On the role of conductance, geography and topology in predicting hashtag virality. - Siddharth Bora; Harvineet Singh; Anirban Sen; Amitabha Bagchi; Parag Singla (Soc. Netw. Anal. Min., 2015)

Show and Recall: Learning What Makes Videos Memorable. - Sumit Shekhar; Dhruv Singal; Harvineet Singh; Manav Kedia; Akhil Shetty (ICCV Workshops, 2017)

Learning User Representations in Online Social Networks using Temporal Dynamics of Information Diffusion. - Harvineet Singh; Amitabha Bagchi; Parag Singla (CoRR, 2017)

Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning. - Harvineet Singh; Shiv Kumar Saini; Ritwick Chaudhry; Pradeep Dogga (EDM, 2018)

Modeling Time to Open of Emails with a Latent State for User Engagement Level. - Moumita Sinha; Vishwa Vinay; Harvineet Singh (WSDM, 2018)

Online Diverse Learning to Rank from Partial-Click Feedback. - Prakhar Gupta; Gaurush Hiranandani; Harvineet Singh; Branislav Kveton; Zheng Wen; Iftikhar Ahamath Burhanuddin (CoRR, 2018)

Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank. - Gaurush Hiranandani; Harvineet Singh; Prakhar Gupta; Iftikhar Ahamath Burhanuddin; Zheng Wen; Branislav Kveton (UAI, 2019)

Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts. - Aadhavan M. Nambhi; Bhanu Prakash Reddy; Aarsh Prakash Agarwal; Gaurav Verma; Harvineet Singh; Iftikhar Ahamath Burhanuddin (UMAP, 2019)

Fair Predictors under Distribution Shift. - Harvineet Singh; Rina Singh; Vishwali Mhasawade; Rumi Chunara (CoRR, 2019)

An RNN-Survival Model to Decide Email Send Times. - Harvineet Singh; Moumita Sinha; Atanu R. Sinha; Sahil Garg; Neha Banerjee (CoRR, 2020)

Fairness Violations and Mitigation under Covariate Shift. - Harvineet Singh; Rina Singh; Vishwali Mhasawade; Rumi Chunara (FAccT, 2021)

Learning Under Adversarial and Interventional Shifts. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (CoRR, 2021)

Fair, Robust, and Data-Efficient Machine Learning in Healthcare. - Harvineet Singh (AIES, 2022)

Towards Robust Off-Policy Evaluation via Human Inputs. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (AIES, 2022)

Segmenting across places: The need for fair transfer learning with satellite imagery. - Miao Zhang; Harvineet Singh; Lazarus Chok; Rumi Chunara (CVPR Workshops, 2022)

Machine Learning for Health (ML4H) 2022. - Antonio Parziale; Monica Agrawal; Shengpu Tang; Kristen Severson 001; Luis Oala; Adarsh Subbaswamy; Sayantan Kumar; Elora D. M. Schörverth; Stefan Hegselmann; Helen Zhou; Ghada Zamzmi; Purity Mugambi; et al. (ML4H@NeurIPS, 2022)

Data poisoning attacks on off-policy policy evaluation methods. - Elita A. Lobo; Harvineet Singh; Marek Petrik; Cynthia Rudin; Himabindu Lakkaraju (UAI, 2022)

Measures of Disparity and their Efficient Estimation. - Harvineet Singh; Rumi Chunara (AIES, 2023)

When do Minimax-fair Learning and Empirical Risk Minimization Coincide? - Harvineet Singh; Matthäus Kleindessner; Volkan Cevher; Rumi Chunara; Chris Russell 001 (ICML, 2023)

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. - Haoran Zhang 003; Harvineet Singh; Marzyeh Ghassemi; Shalmali Joshi (ICML, 2023)

Machine Learning for Health (ML4H) 2023. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Kristen Severson 001; Mercy Nyamewaa Asiedu; Serina Chang; Bonaventure F. P. Dossou; Qian Huang; Fahad Kamran; Haoran Zhang 003; Sujay Nagaraj; et al. (ML4H@NeurIPS, 2023)

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CoRR, 2023)

Machine Learning for Health symposium 2023 - Findings track. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Mercy Nyamewaa Asiedu; Serina Chang; Thomas Hartvigsen; Harvineet Singh (CoRR, 2023)

A Brief Tutorial on Sample Size Calculations for Fairness Audits. - Harvineet Singh; Fan Xia; Mi-Ok Kim; Romain Pirracchio; Rumi Chunara; Jean Feng (CoRR, 2023)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Anthony Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CLeaR, 2024)

A hierarchical decomposition for explaining ML performance discrepancies. - Harvineet Singh; Fan Xia; Adarsh Subbaswamy; Alexej Gossmann; Jean Feng (NeurIPS, 2024)

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. - Hyewon Jeong; Sarah Jabbour; Yuzhe Yang 003; Rahul Thapa; Hussein Mozannar; William Jongwon Han; Nikita Mehandru; Michael Wornow; Vladislav Lialin; Xin Liu 034; Alejandro Lozano; Jiacheng Zhu; et al. (CoRR, 2024)

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. - Harvineet Singh; Fan Xia; Alexej Gossmann; Andrew Chuang; Julian C. Hong; Jean Feng (CoRR, 2025)