Stem pac$ (all stems)

20 papers:

ICML-c2-2014-BrunskillL #learning

PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.

ICML-c2-2014-PentinaL #bound #learning

A PAC-Bayesian bound for Lifelong Learning (AP, CHL), pp. 991–999.

ICML-c2-2014-ZhouCL #crowdsourcing #identification #multi

Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing (YZ, XC, JL), pp. 217–225.

ICML-c3-2013-GermainHLM #adaptation #approach #classification #linear

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers (PG, AH, FL, EM), pp. 738–746.

ICML-2012-KalyanakrishnanTAS #multi #probability #set

PAC Subset Selection in Stochastic Multi-armed Bandits (SK, AT, PA, PS), p. 34.

ICML-2012-MorvantKR #bound #classification #matrix #multi

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification (EM, SK, LR), p. 158.

ICML-2011-GermainLLMS #approach #kernel

A PAC-Bayes Sample-compression Approach to Kernel Methods (PG, AL, FL, MM, SS), pp. 297–304.

ICML-2011-RoyLM #bound #polynomial #source code

From PAC-Bayes Bounds to Quadratic Programs for Majority Votes (JFR, FL, MM), pp. 649–656.

CIKM-2010-BonifatiWL #distributed #named #peer-to-peer #privacy #social

SPac: a distributed, peer-to-peer, secure and privacy-aware social space (AB, WHW, RL), pp. 1953–1954.

ECIR-2010-CoxZFH #approximate #correctness #query #using

Improving Query Correctness Using Centralized Probably Approximately Correct (PAC) Search (IJC, JZ, RF, LKH), pp. 265–280.

ICML-2009-GermainLLM #classification #learning #linear

PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.

STOC-2006-Feldman #approximate #learning #logic #query

Hardness of approximate two-level logic minimization and PAC learning with membership queries (VF), pp. 363–372.

ICML-2006-StrehlLWLL #learning

PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.

ICML-2005-LavioletteM #bound #classification

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers (FL, MM), pp. 481–488.

TOOLS-EUROPE-2000-DuvalP #design pattern #interactive #kernel #object-oriented #using

Using the PAC-Amodeus Model and Design Patterns to Make Interactive an Existing Object-Oriented Kernel (TD, FP), pp. 407–418.

TOOLS-PACIFIC-1999-Hussey #design pattern #specification #using

Using Design Patterns to Derive PAC Architectures from Object-Z Specifications (AH), pp. 40–51.

CHI-1997-CalvaryCN #architecture #design

From Single-User Architectural Design to PAC*: a Generic Software Architecture Model for CSCW (GC, JC, LN), pp. 242–249.

ICML-1997-Decatur #classification #induction #learning

PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.

ICML-1995-AuerHM #theory and practice

Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (PA, RCH, WM), pp. 21–29.

ML-1989-Chrisman #bias

Evaluating Bias During Pac-Learning (LC), pp. 469–471.