55 citations · 138 across the 10 of their papers we have counts for
11 papers
Metric Elicitation; Moving from Theory to Practice
Safinah Ali, Sohini Upadhyay, Gaurush Hiranandani +2
Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy…
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
Jessica Dai, Sohini Upadhyay, Ulrich Aivodji +2
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting…
Extending LIME for Business Process Automation
Sohini Upadhyay, Vatche Isahagian, Vinod Muthusamy +1
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions. Busin…
What will it take to generate fairness-preserving explanations?
Jessica Dai, Sohini Upadhyay, Stephen H. Bach +1
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern. However, the link between the fairness of the bl…
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi +2
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explan…
Towards Robust and Reliable Algorithmic Recourse
Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post hoc techniques which provide rec…
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal +3
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 techniq…
Online Semi-Supervised Learning with Bandit Feedback
Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal +2
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…
Double-Linear Thompson Sampling for Context-Attentive Bandits
Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay +2
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 s…
Contextual Bandit with Missing Rewards
Djallel Bouneffouf, Sohini Upadhyay, Yasaman Khazaeni
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 associ…
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
Sohini Upadhyay, Mayank Agarwal, Djallel Bounneffouf +1
Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orchestrate multiple independently trained…