Search Results for author: Sohini Upadhyay

Found 11 papers, 0 papers with code

Metric Elicitation; Moving from Theory to Practice

no code implementations7 Dec 2022 Safinah Ali, Sohini Upadhyay, Gaurush Hiranandani, Elena L. Glassman, Oluwasanmi Koyejo

Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting.


Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

no code implementations15 May 2022 Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H. Bach, Himabindu Lakkaraju

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.

Decision Making Fairness

Extending LIME for Business Process Automation

no code implementations9 Aug 2021 Sohini Upadhyay, Vatche Isahagian, Vinod Muthusamy, Yara Rizk

AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions.

What will it take to generate fairness-preserving explanations?

no code implementations24 Jun 2021 Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju

In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern.


Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis

no code implementations18 Jun 2021 Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju

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.

Counterfactual Explanation

Towards Robust and Reliable Algorithmic Recourse

no code implementations NeurIPS 2021 Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju

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.

Decision Making

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

no code implementations21 Feb 2021 Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju

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.

Online Semi-Supervised Learning with Bandit Feedback

no code implementations ICLR Workshop LLD 2019 Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni, DjallelBouneffouf

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.

Imputation Multi-Armed Bandits

Double-Linear Thompson Sampling for Context-Attentive Bandits

no code implementations15 Oct 2020 Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish

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.

Medical Diagnosis Thompson Sampling

Contextual Bandit with Missing Rewards

no code implementations13 Jul 2020 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 associated with each context-based decision may not always be observed("missing rewards").

A Bandit Approach to Posterior Dialog Orchestration Under a Budget

no code implementations22 Jun 2019 Sohini Upadhyay, Mayank Agarwal, Djallel Bounneffouf, Yasaman Khazaeni

Building multi-domain AI agents is a challenging task and an open problem in the area of AI.

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