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» Proving Time Bounds for Randomized Distributed Algorithms
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ACL
1994
14 years 11 months ago
A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...
Partha Niyogi, Robert C. Berwick
CVPR
2007
IEEE
15 years 11 months ago
Two thresholds are better than one
The concept of the Bayesian optimal single threshold is a well established and widely used classification technique. In this paper, we prove that when spatial cohesion is assumed ...
Tao Zhang, Terrance E. Boult, R. C. Johnson
ICFP
2010
ACM
14 years 10 months ago
Distance makes the types grow stronger: a calculus for differential privacy
We want assurances that sensitive information will not be disclosed when aggregate data derived from a database is published. Differential privacy offers a strong statistical guar...
Jason Reed, Benjamin C. Pierce
84
Voted
ML
2006
ACM
14 years 9 months ago
Universal parameter optimisation in games based on SPSA
Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use ge...
Levente Kocsis, Csaba Szepesvári
STOC
2009
ACM
150views Algorithms» more  STOC 2009»
15 years 10 months ago
Integrality gaps for Sherali-Adams relaxations
We prove strong lower bounds on integrality gaps of Sherali?Adams relaxations for MAX CUT, Vertex Cover, Sparsest Cut and other problems. Our constructions show gaps for Sherali?A...
Moses Charikar, Konstantin Makarychev, Yury Makary...