Computational learning theory

See also: Statistical learning theory

Theory of machine learning

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Part of a series on

Machine learning
and data mining

Paradigms

* Supervised learning
* Unsupervised learning
* Semi-supervised learning
* Self-supervised learning
* Reinforcement learning
* Meta-learning
* Online learning
* Batch learning
* Curriculum learning
* Rule-based learning
* Neuro-symbolic AI
* Neuromorphic engineering
* Quantum machine learning

Problems

* Classification
* Generative modeling
* Regression
* Clustering
* Dimensionality reduction
* Density estimation
* Anomaly detection
* Data cleaning
* AutoML
* Association rules
* Semantic analysis
* Structured prediction
* Feature engineering
* Feature learning
* Learning to rank
* Grammar induction
* Ontology learning
* Multimodal learning

Supervised learning
(classification • regression)

* Apprenticeship learning
* Decision trees
* Ensembles
* Bagging
* Boosting
* Random forest
* k-NN
* Linear regression
* Naive Bayes
* Artificial neural networks
* Logistic regression
* Perceptron
* Relevance vector machine (RVM)
* Support vector machine (SVM)

Clustering

* BIRCH
* CURE
* Hierarchical
* k-means
* Fuzzy
* Expectation–maximization (EM)

DBSCAN
* OPTICS
* Mean shift

Dimensionality reduction

* Factor analysis
* CCA
* ICA
* LDA
* NMF
* PCA
* PGD
* t-SNE
* SDL

Structured prediction

* Graphical models
* Bayes net
* Conditional random field
* Hidden Markov

Anomaly detection

* RANSAC
* k-NN
* Local outlier factor
* Isolation forest

Neural networks

* Autoencoder
* Deep learning
* Feedforward neural network
* Recurrent neural network
* LSTM
* GRU
* ESN
* reservoir computing
* Boltzmann machine
* Restricted
* GAN
* Diffusion model
* SOM
* Convolutional neural network
* U-Net
* LeNet
* AlexNet
* DeepDream
* Neural field
* Neural radiance field
* Physics-informed neural networks
* Transformer
* Vision
* Mamba
* Spiking neural network
* Memtransistor
* Electrochemical RAM (ECRAM)

Reinforcement learning

* Q-learning
* Policy gradient
* SARSA
* Temporal difference (TD)
* Multi-agent
* Self-play

Learning with humans

* Active learning
* Crowdsourcing
* Human-in-the-loop
* Mechanistic interpretability
* RLHF

Model diagnostics

* Coefficient of determination
* Confusion matrix
* Learning curve
* ROC curve

Mathematical foundations

* Kernel machines
* Bias–variance tradeoff
* Computational learning theory
* Empirical risk minimization
* Occam learning
* PAC learning
* Statistical learning
* VC theory
* Topological deep learning

Journals and conferences

* AAAI
* ECML PKDD
* NeurIPS
* ICML
* ICLR
* IJCAI
* ML
* JMLR

Related articles

* Glossary of artificial intelligence
* List of datasets for machine-learning research
* List of datasets in computer vision and image processing
* Outline of machine learning

* v
* t
* e

In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.[1]

## Overview

[edit]

Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible. The algorithm takes these previously labeled samples and uses them to induce a classifier. This classifier is a function that assigns labels to samples, including samples that have not been seen previously by the algorithm. The goal of the supervised learning algorithm is to optimize some measure of performance such as minimizing the number of mistakes made on new samples.

In addition to performance bounds, computational learning theory studies the time complexity and feasibility of learning.[citation needed] In computational learning theory, a computation is considered feasible if it can be done in polynomial time.[citation needed] There are two kinds of time complexity results:

* Positive results - Showing that a certain class of functions is learnable in polynomial time.
* Negative results - Showing that certain classes cannot be learned in polynomial time.[2]

Negative results often rely on commonly believed, but yet unproven assumptions,[citation needed] such as:

* Computational complexity – P ≠ NP (the P versus NP problem);
* Cryptographic – One-way functions exist.

There are several different approaches to computational learning theory based on making different assumptions about the inference principles used to generalise from limited data. This includes different definitions of probability (see frequency probability, Bayesian probability) and different assumptions on the generation of samples.[citation needed] The different approaches include:

* Exact learning, proposed by Dana Angluin[citation needed];
* Probably approximately correct learning (PAC learning), proposed by Leslie Valiant;[3]
* VC theory, proposed by Vladimir Vapnik and Alexey Chervonenkis;[4]
* Inductive inference as developed by Ray Solomonoff;[5][6]
* Algorithmic learning theory, from the work of E. Mark Gold;[7]
* Online machine learning, from the work of Nick Littlestone[citation needed].

While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms. For example, PAC theory inspired boosting, VC theory led to support vector machines, and Bayesian inference led to belief networks.

## See also

[edit]

* Error tolerance (PAC learning)
* Grammar induction
* Information theory
* Occam learning
* Stability (learning theory)

## References

[edit]

1. ^ "ACL - Association for Computational Learning".
2. ^ Kearns, Michael; Vazirani, Umesh (August 15, 1994). An Introduction to Computational Learning Theory. MIT Press. ISBN 978-0262111935.
3. ^ Valiant, Leslie (1984). "A Theory of the Learnable" (PDF). Communications of the ACM. 27 (11): 1134–1142\. doi:10.1145/1968.1972. S2CID 12837541. Archived from the original (PDF) on 2019-05-17. Retrieved 2022-11-24.
4. ^ Vapnik, V.; Chervonenkis, A. (1971). "On the uniform convergence of relative frequencies of events to their probabilities" (PDF). Theory of Probability and Its Applications. 16 (2): 264–280\. doi:10.1137/1116025.
5. ^ Solomonoff, Ray (March 1964). "A Formal Theory of Inductive Inference Part 1". Information and Control. 7 (1): 1–22\. doi:10.1016/S0019-9958(64)90223-2.
6. ^ Solomonoff, Ray (1964). "A Formal Theory of Inductive Inference Part 2". Information and Control. 7 (2): 224–254\. doi:10.1016/S0019-9958(64)90131-7.
7. ^ Gold, E. Mark (1967). "Language identification in the limit" (PDF). Information and Control. 10 (5): 447–474\. doi:10.1016/S0019-9958(67)91165-5.

## Further reading

[edit]

A description of some of these publications is given at important publications in machine learning.

### Surveys

[edit]

* Angluin, D. 1992. Computational learning theory: Survey and selected bibliography. In Proceedings of the Twenty-Fourth Annual ACM Symposium on Theory of Computing (May 1992), pages 351–369. http://portal.acm.org/citation.cfm?id=129712.129746
* D. Haussler. Probably approximately correct learning. In AAAI-90 Proceedings of the Eight National Conference on Artificial Intelligence, Boston, MA, pages 1101–1108. American Association for Artificial Intelligence, 1990. http://citeseer.ist.psu.edu/haussler90probably.html

### Feature selection

[edit]

* A. Dhagat and L. Hellerstein, "PAC learning with irrelevant attributes", in 'Proceedings of the IEEE Symp. on Foundation of Computer Science', 1994. http://citeseer.ist.psu.edu/dhagat94pac.html

### Optimal O notation learning

[edit]

* Oded Goldreich, Dana Ron. On universal learning algorithms. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.2224

### Negative results

[edit]

* M. Kearns and Leslie Valiant. 1989. Cryptographic limitations on learning boolean formulae and finite automata. In Proceedings of the 21st Annual ACM Symposium on Theory of Computing, pages 433–444, New York. ACM. http://citeseer.ist.psu.edu/kearns89cryptographic.html[dead link]

### Error tolerance

[edit]

* Michael Kearns and Ming Li. Learning in the presence of malicious errors. SIAM Journal on Computing, 22(4):807–837, August 1993. http://citeseer.ist.psu.edu/kearns93learning.html
* Kearns, M. (1993). Efficient noise-tolerant learning from statistical queries. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, pages 392–401. http://citeseer.ist.psu.edu/kearns93efficient.html

### Equivalence

[edit]

* D.Haussler, M.Kearns, N.Littlestone and M. Warmuth, Equivalence of models for polynomial learnability, Proc. 1st ACM Workshop on Computational Learning Theory, (1988) 42-55.
* Pitt, L.; Warmuth, M. K. (1990). "Prediction-Preserving Reducibility". Journal of Computer and System Sciences. 41 (3): 430–467\. doi:10.1016/0022-0000(90)90028-J.

## External links

[edit]

* Basics of Bayesian inference

* v
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Differentiable computing

General|

* Differentiable programming
* Information geometry
* Statistical manifold
* Automatic differentiation
* Neuromorphic computing
* Pattern recognition
* Ricci calculus
* Computational learning theory
* Inductive bias

Hardware|

* IPU
* TPU
* VPU
* Memristor
* SpiNNaker

Software libraries|

* TensorFlow
* PyTorch
* Keras
* scikit-learn
* Theano
* JAX
* Flux.jl
* MindSpore

* Portals
* Computer programming
* Technology

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