Distributionally Robust Transfer Learning: A Survey

In this book, we will learn about online convex optimization, a subfield of machine learning that deals with optimizing convex objectives in real-time. Think of it like a recipe for cooking – you want the dish to turn out just right, but you can’t afford to pause or go back and adjust the ingredients mid-cook. Online optimization is all about making decisions without knowing the future, which makes it challenging but also exciting!
We will start by understanding the basics of convex optimization, which is like solving a puzzle where the pieces are connected by lines. Then we’ll dive into online optimization and how it can be used in various applications such as finance, healthcare, and more. We’ll explore different algorithms for solving these problems, including stochastic gradient descent and its variations.
One of the key challenges in online optimization is dealing with uncertainty. Think of it like trying to hit a moving target – you need to adjust your aim continuously to account for the target’s motion. In this book, we’ll discuss different ways to model and handle uncertainty, such as using probability distributions or robust optimization techniques.
Another important aspect of online optimization is balancing exploration and exploitation. Exploration means trying new things to learn more about the environment, while exploitation means taking advantage of what you already know to achieve your goals. Finding the right balance between these two is crucial for successful optimization.
Finally, we’ll touch on some of the open problems in online convex optimization and discuss future research directions. It’s like trying to solve a puzzle with missing pieces – there are still many gaps to fill in before we can fully understand this fascinating field.
Throughout the book, we’ll use simple examples and engaging metaphors to help you understand complex concepts. Think of it like following a recipe for your favorite dish – with each step, you’ll get closer to the final result, but you won’t know the whole picture until you’re done! So buckle up, and let’s start exploring the exciting world of online convex optimization together!