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Found 17 papers, 4 papers with code
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
Mechanical ventilation is a critical life-support intervention that uses a machine to deliver controlled air and oxygen to a patient's lungs, assisting or replacing spontaneous breathing.
Tensor Completion via Integer Optimization
The main challenge with the tensor completion problem is a fundamental tension between computation power and the information-theoretic sample complexity rate.
Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards
On top of the agent's learning, the principal trains a parallel algorithm and faces a trade-off between consistently estimating the agent's unknown rewards and maximizing their own utility by offering adaptive incentives to lead the agent.
Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents
Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games within a multi-armed bandit (MAB) framework, where: the principal gives a different incentive for each bandit arm, the agent picks a bandit arm to maximize its own expected reward plus incentive, and the principal observes which arm is chosen and receives a reward (different than that of the agent) for the chosen arm.
Accelerated Nonnegative Tensor Completion via Integer Programming
A recent approach, based on integer programming, resolves this tension for nonnegative tensor completion.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Nonnegative Tensor Completion via Integer Optimization
Unlike matrix completion, tensor completion does not have an algorithm that is known to achieve the information-theoretic sample complexity rate.
Protecting Anonymous Speech: A Generative Adversarial Network Methodology for Removing Stylistic Indicators in Text
With Internet users constantly leaving a trail of text, whether through blogs, emails, or social media posts, the ability to write and protest anonymously is being eroded because artificial intelligence, when given a sample of previous work, can match text with its author out of hundreds of possible candidates.
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching
This paper proposes a novel method based on Deep Reinforcement Learning for developing dynamic scheduling policies through interaction with simulated stochastic manufacturing systems.
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function
A significant theoretical challenge in the nonlinear setting is that there is no explicit characterization of an optimal controller for a given set of cost and system parameters.
Covariance-Robust Dynamic Watermarking
Attack detection and mitigation strategies for cyberphysical systems (CPS) are an active area of research, and researchers have developed a variety of attack-detection tools such as dynamic watermarking.
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.
Average Margin Regularization for Classifiers
We motivate this regularization by a novel generalization bound that shows a tradeoff in classifier accuracy between maximizing its margin and average margin.
Convex Formulations for Fair Principal Component Analysis
We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.
Spectral Algorithms for Computing Fair Support Vector Machines
Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i. e., age, gender, or race).
Non-Stationary Bandits with Habituation and Recovery Dynamics
Many settings involve sequential decision-making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward of each action is initially unknown.
Low-Rank Approximation and Completion of Positive Tensors
Among the consequences is that best rank-1 approximations of positive tensors can be computed in polynomial time.
