COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Computer Architecture: A Quantitative Approach
Computer Architecture: A Quantitative Approach
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Measuring Benchmark Similarity Using Inherent Program Characteristics
IEEE Transactions on Computers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Efficiently exploring architectural design spaces via predictive modeling
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Using Machine Learning to Guide Architecture Simulation
The Journal of Machine Learning Research
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
Using PredictiveModeling for Cross-Program Design Space Exploration in Multicore Systems
PACT '07 Proceedings of the 16th International Conference on Parallel Architecture and Compilation Techniques
Informed Microarchitecture Design Space Exploration Using Workload Dynamics
Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
On multi-view active learning and the combination with semi-supervised learning
Proceedings of the 25th international conference on Machine learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
CPR: Composable performance regression for scalable multiprocessor models
Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
A hybrid generative/discriminative approach to semi-supervised classifier design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Chip Multiprocessor Design Space Exploration through Statistical Simulation
IEEE Transactions on Computers
Semi-supervised learning by disagreement
Knowledge and Information Systems
An Empirical Architecture-Centric Approach to Microarchitectural Design Space Exploration
IEEE Transactions on Computers
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Ever-increasing design complexity and advances of technology impose great challenges on the design of modern microprocessors. One such challenge is to determine promising microprocessor configurations to meet specific design constraints, which is called Design Space Exploration (DSE). In the computer architecture community, supervised learning techniques have been applied to DSE to build regression models for predicting the qualities of design configurations. For supervised learning, however, considerable simulation costs are required for attaining the labeled design configurations. Given limited resources, it is difficult to achieve high accuracy. In this article, inspired by recent advances in semisupervised learning and active learning, we propose the COAL approach which can exploit unlabeled design configurations to significantly improve the models. Empirical study demonstrates that COAL significantly outperforms a state-of-the-art DSE technique by reducing mean squared error by 35% to 95%, and thus, promising architectures can be attained more efficiently.