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Combining labeled and unlabeled data with co-training
TLDR
A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Expand
Fast Planning Through Planning Graph Analysis
TLDR
A new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure the authors call a Planning Graph is introduced, and a new planner, Graphplan, is described that uses this paradigm. Expand
Variational Dropout and the Local Reparameterization Trick
TLDR
The Variational dropout method is proposed, a generalization of Gaussian dropout, but with a more flexibly parameterized posterior, often leading to better generalization in stochastic gradient variational Bayes. Expand
Selection of Relevant Features and Examples in Machine Learning
TLDR
This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area. Expand
Practical privacy: the SuLQ framework
TLDR
This work considers a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries, and modify the privacy analysis to real-valued functions f and arbitrary row types, greatly improving the bounds on noise required for privacy. Expand
A learning theory approach to non-interactive database privacy
TLDR
A new notion of data privacy is introduced, which is called distributional privacy, and it is shown that it is strictly stronger than the prevailing privacy notion, differential privacy. Expand
Noise-tolerant learning, the parity problem, and the statistical query model
TLDR
The algorithm runs in polynomial time for the case of parity functions that depend on only the first O(log n log log n) bits of input, which provides the first known instance of an efficient noise-tolerant algorithm for a concept class that is not learnable in the Statistical Query model of Kearns [1998]. Expand
Learning from Labeled and Unlabeled Data using Graph Mincuts
TLDR
An algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data is considered. Expand
Correlation Clustering
TLDR
This formulation is motivated from a document clustering problem in which one has a pairwise similarity function f learned from past data, and the goal is to partition the current set of documents in a way that correlates with f as much as possible; it can also be viewed as a kind of “agnostic learning” problem. Expand
Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges
TLDR
This work replaces CPLEX as the clearing algorithm of the Alliance for Paired Donation, one of the leading kidney exchanges, and presents the first algorithm capable of clearing these markets on a nationwide scale. Expand
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