I am a PhD student in the Computer Science department at the University of California San Diego advised by Prof. Sanjoy Dasgupta and Prof. Kamalika Chaudhuri. Previously, I was a research fellow in the Machine Teaching Group at Max Planck Institute for Software Systems, where I had the great fortune to be advised by Dr. Adish Singla.
My journey into computer science started at Chennai Mathematical Institute (CMI, India), where I completed my BSc in Mathematics and Computer Science (2013-2016) and MSc in Computer Science (2016-2018) under the supervision of Prof. K Venkata Subrahmanyam.
I am broadly interested in the theoretical aspects of machine learning. More specifically, I’m interested in statistical machine learning, algorithms, interactive learning, optimization, and theory of deep learning. I’m enthusiastic to explore and apply ideas from probability theory, analysis, differential geometry, and statistics to understand the computational and statistical efficiency of learning methods, and the extent to which machines can learn from data.
Contact: (username id) akk002 at ucsd dot edu
Publications and Preprints
Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm
Akash Kumar, Yuxin Chen, Adish Singla.
Accepted in The 35th Conference on Neural Information Processing Systems (NeurIPS’21), 2021
[Proc 2021], [Openreview]
The Teaching Dimension of Kernel Perceptrons
Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen.
Accepted in The 24th International Conference on Artificial Intelligence and Statistics (AISTATS’21), 2021
[ArXiv 2021], [Proc 2021]
Teaching via Best-case Counterexamples (UCSD AI Seminar)
Improved Certified Adversarial Lower Bound Using Adaptive Relaxations
Ongoing project on adversarial deep learning.
Natural Proofs Vs Derandomization
Project report completed as part of the Advanced Complexity course at Chennai Mathematical Institute.