A Bandit Approach to Posterior Dialog Orchestration Under a Budget. - Sohini Upadhyay; Mayank Agarwal; Djallel Bouneffouf 001; Yasaman Khazaeni (CoRR, 2019)

TraceHub - A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets. - Shubham Agarwal 002; Christian Muise; Mayank Agarwal; Sohini Upadhyay; Zilu Tang; Zhongshen Zeng; Yasaman Khazaeni (AAAI, 2020)

Contextual Bandit with Missing Rewards. - Djallel Bouneffouf 001; Sohini Upadhyay; Yasaman Khazaeni (CoRR, 2020)

Online Semi-Supervised Learning with Bandit Feedback. - Sohini Upadhyay; Mikhail Yurochkin; Mayank Agarwal; Yasaman Khazaeni; Djallel Bouneffouf 001 (CoRR, 2020)

Toward Skills Dialog Orchestration with Online Learning. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Mayank Agarwal; Yasaman Khazaeni; Irina Rish (ICASSP, 2021)

Double-Linear Thompson Sampling for Context-Attentive Bandits. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Yasaman Khazaeni; Irina Rish (ICASSP, 2021)

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. - Sushant Agarwal; Shahin Jabbari; Chirag Agarwal; Sohini Upadhyay; Steven Wu 001; Himabindu Lakkaraju (ICML, 2021)

Toward Optimal Solution for the Context-Attentive Bandit Problem. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Irina Rish; Yasaman Khazaeni (IJCAI, 2021)

Towards Robust and Reliable Algorithmic Recourse. - Sohini Upadhyay; Shalmali Joshi; Himabindu Lakkaraju (NeurIPS, 2021)

On the Connections between Counterfactual Explanations and Adversarial Examples. - Martin Pawelczyk; Shalmali Joshi; Chirag Agarwal; Sohini Upadhyay; Himabindu Lakkaraju (CoRR, 2021)

What will it take to generate fairness-preserving explanations? - Jessica Dai; Sohini Upadhyay; Stephen H. Bach; Himabindu Lakkaraju (CoRR, 2021)

Extending LIME for Business Process Automation. - Sohini Upadhyay; Vatche Isahagian; Vinod Muthusamy; Yara Rizk (CoRR, 2021)

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. - Jessica Dai; Sohini Upadhyay; Ulrich Aïvodji; Stephen H. Bach; Himabindu Lakkaraju (AIES, 2022)

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. - Martin Pawelczyk; Chirag Agarwal; Shalmali Joshi; Sohini Upadhyay; Himabindu Lakkaraju (AISTATS, 2022)

Linear Upper Confident Bound with Missing Reward: Online Learning with Less Data. - Djallel Bouneffouf 001; Sohini Upadhyay; Yasaman Khazaeni (IJCNN, 2022)

Metric Elicitation; Moving from Theory to Practice. - Safinah Ali; Sohini Upadhyay; Gaurush Hiranandani; Elena L. Glassman; Oluwasanmi Koyejo (CoRR, 2022)

Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services. - Naveena Karusala; Sohini Upadhyay; Rajesh Veeraraghavan; Krzysztof Z. Gajos (CHI, 2024)

From Stem to Stern: Contestability Along AI Value Chains. - Agathe Balayn; Yulu Pi; David Gray Widder; Kars Alfrink; Mireia Yurrita; Sohini Upadhyay; Naveena Karusala; Henrietta Lyons; Cagatay Turkay; Christelle Tessono; Blair Attard-Frost; Ujwal Gadiraju (CSCW Companion, 2024)

Counterfactual Explanations May Not Be the Best Algorithmic Recourse Approach. - Sohini Upadhyay; Himabindu Lakkaraju; Krzysztof Z. Gajos (IUI, 2025)