Latest Machine Learning Research From Deepmind Explores The Connection Between Gradient-Based Meta-Learning And Convex Optimization

The term “meta-learning” refers to the process by which a learner adjusts to a new challenge by modifying an algorithm with known parameters. The algorithm’s parameters are meta-learned by measuring the learner’s progress and adjusting accordingly. There is a lot of empirical support for this framework. It has been utilized in various contexts, including meta-learning, how to explore reinforcement learning (RL), the discovery of black-box loss functions, algorithms, and even complete training protocols. 

Even so, nothing is understood about the theoretical features of meta-learning. The intricate relationship between the learner and the meta-learner is the main reason behind this. The learner’s challenge is optimizing the parameters of a stochastic objective to minimize the predicted loss.

Optimism (a forecast of the future gradient) in meta-learning is possible using the Bootstrapped Meta-Gradients technique, as explored by a DeepMind research team in their recent publication Optimistic Meta-Gradients.

Most previous research has focused on meta-optimization as an online problem, and convergence guarantees have been derived from that perspective. Unlike other works, this one views meta-learning as a non-linear change to traditional optimization. As such, a meta-learner should tune its meta-parameters for maximum update efficiency.

The researchers first analyze meta-learning with modern convex optimization techniques, during which they validate the increased rates of convergence and consider the optimism associated with meta-learning in the convex situation. After that, they present the first evidence of convergence for the BMG technique and demonstrate how it may be used to communicate optimism in meta-learning.

By contrasting momentum with meta-learned step size, the team discovers that incorporating a non-linearity update algorithm can increase the convergence rate. In order to verify that meta-learning the scale vector reliably accelerates convergence, the team also compares it to an AdaGrad sub-gradient approach for stochastic optimization. Finally, the team contrasts optimistic meta-learning with traditional meta-learning without optimism and finds that the latter is significantly more likely to lead to acceleration.

Overall, this work verifies optimism’s function in speeding up meta-learning and presents new insights into the relationship between convex optimization and meta-learning. The results of this study imply that introducing hope into the meta-learning process is crucial if acceleration is to be realized. When the meta-learner is given cues, optimism comes naturally from a classical optimization perspective. A major boost in speed can be achieved if clues accurately predict the learning dynamics. Their findings give the first rigorous proof of convergence for BMG and a general condition under which optimism in BMG delivers rapid learning as targets in BMG and clues in optimistic online learning commute.

Check out the Paper. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our Reddit Page, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.