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.
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 (ICML, 2020, pdf) by Allan Grønlund et al.
- Implicit Regularization of Random Feature Models (ICML, 2020, pdf) by Arthur Jacot et al.
- Quantum Expectation-Maximization for Gaussian mixture models (ICML, 2020, pdf) by Alessandro Luongo et al.
- From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model (ICML, 2020, 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 or in the proceedings of similar conferences or journals.