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Yoav Freund
Typical Stability
An Efficient Boosting Algorithm for Combining Preferences Raj
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Theory and Algorithms for Modern Problems in Machine Learning and an Analysis of Markets
40Th ACM Symposium on Theory of Computing (STOC 2008) Saturday
Game Theory, Alive Anna R
Online Rank Aggregation
The Boosting Apporach to Machine Learning
Venue Analytics: a Simple Alternative to Citation-Based Metrics
Machine Learning Classification Over Encrypted Data
Testing Closeness of Discrete Distributions
Computational Limitations in Robust Classification and Win-Win Results
Calibrating Noise to Variance in Adaptive Data Analysis
Towards Understanding the Interaction Between Learning Algorithms and Humans a Dissertation Submitted to the Institute for Compu
Contents U U U
DROP: a Workload-Aware Optimizer for Dimensionality Reduction
Novel Frameworks for Mining Heterogeneous and Dynamic
Computa癢nal Thinking
Top View
Cryptographically Private Support Vector Machines⋆
Ronitt Rubinfeld
Challenges in Computational Lower Bounds
PDF We Write As P (Λ)) to Average Over the Exponential Supermartingales in This Section, Proving the Following Theorem
1 This Issues Column!
Efficient Learning from Faulty Data
Return of Organization Exempt from Income
New Directions in Bandit Learning: Singularities and Random Walk Feedback by Tianyu Wang
Co-Ranking Authors and Documents in a Heterogeneous Network∗
Efficient, Noise-Tolerant, and Private Learning Via Boosting
Foundations of Machine Learning Ranking
Behavioral Shaping for Geometric Concepts
Computational Thinking
P R E S S Proceedings of the Twenty-Ninth Annual Acm
Association for Computing Machinery 2 Penn Plaza, Suite 701, New York
A Short Introduction to Boosting
Analyzing and Modeling Groups
Theory Generation for Security Protocols
A Brief Introduction to Boosting