We consider a new model for the testing of untrusted quantum devices, consisting of a single
polynomial time bounded quantum device interacting with a classical polynomial time …

Concentration inequalities have been the subject of exciting developments during the last
two decades, and have been intensively studied and used as a powerful tool in various …

We develop a class of exponential bounds for the probability that a martingale sequence
crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful …

This chapter is devoted to concentration inequalities for martingales such as Azuma-
Hoeffding, Freedman, and De la Pena inequalities. Several extensions will also be provided …

We introduce an algorithm to do backpropagation on a spiking network. Our network is"
spiking" in the sense that our neurons accumulate their activation into a potential over time …

The paper is devoted to establishing some general exponential inequalities for
supermartingales. The inequalities improve or generalize many exponential inequalities of …

A commonly used learning rule is to approximately minimize the\emphaverage loss over the
training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing …

The benefits of using crossover in crossing fitness gaps have been studied extensively in
evolutionary computation. Recent runtime results show that majority-vote crossover is …

We study online meta-learning with bandit feedback, with the goal of improving performance
across multiple tasks if they are similar according to some natural similarity measure. As the …

A primary challenge in large language model (LLM) development is their onerous pre-
training cost. Typically, such pre-training involves optimizing a self-supervised objective …