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Found 11 papers, 0 papers with code
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
Building multi-domain AI agents is a challenging task and an open problem in the area of AI.
Contextual Bandit with Missing Rewards
We consider a novel variant of the contextual bandit problem (i. e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associated with each context-based decision may not always be observed("missing rewards").
Double-Linear Thompson Sampling for Context-Attentive Bandits
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe.
Online Semi-Supervised Learning with Bandit Feedback
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits, motivated by several applications including clini-cal trials and ad recommendations.
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.
Towards Robust and Reliable Algorithmic Recourse
To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts.
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice.
What will it take to generate fairness-preserving explanations?
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern.
Extending LIME for Business Process Automation
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions.
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
We then leverage these properties to propose a novel evaluation framework which can quantitatively measure disparities in the quality of explanations output by state-of-the-art methods.
Metric Elicitation; Moving from Theory to Practice
Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting.
