UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorization.

This robotic pool cleaner can run on battery power or with a connected cable, but it's only effective at cleaning the pool when plugged into an AC outlet. Here's a curious concept from the new-to-us robotic pool cleaner manufacturer Bublue: The BuHybrid L6 is a robotic pool cleaner that can run via a plug-in electrical connection or via an internal battery, a hybrid design that makes more sense than it might seem at first, at least on paper. On the surface, the design has a lot in common with the Polaris VRX iQ and other power-corded robots. A small power box connects to standard wall power via a short cord. A separate, waterproof 49-foot-long cable then connects from the box to the 22-pound robot, attaching to its top via a large four-prong adapter with a screw-on sealing system that waterproofs the connection.

Two popular approaches for model compression are: pruning and quantization.

We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure.

BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research.

Contextual bandit is a class of online learning problems that can be viewed as a simple reinforcement learning problem without transition. For a completely understanding of contextual bandit problems, we refer the readers to the Chapter 4 of [Bubeck et al., 2012]. Here we include the main idea for completeness. In contextual bandit problems, the agent needs to find out the best action given some observed context (a.k.a the optimal policy in reinforcement learning). Formally, we define S as the context set and K as the number of action.