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Displaying 1-15 out of 15 total
Tracking Forecast Memories in stochastic decoders
Found in: Acoustics, Speech, and Signal Processing, IEEE International Conference on
By Saeed Sharifi Tehrani, Ali Naderi, Guy-Armand Kamendje, Shie Mannor, Warren J. Gross
Issue Date:April 2009
pp. 561-564
This paper proposes Tracking Forecast Memories (TFMs) as a novel method for implementing re-randomization and decorrelation of stochastic bit streams in stochastic channel decoders. We show that TFMs are able to achieve decoding performance similar to that...
Survey of Stochastic Computation on Factor Graphs
Found in: Multiple-Valued Logic, IEEE International Symposium on
By Saeed Sharifi Tehrani, Shie Mannor, Warren J. Gross
Issue Date:May 2007
pp. 54
Stochastic computation is a new alternative approach for iterative computation on factor graphs. In this approach, the information is represented by the statistics of the bit stream which results in simple high-speed hardware implementation of graph-based ...
Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Huan Xu,Constantine Caramanis,Shie Mannor
Issue Date:January 2012
pp. 187-193
We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We show that these two properties are fundamentally at odds with each other: A sparse algori...
Efficient Bidding in Dynamic Grid Markets
Found in: IEEE Transactions on Parallel and Distributed Systems
By Amir Danak, Shie Mannor
Issue Date:September 2011
pp. 1483-1496
We analyze rational strategies of users in a dynamic grid market. We consider efficient usage of the shared resources in modeling users' preference relations, an objective that prevents congestion and consequently the collapse of the grid system. A repeate...
Resource Allocation with Supply Adjustment in Distributed Computing Systems
Found in: Distributed Computing Systems, International Conference on
By Amir Danak, Shie Mannor
Issue Date:June 2010
pp. 498-506
We present two resource-allocation mechanisms for on-demand computing services in parallel and distributed systems, where users pay for their actual usage of the computational resources. We specialize our solution for allocation of grid resources which is ...
Detecting epidemics using highly noisy data
Found in: Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing (MobiHoc '13)
By Chris Milling, Constantine Caramanis, Sanjay Shakkottai, Shie Mannor
Issue Date:July 2013
pp. 177-186
From Cholera, AIDS/HIV, and Malaria, to rumors and viral video, understanding the causative network behind an epidemic's spread has repeatedly proven critical for managing the spread (controlling or encouraging, as the case may be). Our current approaches ...
A novel similarity measure for time series data with applications to gait and activity recognition
Found in: Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct (Ubicomp '10 Adjunct)
By Doina Precup, Jordan Frank, Shie Mannor
Issue Date:September 2010
pp. 407-408
In this abstract, we propose a novel approach to modeling time-series for the purpose of comparing segments of data in order to classify activities based on accelerometer sensor data. Our approach consists of producing an ensemble of simple classifiers tha...
Generative models for rapid information propagation
Found in: Proceedings of the First Workshop on Social Media Analytics (SOMA '10)
By Elad Yom-Tov, Kirill Dyagilev, Shie Mannor
Issue Date:July 2010
pp. 35-43
We consider the dynamics of rapid propagation of information in complex social networks focusing on mobile phone networks. We introduce two models for an information propagation process. The first model describes the temporal behavior of people which leads...
Piecewise-stationary bandit problems with side observations
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Jia Yuan Yu, Shie Mannor
Issue Date:June 2009
pp. 1-8
We consider a sequential decision problem where the rewards are generated by a piecewise-stationary distribution. However, the different reward distributions are unknown and may change at unknown instants. Our approach uses a limited number of side observa...
Reinforcement learning in the presence of rare events
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Doina Precup, Jordan Frank, Shie Mannor
Issue Date:July 2008
pp. 336-343
We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these events are sampled according to their natural probability of occurring, converg...
Automatic basis function construction for approximate dynamic programming and reinforcement learning
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Doina Precup, Philipp W. Keller, Shie Mannor
Issue Date:June 2006
pp. 449-456
We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results by Bertsekas and Castañon (1989) who proposed a method for automatically...
The cross entropy method for classification
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Dori Peleg, Reuven Rubinstein, Shie Mannor
Issue Date:August 2005
pp. 561-568
We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the "L0 norm") as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cr...
Reinforcement learning with Gaussian processes
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Ron Meir, Shie Mannor, Yaakov Engel
Issue Date:August 2005
pp. 201-208
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framework by addressing two pressing issues, which were not adequately treated in t...
Bias and variance in value function estimation
Found in: Twenty-first international conference on Machine learning (ICML '04)
By Duncan Simester, John N. Tsitsiklis, Peng Sun, Shie Mannor
Issue Date:July 2004
pp. 182-182
We consider the bias and variance of value function estimation that are caused by using an empirical model instead of the true model. We analyze these bias and variance for Markov processes from a classical (frequentist) statistical point of view, and in a...
Dynamic abstraction in reinforcement learning via clustering
Found in: Twenty-first international conference on Machine learning (ICML '04)
By Amit Hoze, Ishai Menache, Shie Mannor, Uri Klein
Issue Date:July 2004
pp. 182-182
We consider a graph theoretic approach for automatic construction of options in a dynamic environment. A map of the environment is generated on-line by the learning agent, representing the topological structure of the state transitions. A clustering algori...