimization to randomized mechanisms: toward optimal combinatorial auctions
TLDR
We design an expected polynomial time, truthful in expectation, (1-1/e)-approximation mechanism for welfare maximization in a fundamental class of combinatorial auctions with heterogeneous goods and restricted valuations. Expand
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Lower Bounds for Complementation of ω-Automata Via the Full Automata Technique
In this paper, we first introduce a new lower bound technique for the state complexity of transformations of automata. Namely we suggest considering the class of full automata in lower boundExpand
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