Answers.Body: <p>If you look into the top conferences on machine learning and neural networks, such as NeurIPS, ICLR, and ICML, you will find many papers related to neural networks and deep learning, given that these are still very hot/promising topics. However, occasionally, you will find accepted papers that do not involve neural networks. Here's a small list of them that I've found after a quick search.</p>
  <ul>
  <li><a href="https://icml.cc/virtual/2020/poster/6269" rel="nofollow noreferrer">Near-Tight Margin-Based Generalization Bounds for Support Vector Machines</a> (ICML, 2020, <a href="http://proceedings.mlr.press/v119/gronlund20a/gronlund20a.pdf" rel="nofollow noreferrer">pdf</a>) by Allan Grønlund et al.</li>
  <li><a href="https://icml.cc/virtual/2020/poster/6454" rel="nofollow noreferrer">Implicit Regularization of Random Feature Models</a> (ICML, 2020, <a href="http://proceedings.mlr.press/v119/jacot20a/jacot20a.pdf" rel="nofollow noreferrer">pdf</a>) by Arthur Jacot et al.</li>
  <li><a href="https://icml.cc/virtual/2020/poster/6102" rel="nofollow noreferrer">Quantum Expectation-Maximization for Gaussian mixture models</a> (ICML, 2020, <a href="http://proceedings.mlr.press/v119/kerenidis20a/kerenidis20a.pdf" rel="nofollow noreferrer">pdf</a>) by Alessandro Luongo et al.</li>
  <li><a href="https://icml.cc/virtual/2020/poster/5831" rel="nofollow noreferrer">From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model</a> (ICML, 2020, <a href="http://proceedings.mlr.press/v119/saha20b/saha20b.pdf" rel="nofollow noreferrer">pdf</a>) by Aadirupa Saha et al.</li>
  </ul>
  <p>So, yes, research in machine learning is not exclusively devoted to neural networks and deep learning. You can probably find more papers <a href="https://icml.cc/virtual/2020/papers.html?filter=keywords" rel="nofollow noreferrer">here</a> or in the proceedings of similar conferences or journals.</p>
  
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Answers.CreationDate: 2021-01-31T12:47:36.530Z
Answers.LastActivityDate: 2021-01-31T12:47:36.530Z
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Body: <p>So far, I have not been able to find many papers that do not involve neural networks, so I was hoping I can gain some insight here. Any references would be greatly appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-31T09:33:24.903Z
Comments.Text: Numenta and Vicarious are using techniques different from neural networks. Look into their publications.
Comments.UserDisplayName: Shimano
Tags: <neural-networks><machine-learning><reference-request><research>
Title: Are there any active areas of research in machine learning that do not involve neural networks at all?
UserDisplayName: mhdadk
ThreadText: # Are there any active areas of research in machine learning that do not involve neural networks at all?
  
  (Asked by: username_0 on 2021-01-31)
  
  So far, I have not been able to find many papers that do not involve neural networks, so I was hoping I can gain some insight here. Any references would be greatly appreciated.
  
  **Tags:** neural-networks, machine-learning, reference-request, research
  > **Comments:**
  > **username_1**: Numenta and Vicarious are using techniques different from neural networks. Look into their publications.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-01-31. Score: 3 | Accepted answer
  
  If you look into the top conferences on machine learning and neural networks, such as NeurIPS, ICLR, and ICML, you will find many papers related to neural networks and deep learning, given that these are still very hot/promising topics. However, occasionally, you will find accepted papers that do not involve neural networks. Here's a small list of them that I've found after a quick search.
  
  * [Near-Tight Margin-Based Generalization Bounds for Support Vector Machines](https://icml.cc/virtual/2020/poster/6269) (ICML, 2020, [pdf](http://proceedings.mlr.press/v119/gronlund20a/gronlund20a.pdf)) by Allan Grønlund et al.
  * [Implicit Regularization of Random Feature Models](https://icml.cc/virtual/2020/poster/6454) (ICML, 2020, [pdf](http://proceedings.mlr.press/v119/jacot20a/jacot20a.pdf)) by Arthur Jacot et al.
  * [Quantum Expectation-Maximization for Gaussian mixture models](https://icml.cc/virtual/2020/poster/6102) (ICML, 2020, [pdf](http://proceedings.mlr.press/v119/kerenidis20a/kerenidis20a.pdf)) by Alessandro Luongo et al.
  * [From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model](https://icml.cc/virtual/2020/poster/5831) (ICML, 2020, [pdf](http://proceedings.mlr.press/v119/saha20b/saha20b.pdf)) by Aadirupa Saha et al.
  
  So, yes, research in machine learning is not exclusively devoted to neural networks and deep learning. You can probably find more papers [here](https://icml.cc/virtual/2020/papers.html?filter=keywords) or in the proceedings of similar conferences or journals.
  
  
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