I am about to write my bachelor thesis about non-parametric density estimation, especially kernel density estimators and their application in classification. As I am quite new to looking for academic literature, I am having a hard time finding the most important and modern papers, or other resources, and would be glad if someone could give me a hint. Right now I am mainly working with older works (especially by Silverman and Devroye).
Kernel Smoothing by Wand & Jones was my main book reference, you can follow the literature from their bibliography. It's very well written, thin and technical just enough
Adding to Aksakal, there are two more classical books that are worth recommending:
Silverman, B. W. (1986). Density estimation for statistics and data analysis. CRC press.
Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.
Density estimation is obviously a vastly studied topic. If you care for results in density estimation with provable guarantees on sample complexity and time of computation, while having approximation up to arbitrarily low error, I strongly recommend you peruse literature from the Theoretical Computer Science community.
A starting point could be this presentation by Diakonikolas, and then, consequently this survey again by Diakonikolas. You will find folklore and more recent results on non-parametric density estimation, for discrete and continuous distributions. The references in the presentation and survey all contain pointers to a wealth of literature (with clear discussion on applications) on density estimation, studied by various communities.