Now showing items 1-10 of 280
Learning Solutions of Similar Linear Programming Problems using Boosting Trees
In many optimization problems, similar linear programming (LP) problems occur in the nodes of the branch and bound trees that are used to solve integer (mixed or pure, deterministic or stochastic) programming problems. ...
From primal templates to invariant recognition
We can immediately recognize novel objects seen only once before -- in different positions on the retina and at different scales (distances). Is this ability hardwired by our genes or learned during development -- and ...
Conservative-Bayesian Mechanism Design
Classical Bayesian mechanism design is "centralized," that is, the designer is assumed to know the distribution D from which the players' type profile has been drawn. We instead investigate a very "decentralized" Bayesian ...
CryptDB: A Practical Encrypted Relational DBMS
CryptDB is a DBMS that provides provable and practical privacy in the face of a compromised database server or curious database administrators. CryptDB works by executing SQL queries over encrypted data. At its core are ...
A File Location, Replication, and Distribution System for Network Information to Aid Network Management
This thesis demonstrates and evaluates the design, architecture, and implementation of a file location, replication, and distribution system built with the objective of managing information in an Internet network. The ...
Generalization and Properties of the Neural Response
Hierarchical learning algorithms have enjoyed tremendous growth in recent years, with many new algorithms being proposed and applied to a wide range of applications. However, despite the apparent success of hierarchical ...
Mechanism Design With Approximate Player Types
We investigate mechanism design when the players do not exactly know their types, but have instead only partial information about them.
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after ...
Compositional Policy Priors
This paper describes a probabilistic framework for incorporating structured inductive biases into reinforcement learning. These inductive biases arise from policy priors, probability distributions over optimal policies. ...
Verifying Quantitative Reliability of Programs That Execute on Unreliable Hardware
Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and recovery from soft errors is ...