Welcome to ALT Highlights, a series of blog posts spotlighting various happenings at the recent conference ALT 2021, including plenary talks, tutorials, trends in learning theory, and more! To reach a broad audience, the series is disseminated as guest posts on different blogs in machine learning and theoretical computer science. This initiative is organized by the Learning Theory Alliance and is overseen by Gautam Kamath. All posts in ALT Highlights are indexed on the official Learning Theory Alliance blog.

This is the sixth and final post in the series, on trends in machine learning theory, written by Margalit Glasgow, Michal Moshkovitz, and Cyrus Rashtchian.

**Introduction**

Throughout the last few decades, we have witnessed unprecedented growth of machine learning. Originally a topic formalized by a small group of computer scientists, machine learning now impacts many areas: the physical sciences, medicine, commerce, finance, urban planning, and more. The rapid growth of machine learning can be partially attributed to the availability of large amounts of data and the development of powerful computing devices. Another important factor is that machine learning has foundations in many other fields, such as theoretical computer science, algorithms, applied mathematics, statistics, and optimization.

If machine learning is already mathematically rooted in many existing research areas, why do we need a field solely dedicated to learning theory? According to Daniel Hsu, “Learning theory serves (at least) two purposes: to help make sense of machine learning, and also to explore the capabilities and limitations of learning algorithms.” Besides finding innovative applications for existing tools, learning theorists also provide answers to long-standing problems and ask new fundamental questions.

Modern learning theory goes beyond classical statistical and computer science paradigms by:

- developing insights about specific computational models (e.g., neural networks)
- analyzing popular learning algorithms (e.g., stochastic gradient descent)
- taking into account data distributions (e.g., margin bounds or manifold assumptions)
- adding auxiliary goals (e.g., robustness or privacy), and
- rethinking how algorithms interact with and access data (e.g., online or reinforcement learning).

By digging deep into the basic questions, researchers generate new concepts and models that change the way we solve problems and help us understand emerging phenomena.

This article provides a brief overview of three key areas in machine learning theory: new learning paradigms, trustworthy machine learning, and reinforcement learning. We describe the main thrust of each of these areas, as well as point to a few papers from ALT 2021 (the 32nd International Conference on Algorithmic Learning Theory) that touch each of these topics. To share a broader view, we also asked experts in the areas to comment on the field and on their recent papers. Needless to say, this article only scratches the surface. At the end, we point to places to learn more about learning theory.

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