Amit Deshpande

62 papers A* 12A 3B 1C 1Misc 2Journal 31Unranked 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	On Optimal Steering to Achieve Exact Fairness. CoRR Mohit Sharma, Amit Deshpande, Chiranjib Bhattacharyya, Rajiv Ratn Shah
2025	A*	`conf`	Optimal Fair Learning Robust to Adversarial Distribution Shift. ICML Sushant Agarwal, Amit Deshpande, Rajmohan Rajaraman, Ravi Sundaram
2024	A*	`conf`	How Far Can Fairness Constraints Help Recover From Biased Data? ICML Mohit Sharma, Amit Deshpande
2024	J	`jnl`	On the Power of Randomization in Fair Classification and Representation. CoRR Sushant Agarwal, Amit Deshpande
2024	A*	`conf`	Rethinking Robustness of Model Attributions. AAAI Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N. Balasubramanian
2023	J	`jnl`	How Far Can Fairness Constraints Help Recover From Biased Data? CoRR Mohit Sharma, Amit Deshpande
2023	J	`jnl`	Improved Outlier Robust Seeding for k-means. CoRR Amit Deshpande, Rameshwar Pratap
2023	J	`jnl`	On Testing and Comparing Fair classifiers under Data Bias. CoRR Mohit Sharma, Amit Deshpande, Rajiv Ratn Shah
2023	J	`jnl`	One-Pass Additive-Error Subset Selection for ℓ Algorithmica Amit Deshpande, Rameshwar Pratap
2023	J	`jnl`	Rethinking Robustness of Model Attributions. CoRR Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N. Balasubramanian
2022	A	`conf`	Learning and Generalization in Overparameterized Normalizing Flows. AISTATS Kulin Shah, Amit Deshpande, Navin Goyal
2022	—	`conf`	On the Power of Randomization in Fair Classification and Representation. FAccT Sushant Agarwal, Amit Deshpande
2022	A*	`conf`	One-Pass Additive-Error Subset Selection for ℓ ICALP Amit Deshpande, Rameshwar Pratap
2022	J	`jnl`	One-pass additive-error subset selection for 𝓁 CoRR Amit Deshpande, Rameshwar Pratap
2022	—	`conf`	Universalization of Any Adversarial Attack using Very Few Test Examples. COMAD/CODS Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam, Vineeth N. Balasubramanian
2021	A*	`conf`	Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks. NeurIPS Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata, Vineeth N. Balasubramanian
2021	J	`jnl`	Learning and Generalization in Overparameterized Normalizing Flows. CoRR Kulin Shah, Amit Deshpande, Navin Goyal
2021	J	`jnl`	On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling. CoRR Amit Deshpande, Rameshwar Pratap
2021	C	`conf`	Rawlsian Fair Adaptation of Deep Learning Classifiers. AIES Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya
2021	J	`jnl`	Rawlsian Fair Adaptation of Deep Learning Classifiers. CoRR Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya
2021	J	`jnl`	Sampling-based dimension reduction for subspace approximation with outliers. Theor. Comput. Sci. Amit Deshpande, Rameshwar Pratap
2020	J	`jnl`	How do SGD hyperparameters in natural training affect adversarial robustness? CoRR Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam
2020	J	`jnl`	Invariance vs. Robustness of Neural Networks. CoRR Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam
2020	J	`jnl`	On Universalized Adversarial and Invariant Perturbations. CoRR Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam
2020	A	`conf`	Robust k-means++. UAI Amit Deshpande, Praneeth Kacham, Rameshwar Pratap
2020	Misc	`conf`	Subspace Approximation with Outliers. COCOON Amit Deshpande, Rameshwar Pratap
2020	J	`jnl`	Subspace approximation with outliers. CoRR Amit Deshpande, Rameshwar Pratap
2020	J	`jnl`	Universalization of any adversarial attack using very few test examples. CoRR Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam
2018	—	`conf`	Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) Amit Deshpande, Navin Goyal, Sushrut Karmalkar
2018	A*	`conf`	Fair and Diverse DPP-Based Data Summarization. ICML L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
2018	J	`jnl`	Fair and Diverse DPP-based Data Summarization. CoRR L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
2017	Misc	`conf`	Blood Count on a Smartphone Microscope: Challenges. HotMobile Uma Parthavi Moravapalle, Amit Deshpande, Ashish Kapoor, Ramachandran Ramjee, Priya Ravi
2017	—	`conf`	On Robust Concepts and Small Neural Nets. ICLR (Workshop) Amit Deshpande, Sushrut Karmalkar
2017	—	`conf`	On the Complexity of Constrained Determinantal Point Processes. APPROX-RANDOM L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Damian Straszak, Nisheeth K. Vishnoi
2016	—	`conf`	Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. NIPS Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
2016	J	`jnl`	Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. CoRR Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
2016	—	`conf`	Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1. FSTTCS Amit Deshpande, Prahladh Harsha, Rakesh Venkat
2016	J	`jnl`	How to be Fair and Diverse? CoRR L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
2016	J	`jnl`	On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes. CoRR Tarun Kathuria, Amit Deshpande
2015	J	`jnl`	Embedding approximately low-dimensional ℓ CoRR Amit Deshpande, Prahladh Harsha, Rakesh Venkat
2015	A*	`conf`	On Greedy Maximization of Entropy. ICML Dravyansh Sharma, Ashish Kapoor, Amit Deshpande
2014	—	`conf`	Guruswami-Sinop Rounding without Higher Level Lasserre. APPROX-RANDOM Amit Deshpande, Rakesh Venkat
2014	J	`jnl`	Guruswami-Sinop Rounding without Higher Level Lasserre. CoRR Amit Deshpande, Rakesh Venkat
2012	—	`conf`	Zero-One Rounding of Singular Vectors. ICALP (1) Amit Deshpande, Ravindran Kannan, Nikhil Srivastava
2011	A*	`conf`	Algorithms and Hardness for Subspace Approximation. SODA Amit Deshpande, Madhur Tulsiani, Nisheeth K. Vishnoi
2010	A*	`conf`	Efficient Volume Sampling for Row/Column Subset Selection. FOCS Amit Deshpande, Luis Rademacher
2010	J	`jnl`	Efficient volume sampling for row/column subset selection CoRR Amit Deshpande, Luis Rademacher
2009	—	`conf`	Adaptive Sampling for k-Means Clustering. APPROX-RANDOM Ankit Aggarwal, Amit Deshpande, Ravi Kannan
2009	J	`jnl`	Algorithms and Hardness for Subspace Approximation CoRR Amit Deshpande, Kasturi R. Varadarajan, Madhur Tulsiani, Nisheeth K. Vishnoi
2009	B	`conf`	Finding Dense Subgraphs in WAOA Atish Das Sarma, Amit Deshpande, Ravi Kannan
2009	J	`jnl`	NP-hardness of Euclidean sum-of-squares clustering. Mach. Learn. Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat
2009	—	`conf`	Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry. APPROX-RANDOM Karthekeyan Chandrasekaran, Amit Deshpande, Santosh S. Vempala
2009	J	`jnl`	The Limit of Convexity Based Isoperimetry: Sampling Harmonic-Concave Functions CoRR Karthekeyan Chandrasekaran, Amit Deshpande, Santosh S. Vempala
2008	J	`jnl`	Finding Dense Subgraphs in G(n,1/2) CoRR Atish Das Sarma, Amit Deshpande, Ravi Kannan
2007	A*	`conf`	Sampling-based dimension reduction for subspace approximation. STOC Amit Deshpande, Kasturi R. Varadarajan
2006	—	`conf`	Adaptive Sampling and Fast Low-Rank Matrix Approximation. APPROX-RANDOM Amit Deshpande, Santosh S. Vempala
2006	J	`jnl`	Adaptive Sampling and Fast Low-Rank Matrix Approximation. Electron. Colloquium Comput. Complex. Amit Deshpande, Santosh S. Vempala
2006	J	`jnl`	Matrix Approximation and Projective Clustering via Volume Sampling. Theory Comput. Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
2006	A*	`conf`	Matrix approximation and projective clustering via volume sampling. SODA Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
2005	A*	`conf`	Improved Smoothed Analysis of the Shadow Vertex Simplex Method. FOCS Amit Deshpande, Daniel A. Spielman
2005	J	`jnl`	Lower bounds for adaptive locally decodable codes. Random Struct. Algorithms Amit Deshpande, Rahul Jain, Telikepalli Kavitha, Satyanarayana V. Lokam, Jaikumar Radhakrishnan
2002	A	`conf`	Better Lower Bounds for Locally Decodable Codes. CCC Amit Deshpande, Rahul Jain, Telikepalli Kavitha, Jaikumar Radhakrishnan, Satyanarayana V. Lokam