Better Lower Bounds for Locally Decodable Codes. - Amit Deshpande 001; Rahul Jain 001; Telikepalli Kavitha; Jaikumar Radhakrishnan; Satyanarayana V. Lokam (CCC, 2002)

Improved Smoothed Analysis of the Shadow Vertex Simplex Method. - Amit Deshpande 001; Daniel A. Spielman (FOCS, 2005)

Lower bounds for adaptive locally decodable codes. - Amit Deshpande 001; Rahul Jain 001; Telikepalli Kavitha; Satyanarayana V. Lokam; Jaikumar Radhakrishnan (Random Struct. Algorithms, 2005)

Adaptive Sampling and Fast Low-Rank Matrix Approximation. - Amit Deshpande 001; Santosh S. Vempala (Electron. Colloquium Comput. Complex., 2006)

Matrix Approximation and Projective Clustering via Volume Sampling. - Amit Deshpande 001; Luis Rademacher; Santosh S. Vempala; Grant Wang (Theory Comput., 2006)

Sampling-based dimension reduction for subspace approximation. - Amit Deshpande 001; Kasturi R. Varadarajan (STOC, 2007)

Finding Dense Subgraphs in G(n,1/2) - Atish Das Sarma; Amit Deshpande 001; Ravi Kannan (CoRR, 2008)

Adaptive Sampling for k-Means Clustering. - Ankit Aggarwal; Amit Deshpande 001; Ravi Kannan (APPROX-RANDOM, 2009)

Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry. - Karthekeyan Chandrasekaran; Amit Deshpande 001; Santosh S. Vempala (APPROX-RANDOM, 2009)

Finding Dense Subgraphs in G(n, 1/2). - Atish Das Sarma; Amit Deshpande 001; Ravi Kannan (WAOA, 2009)

The Limit of Convexity Based Isoperimetry: Sampling Harmonic-Concave Functions - Karthekeyan Chandrasekaran; Amit Deshpande 001; Santosh S. Vempala (CoRR, 2009)

Algorithms and Hardness for Subspace Approximation - Amit Deshpande 001; Kasturi R. Varadarajan; Madhur Tulsiani; Nisheeth K. Vishnoi (CoRR, 2009)

NP-hardness of Euclidean sum-of-squares clustering. - Daniel Aloise; Amit Deshpande 001; Pierre Hansen; Preyas Popat (Mach. Learn., 2009)

Efficient Volume Sampling for Row/Column Subset Selection. - Amit Deshpande 001; Luis Rademacher (FOCS, 2010)

Algorithms and Hardness for Subspace Approximation. - Amit Deshpande 001; Madhur Tulsiani; Nisheeth K. Vishnoi (SODA, 2011)

Zero-One Rounding of Singular Vectors. - Amit Deshpande 001; Ravindran Kannan; Nikhil Srivastava (ICALP (1), 2012)

Guruswami-Sinop Rounding without Higher Level Lasserre. - Amit Deshpande 001; Rakesh Venkat (APPROX-RANDOM, 2014)

On Greedy Maximization of Entropy. - Dravyansh Sharma; Ashish Kapoor; Amit Deshpande 001 (ICML, 2015)

Embedding approximately low-dimensional ℓ22 metrics into ℓ1. - Amit Deshpande 001; Prahladh Harsha; Rakesh Venkat (CoRR, 2015)

Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1. - Amit Deshpande 001; Prahladh Harsha; Rakesh Venkat (FSTTCS, 2016)

Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. - Tarun Kathuria; Amit Deshpande 001; Pushmeet Kohli (NIPS, 2016)

How to be Fair and Diverse? - L. Elisa Celis; Amit Deshpande 001; Tarun Kathuria; Nisheeth K. Vishnoi (CoRR, 2016)

On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes. - Tarun Kathuria; Amit Deshpande 001 (CoRR, 2016)

On the Complexity of Constrained Determinantal Point Processes. - L. Elisa Celis; Amit Deshpande 001; Tarun Kathuria; Damian Straszak; Nisheeth K. Vishnoi (APPROX-RANDOM, 2017)

On Robust Concepts and Small Neural Nets. - Amit Deshpande 001; Sushrut Karmalkar (ICLR (Workshop), 2017)

Blood Count on a Smartphone Microscope: Challenges. - Uma Parthavi Moravapalle; Amit Deshpande 001; Ashish Kapoor; Ramachandran Ramjee; Priya Ravi (HotMobile, 2017)

Depth separation and weight-width trade-offs for sigmoidal neural networks. - Amit Deshpande 001; Navin Goyal; Sushrut Karmalkar (ICLR (Workshop), 2018)

Fair and Diverse DPP-Based Data Summarization. - L. Elisa Celis; Vijay Keswani; Damian Straszak; Amit Deshpande 001; Tarun Kathuria; Nisheeth K. Vishnoi (ICML, 2018)

Subspace Approximation with Outliers. - Amit Deshpande 001; Rameshwar Pratap (COCOON, 2020)

Robust k-means++. - Amit Deshpande 001; Praneeth Kacham; Rameshwar Pratap (UAI, 2020)

Invariance vs. Robustness of Neural Networks. - Sandesh Kamath; Amit Deshpande 001; K. V. Subrahmanyam 001 (CoRR, 2020)

Universalization of any adversarial attack using very few test examples. - Sandesh Kamath; Amit Deshpande 001; K. V. Subrahmanyam 001 (CoRR, 2020)

On Universalized Adversarial and Invariant Perturbations. - Sandesh Kamath; Amit Deshpande 001; K. V. Subrahmanyam 001 (CoRR, 2020)

How do SGD hyperparameters in natural training affect adversarial robustness? - Sandesh Kamath; Amit Deshpande 001; K. V. Subrahmanyam 001 (CoRR, 2020)

Rawlsian Fair Adaptation of Deep Learning Classifiers. - Kulin Shah; Pooja Gupta; Amit Deshpande 001; Chiranjib Bhattacharyya (AIES, 2021)

Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks. - Sandesh Kamath; Amit Deshpande 001; Subrahmanyam Kambhampati Venkata; Vineeth N. Balasubramanian (NeurIPS, 2021)

On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling. - Amit Deshpande 001; Rameshwar Pratap (CoRR, 2021)

Sampling-based dimension reduction for subspace approximation with outliers. - Amit Deshpande 001; Rameshwar Pratap (Theor. Comput. Sci., 2021)

Learning and Generalization in Overparameterized Normalizing Flows. - Kulin Shah; Amit Deshpande 001; Navin Goyal (AISTATS, 2022)

Universalization of Any Adversarial Attack using Very Few Test Examples. - Sandesh Kamath; Amit Deshpande 001; K. V. Subrahmanyam 001; Vineeth N. Balasubramanian (COMAD/CODS, 2022)

On the Power of Randomization in Fair Classification and Representation. - Sushant Agarwal; Amit Deshpande 001 (FAccT, 2022)

One-Pass Additive-Error Subset Selection for ℓp Subspace Approximation. - Amit Deshpande 001; Rameshwar Pratap (ICALP, 2022)

On Testing and Comparing Fair classifiers under Data Bias. - Mohit Sharma 004; Amit Deshpande 001; Rajiv Ratn Shah (CoRR, 2023)

Improved Outlier Robust Seeding for k-means. - Amit Deshpande 001; Rameshwar Pratap (CoRR, 2023)

One-Pass Additive-Error Subset Selection for ℓ p Subspace Approximation and (k, p)-Clustering. - Amit Deshpande 001; Rameshwar Pratap (Algorithmica, 2023)

Rethinking Robustness of Model Attributions. - Sandesh Kamath; Sankalp Mittal; Amit Deshpande 001; Vineeth N. Balasubramanian (AAAI, 2024)

How Far Can Fairness Constraints Help Recover From Biased Data? - Mohit Sharma 004; Amit Deshpande 001 (ICML, 2024)