Akarsh Kumar

These is research I am currently working on or will do in future.

Evolving/Meta-Learning Symbolically Extrapolative Generalization in NNs

My NSF GRFP Proposal

  • Symbolism was the first frontier of AI until connectionism with NNs took over.

  • Training NNs with SGD exploits statistical regularities in the training data to effectively solve the task.

  • However, the NN never learns the true structure in the data as humans do.

  • Human brains emerge symbolism from connectionism to achieve System 2 reasoning and understanding of data.

  • Can we meta-learn a learning algorithm to emerge symbols in an NN to understand the true structure in the data?

Contact me if you want to collaborate or see my written detailed formalization of this project.

Evolving/Meta-Learning Optimal Sparsity and Weight Sharing in NNs

  • Currently, neural architecture search builds NNs from hard coded building blocks.

  • Can we instead learn the optimal building blocks themselves?

  • All NN building blocks (Conv, Attention, MLP-Mixer, etc.) carefully balance weight sparsity and weight sharing.

  • In this project, I formalize finding the best sparsity and weight sharing as an optimization problem.

  • This project will give insights into the next generation of NN architectures.

Contact me if you want to collaborate or see my written detailed formalization of this project.

Go-Explore with Tabula Rasa Intelligent Exploration

Here is an idea allowing the agent to intelligently explore based on its past exploration successes.

  • We run normal Go-Explore early on and collect the archive of cells.

  • This gives a dataset where we can learn “what kinds of cells were promising/productive and led to new promising cells/rewards” and “what kinds of cells were unproductive and useless”.

  • Train a classifier to predict the productiveness of given cells.

  • Intrinsically motivate the agent to get to states/cells which the classifier thinks are productive.

  • The classifier may learn what is promising/productive on a global scale, and this global information may be used to achieve intelligent local exploration of the agent!

  • Also, by intelligently exploring locally, we will ignore many useless states, and thus our future archive will be smaller, making this algorithm scalable to large state/cell spaces.

Contact me if you want to collaborate or see my written detailed formalization of this project.

A Goal Switching Approach to Multi-Task/Objective Learning.

  • Parameterize multi-task/objective problem as a grid of tasks (each cell is a task/problem).

  • Run MAP-Elites as you optimize solutions for specific tasks but also goal switch between tasks.

  • Can find a pseudo-optimal curriculum for each of the tasks.

  • If multi-task problem is really a multi-objective problem, it may find better pareto optimal solutions.

Contact me if you want to collaborate or see my written detailed formalization of this project.

Evolving/Meta-Learning the Optimal Optimization Algorithm

  • SGD is a powerful optimization algorithm, and almost always beats EAs in differentiable problems.

  • However, QD has shown us that EAs with diversity is nessesary for to solve some deceptive problems.

  • Can we evolve/learn the optimal optimization algorithm?

  • Specifically, our search space should contain simple SGD, simple GAs (truncation selection), etc.

  • Will our final evolutionary controller be naturally QD and outperform SGD?

Contact me if you want to collaborate or see my written detailed formalization of this project.

Two Man SGD

  • SGD keeps one point and follows its gradient.

  • What if we had two points and their gradients and created a clever crossover mechanism?

  • This new optimization algorithm has strictly more information than SGD and thus should be able to strictly outperform SGD.

  • This point-gradient crossover could be used in a GA as well.

  • I have developed a crossover mechanism that outperforms SGD in early parts of NN training.

Contact me if you want to collaborate or see my written detailed formalization of this project.

Taming POET for Automatic Curriculum Learning

  • POET has shown us the importance of goal switching and QD populations in RL environments.

  • However, POET is Open-Ended (i.e. has no goal but to build complexity)

  • Can we tame POET to keep its Open-Endedness to explore early on, but later exploit its findings to create a curriculum for a target distribution of tasks?

Contact me if you want to collaborate or see my written detailed formalization of this project.

Meta-Learning a Neuroevolution Crossover Mechanism

  • Crossover in neuroevolution is hard because of the competing conventions problem.

  • However, two different NNs which have learned different useful representations may be combined into a single NN which takes the best of both NNs.

  • Can we meta-learn a crossover mechanism NNs?

Contact me if you want to collaborate or see my written detailed formalization of this project.