| 2025 | ICML | Optimal Fair Learning Robust to Adversarial Distribution Shift. | Sushant Agarwal, Amit Deshpande, Rajmohan Rajaraman, Ravi Sundaram |
| 2024 | AAAI | Rethinking Robustness of Model Attributions. | Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N. Balasubramanian |
| 2024 | ACL | NICE: To Optimize In-Context Examples or Not? | Pragya Srivastava, Satvik Golechha, Amit Deshpande, Amit Sharma |
| 2024 | ICML | How Far Can Fairness Constraints Help Recover From Biased Data? | Mohit Sharma, Amit Deshpande |
| 2024 | SIGIR | Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance. | Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis |
| 2023 | AIES | Sampling Individually-Fair Rankings that are Always Group Fair. | Sruthi Gorantla, Anay Mehrotra, Amit Deshpande, Anand Louis |
| 2023 | IJCAI | Sampling Ex-Post Group-Fair Rankings. | Sruthi Gorantla, Amit Deshpande, Anand Louis |
| 2022 | AISTATS | Learning and Generalization in Overparameterized Normalizing Flows. | Kulin Shah, Amit Deshpande, Navin Goyal |
| 2022 | COMAD | Universalization of Any Adversarial Attack using Very Few Test Examples. | Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam, Vineeth N. Balasubramanian |
| 2022 | ICALP | One-Pass Additive-Error Subset Selection for ℓ | Amit Deshpande, Rameshwar Pratap |
| 2021 | AAAI | How Linguistically Fair Are Multilingual Pre-Trained Language Models? | Monojit Choudhury, Amit Deshpande |
| 2021 | AAAI | The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective. | Naman Goel, Alfonso Amayuelas, Amit Deshpande, Amit Sharma |
| 2021 | AIES | Rawlsian Fair Adaptation of Deep Learning Classifiers. | Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya |
| 2021 | ICML | On the Problem of Underranking in Group-Fair Ranking. | Sruthi Gorantla, Amit Deshpande, Anand Louis |
| 2020 | COCOON | Subspace Approximation with Outliers. | Amit Deshpande, Rameshwar Pratap |
| 2020 | UAI | Robust k-means++. | Amit Deshpande, Praneeth Kacham, Rameshwar Pratap |
| 2019 | AISTATS | On Euclidean k-Means Clustering with alpha-Center Proximity. | Amit Deshpande, Anand Louis, Apoorv Vikram Singh |
| 2018 | ICLR | Depth separation and weight-width trade-offs for sigmoidal neural networks. | Amit Deshpande, Navin Goyal, Sushrut Karmalkar |
| 2018 | ICML | Fair and Diverse DPP-Based Data Summarization. | L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi |
| 2017 | ICLR | On Robust Concepts and Small Neural Nets. | Amit Deshpande, Sushrut Karmalkar |
| 2015 | ICML | On Greedy Maximization of Entropy. | Dravyansh Sharma, Ashish Kapoor, Amit Deshpande |
| 2012 | ICALP | Zero-One Rounding of Singular Vectors. | Amit Deshpande, Ravindran Kannan, Nikhil Srivastava |
| 2011 | SODA | Algorithms and Hardness for Subspace Approximation. | Amit Deshpande, Madhur Tulsiani, Nisheeth K. Vishnoi |
| 2010 | FOCS | Efficient Volume Sampling for Row/Column Subset Selection. | Amit Deshpande, Luis Rademacher |
| 2009 | WAOA | Finding Dense Subgraphs in | Atish Das Sarma, Amit Deshpande, Ravi Kannan |
| 2007 | STOC | Sampling-based dimension reduction for subspace approximation. | Amit Deshpande, Kasturi R. Varadarajan |
| 2006 | SODA | Matrix approximation and projective clustering via volume sampling. | Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang |
| 2005 | FOCS | Improved Smoothed Analysis of the Shadow Vertex Simplex Method. | Amit Deshpande, Daniel A. Spielman |