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Learning from Labeled and Unlabeled Data using Graph Mincuts
TLDR
We propose an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data. Expand
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Multi-parameter mechanism design and sequential posted pricing
TLDR
We consider the classical mathematical economics problem of Bayesian optimal mechanism design where a principal aims to optimize expected revenue when allocating resources to self-interested agents with preferences drawn from a known distribution. Expand
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Approximation algorithms for orienteering and discounted-reward TSP
TLDR
We give the first constant-factor approximation algorithm for the rooted orienteering problem, as well as a new problem that we call the Discounted-Reward TSP, motivated by robot navigation. Expand
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Algorithmic pricing via virtual valuations
TLDR
Algorithmic pricing is the computational problem that sellers (e.g., supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Expand
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Approximation algorithms for deadline-TSP and vehicle routing with time-windows
TLDR
We give an approximation algorithm for the Deadline-TSP problem and a bicriteria approximation for the Vehicle Routing with Time-Window problem. Expand
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The power of randomness in bayesian optimal mechanism design
TLDR
We investigate the power of randomness in the context of a fundamental Bayesian optimal mechanism design problem - a single seller aims to maximize expected revenue by allocating multiple kinds of resources to "unit-demand" agents with preferences drawn from a known distribution. Expand
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Correlation clustering
  • N. Bansal, A. Blum, S. Chawla
  • Mathematics, Computer Science
  • The 43rd Annual IEEE Symposium on Foundations of…
  • 16 November 2002
TLDR
We consider the following clustering problem: we have a complete graph on n vertices (items), where each edge is labeled either + or - depending on whether a and /spl upsi/ have been deemed to be similar or different. Expand
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Toward Privacy in Public Databases
We initiate a theoretical study of the census problem. Informally, in a census individual respondents give private information to a trusted party (the census bureau), who publishes a sanitizedExpand
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Near Optimal LP Rounding Algorithm for CorrelationClustering on Complete and Complete k-partite Graphs
TLDR
We give new rounding schemes for the standard linear programming relaxation of the correlation clustering problem, achieving approximation factors almost matching the integrality gaps: For complete graphs our approximation is 2.5. Expand
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On the Hardness of Approximating Multicut and Sparsest-Cut
TLDR
We show that the MULTICUT, SPARSEST-CUT, and MIN-2CNF/spl equiv/DELETION problems are NP-hard to approximate within every constant factor, assuming the unique games conjecture of Khot. Expand
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