Integrating neurons into digital systems may enable performance infeasible with silicon
alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive …

A comprehensive and cutting-edge introduction to the foundations and modern applications
of learning theory. Research has exploded in the field of machine learning resulting in …

We study bandit model selection in stochastic environments. Our approach relies on a
master algorithm that selects between candidate base algorithms. We develop a master …

Multi-armed bandit has been well-known for its efficiency in online decision-making in terms
of minimizing the loss of the participants' welfare during experiments (ie, the regret). In …

We present an algorithm for multiarmed bandits that achieves almost optimal performance in
both stochastic and adversarial regimes without prior knowledge about the nature of the …

The data used to pretrain large language models has a decisive impact on a model's
downstream performance, which has led to a large body of work on data selection methods …

Vehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as
many vehicle data collected and perceived by various on-board sensors contain sensitive …

Few-shot learning is valuable in many real-world applications, but learning a generalizable
model without overfitting to the few labeled datapoints is challenging. In this work, we focus …

This work addresses the coexistence problem for radar networks. Specifically, we model a
network of cooperative, independent, and non-communicating radar nodes which must …

Bandit and reinforcement learning (RL) problems can often be framed as optimization
problems where the goal is to maximize average performance while having access only to …