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Found 10 papers, 1 papers with code
Gaussian Imagination in Bandit Learning
We study the performance of an agent that attains a bounded information ratio with respect to a bandit environment with a Gaussian prior distribution and a Gaussian likelihood function when applied instead to a Bernoulli bandit.
Treatment Effects in Market Equilibrium
When randomized trials are run in a marketplace equilibriated by prices, interference arises.
Learning and Information in Stochastic Networks and Queues
We review the role of information and learning in the stability and optimization of queueing systems.
Hierarchical Causal Bandit
Causal bandit is a nascent learning model where an agent sequentially experiments in a causal network of variables, in order to identify the reward-maximizing intervention.
Learner-Private Convex Optimization
Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function.
A Bit Better? Quantifying Information for Bandit Learning
The information ratio offers an approach to assessing the efficacy with which an agent balances between exploration and exploitation.
Query Complexity of Bayesian Private Learning
How many queries are necessary and sufficient in order for the learner to accurately estimate the target, while simultaneously concealing the target from the adversary?
Optimal query complexity for private sequential learning against eavesdropping
We study the query complexity of a learner-private sequential learning problem, motivated by the privacy and security concerns due to eavesdropping that arise in practical applications such as pricing and Federated Learning.
Reinforcement with Fading Memories
We focus on a family of decision rules where the agent makes a new choice by randomly selecting an action with a probability approximately proportional to the amount of past rewards associated with each action in her memory.
Private Sequential Learning
We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning.
