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» Learning in the Limit with Adversarial Disturbances
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SIGMOD
2012
ACM
223views Database» more  SIGMOD 2012»
11 years 7 months ago
MaskIt: privately releasing user context streams for personalized mobile applications
The rise of smartphones equipped with various sensors has enabled personalization of various applications based on user contexts extracted from sensor readings. At the same time i...
Michaela Götz, Suman Nath, Johannes Gehrke
OSDI
2008
ACM
14 years 5 months ago
From Optimization to Regret Minimization and Back Again
Internet routing is mostly based on static information-it's dynamicity is limited to reacting to changes in topology. Adaptive performance-based routing decisions would not o...
Ioannis C. Avramopoulos, Jennifer Rexford, Robert ...
AGENTS
1999
Springer
13 years 9 months ago
Team-Partitioned, Opaque-Transition Reinforcement Learning
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Peter Stone, Manuela M. Veloso
EELC
2006
156views Languages» more  EELC 2006»
13 years 9 months ago
The Human Speechome Project
The Human Speechome Project is an effort to observe and computationally model the longitudinal course of language development for a single child at an unprecedented scale. The ide...
Deb Roy, Rupal Patel, Philip DeCamp, Rony Kubat, M...
DISCEX
2003
IEEE
13 years 10 months ago
ANON: An IP-Layer Anonymizing Infrastructure
This exhibition demonstrates an IP-layer anonymizing infrastructure, called ANON, which allows server addresses to be hidden from clients and vice versa. In providing address anon...
Chen-Mou Cheng, H. T. Kung, Koan-Sin Tan, Scott Br...