In this paper, we study the stability and generalization properties of penalized empirical-risk minimization algorithms. We propose a set of properties of the penalty term that is sufficient to ensure uniform ?-stability: ...

Single chip processors with thousands of cores will be available in the next ten years and clouds of multicore processors afford the operating system designer thousands of cores today. Constructing operating systems for ...

In this report we investigate distributed computation in dynamic networks in which the network topology changes from round to round. We consider a worst-case model in which the communication links for each round are chosen ...

The currently prevailing equilibrium-based approach to mechanism design suffers from a plurality of fundamental problems, and new conceptual frameworks are needed to solve or sufficiently alleviate them. In this paper, we ...

This paper introduces the open-source Graphite distributed parallel multicore simulator infrastructure. Graphite is designed from the ground up for exploration of future multicore processors containing dozens, hundreds, ...

As multicore processors become increasingly prevalent, system complexity is skyrocketing. The advent of the asymmetric multicore compounds this -- it is no longer practical for an average programmer to balance the system ...

We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. ...

In this paper, we present a generative model for co-clustering and develop algorithms based on the mean field approximation for the corresponding modeling problem. These algorithms can be viewed as generalizations of the ...

I propose a shift in the foundations of computation. Practically all ideas of general-purpose computation today are founded either on execution of sequences of atomic instructions, i.e., assembly languages, or on evaluation ...

In this paper we propose a general framework to characterize and solve the optimization problems underlying a large class of sparsity based regularization algorithms. More precisely, we study the minimization of learning ...

To find interesting, personally relevant web content, we often rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means ...

In these notes we focus on the concept of Shannon entropy in an attempt to provide a systematic way of assessing the discrimination properties of the neural response, and quantifying the role played by the number of layers ...

The past four decades of research in visual neuroscience has generated a large and disparate body of literature on the role of attention [Itti et al., 2005]. Although several models have been developed to describe specific ...

The human visual system can recognize several thousand object categories irrespective of their position and size. This combination of selectivity and invariance is built up gradually across several stages of visual processing. ...

Online, forward-search techniques have demonstrated promising results for solving problems in partially observable environments. These techniques depend on the ability to efficiently search and evaluate the set of beliefs ...

Decentralized systems, such as distributed hash tables, are subject to the Sybil attack, in which an adversary creates many false identities to increase its influence. This paper proposes a routing protocol for a distributed ...

A progress report describing the application of policy gradient and policy search by dynamic programming methods to an aircraft collision avoidance problem inspired by the requirements of next-generation TCAS.

Non-deterministic LISP can be used to describe a search problem without specifying the method used to solve the problem. We show that SCHEMER, a non-deterministic dialect of SCHEME, can support dependency-directed backtracking ...

This code and data is publicly listed code for the LOLCAT Method developed by Sagar Indurkhya and Jacob Beal, in the paper: "Reaction factoring and bipartite update graphs accelerate the Gillespie algorithm for large-scale ...