| 2026 | AAAI | Incorporating Token Importance in Multi-Vector Retrieval. | Archish S, Ankit Garg, Kirankumar Shiragur, Neeraj Kayal |
| 2025 | ICML | Graph-Based Algorithms for Diverse Similarity Search. | Piyush Anand, Piotr Indyk, Ravishankar Krishnaswamy, Sepideh Mahabadi, Vikas C. Raykar, Kirankumar Shiragur, Haike Xu |
| 2025 | ICML | Sort Before You Prune: Improved Worst-Case Guarantees of the DiskANN Family of Graphs. | Siddharth Gollapudi, Ravishankar Krishnaswamy, Kirankumar Shiragur, Harsh Wardhan |
| 2024 | AISTATS | Causal Discovery under Off-Target Interventions. | Davin Choo, Kirankumar Shiragur, Caroline Uhler |
| 2024 | AISTATS | Membership Testing in Markov Equivalence Classes via Independence Queries. | Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler |
| 2024 | ICML | Causal Discovery with Fewer Conditional Independence Tests. | Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler |
| 2023 | AISTATS | Subset verification and search algorithms for causal DAGs. | Davin Choo, Kirankumar Shiragur |
| 2023 | ICML | New metrics and search algorithms for weighted causal DAGs. | Davin Choo, Kirankumar Shiragur |
| 2023 | UAI | Adaptivity Complexity for Causal Graph Discovery. | Davin Choo, Kirankumar Shiragur |
| 2021 | COLT | The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood. | Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford |
| 2021 | ICML | Reward Identification in Inverse Reinforcement Learning. | Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon |
| 2021 | SODA | On the Competitive Analysis and High Accuracy Optimality of Profile Maximum Likelihood. | Yanjun Han, Kirankumar Shiragur |
| 2021 | STOC | Fractionally log-concave and sector-stable polynomials: counting planar matchings and more. | Yeganeh Alimohammadi, Nima Anari, Kirankumar Shiragur, Thuy-Duong Vuong |
| 2019 | STOC | Efficient profile maximum likelihood for universal symmetric property estimation. | Moses Charikar, Kirankumar Shiragur, Aaron Sidford |