3 accepted papers
Transformers have reshaped modern artificial intelligence, yet their theoretical foundations remain incomplete. This thesis investigates the approximation power and memory limitations of transformers. I combine tools from approximation theory and statistical learning theory to provide provable guara
We study the fundamental expressivity limits of transformer models by formalizing the notion of accessible sequences---those that a transformer can produce for some prompt---and characterizing how accessibility depends on prompt length and model parameters. Our analysis provides a theoretical explan…
Quantum state tomography, the task of learning an unknown quantum state, is a fundamental problem in quantum information. In standard settings, the complexity of this problem depends significantly on the type of quantum state that one is trying to learn, with pure states being substantially easier t…