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ORCIDAmit Deshpande 0001
Person information
- affiliation: Microsoft Research, Bangalore, India
- affiliation (former): MIT, Cambridge, MA, USA
Other persons with the same name
- Amit Deshpande — disambiguation page
- Amit Deshpande 0002 — Drexel University, College of Information Science and Technology, Philadelphia, USA
- Amit Deshpande 0003 — SAP Research, Palo Alto, USA
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2020 – today
- 2024
[c30]Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N. Balasubramanian:
Rethinking Robustness of Model Attributions. AAAI 2024: 2688-2696- [c29]Mohit Sharma, Amit Deshpande:
How Far Can Fairness Constraints Help Recover From Biased Data? ICML 2024 - [i25]Sushant Agarwal, Amit Deshpande:
On the Power of Randomization in Fair Classification and Representation. CoRR abs/2406.03142 (2024) - 2023
- [j5]Amit Deshpande, Rameshwar Pratap:
One-Pass Additive-Error Subset Selection for ℓ p Subspace Approximation and (k, p)-Clustering. Algorithmica 85(10): 3144-3167 (2023) - [i24]Mohit Sharma, Amit Deshpande, Rajiv Ratn Shah:
On Testing and Comparing Fair classifiers under Data Bias. CoRR abs/2302.05906 (2023) - [i23]Amit Deshpande, Rameshwar Pratap:
Improved Outlier Robust Seeding for k-means. CoRR abs/2309.02710 (2023) - [i22]Mohit Sharma, Amit Deshpande:
How Far Can Fairness Constraints Help Recover From Biased Data? CoRR abs/2312.10396 (2023) - [i21]Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N. Balasubramanian:
Rethinking Robustness of Model Attributions. CoRR abs/2312.10534 (2023) - 2022
- [c28]Kulin Shah, Amit Deshpande, Navin Goyal:
Learning and Generalization in Overparameterized Normalizing Flows. AISTATS 2022: 9430-9504 - [c27]Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam, Vineeth N. Balasubramanian:
Universalization of Any Adversarial Attack using Very Few Test Examples. COMAD/CODS 2022: 72-80 - [c26]Sushant Agarwal, Amit Deshpande:
On the Power of Randomization in Fair Classification and Representation. FAccT 2022: 1542-1551 - [c25]Amit Deshpande, Rameshwar Pratap:
One-Pass Additive-Error Subset Selection for ℓp Subspace Approximation. ICALP 2022: 51:1-51:14 - [i20]Amit Deshpande, Rameshwar Pratap:
One-pass additive-error subset selection for 𝓁p subspace approximation. CoRR abs/2204.12073 (2022) - 2021
- [j4]Amit Deshpande, Rameshwar Pratap
:
Sampling-based dimension reduction for subspace approximation with outliers. Theor. Comput. Sci. 858: 100-113 (2021) - [c24]Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya:
Rawlsian Fair Adaptation of Deep Learning Classifiers. AIES 2021: 936-945 - [c23]Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata, Vineeth N. Balasubramanian:
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks. NeurIPS 2021: 27462-27474 - [i19]Amit Deshpande, Rameshwar Pratap:
On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling. CoRR abs/2103.11107 (2021) - [i18]Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya:
Rawlsian Fair Adaptation of Deep Learning Classifiers. CoRR abs/2105.14890 (2021) - [i17]Kulin Shah, Amit Deshpande, Navin Goyal:
Learning and Generalization in Overparameterized Normalizing Flows. CoRR abs/2106.10535 (2021) - 2020
- [c22]Amit Deshpande, Rameshwar Pratap:
Subspace Approximation with Outliers. COCOON 2020: 1-13 - [c21]Amit Deshpande, Praneeth Kacham, Rameshwar Pratap:
Robust k-means++. UAI 2020: 799-808 - [i16]Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam:
Invariance vs. Robustness of Neural Networks. CoRR abs/2002.11318 (2020) - [i15]Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam:
Universalization of any adversarial attack using very few test examples. CoRR abs/2005.08632 (2020) - [i14]Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam:
On Universalized Adversarial and Invariant Perturbations. CoRR abs/2006.04449 (2020) - [i13]Sandesh Kamath, Amit Deshpande, K. V. Subrahmanyam:
How do SGD hyperparameters in natural training affect adversarial robustness? CoRR abs/2006.11604 (2020) - [i12]Amit Deshpande, Rameshwar Pratap:
Subspace approximation with outliers. CoRR abs/2006.16573 (2020)
2010 – 2019
- 2018
- [c20]Amit Deshpande, Navin Goyal, Sushrut Karmalkar:
Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) 2018 - [c19]L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi:
Fair and Diverse DPP-Based Data Summarization. ICML 2018: 715-724 - [i11]L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi:
Fair and Diverse DPP-based Data Summarization. CoRR abs/1802.04023 (2018) - 2017
- [c18]L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Damian Straszak, Nisheeth K. Vishnoi:
On the Complexity of Constrained Determinantal Point Processes. APPROX-RANDOM 2017: 36:1-36:22 - [c17]Amit Deshpande, Sushrut Karmalkar:
On Robust Concepts and Small Neural Nets. ICLR (Workshop) 2017 - [c16]Uma Parthavi Moravapalle, Amit Deshpande, Ashish Kapoor, Ramachandran Ramjee, Priya Ravi:
Blood Count on a Smartphone Microscope: Challenges. HotMobile 2017: 37-42 - 2016
- [c15]Amit Deshpande, Prahladh Harsha, Rakesh Venkat:
Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1. FSTTCS 2016: 10:1-10:13 - [c14]Tarun Kathuria, Amit Deshpande, Pushmeet Kohli:
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. NIPS 2016: 4206-4214 - [i10]Tarun Kathuria, Amit Deshpande:
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes. CoRR abs/1607.01551 (2016) - [i9]L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi:
How to be Fair and Diverse? CoRR abs/1610.07183 (2016) - [i8]Tarun Kathuria, Amit Deshpande, Pushmeet Kohli:
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. CoRR abs/1611.04088 (2016) - 2015
- [c13]Dravyansh Sharma, Ashish Kapoor, Amit Deshpande:
On Greedy Maximization of Entropy. ICML 2015: 1330-1338 - [i7]Amit Deshpande, Prahladh Harsha, Rakesh Venkat:
Embedding approximately low-dimensional ℓ22 metrics into ℓ1. CoRR abs/1512.04170 (2015) - 2014
- [c12]Amit Deshpande, Rakesh Venkat:
Guruswami-Sinop Rounding without Higher Level Lasserre. APPROX-RANDOM 2014: 105-114 - [i6]Amit Deshpande, Rakesh Venkat:
Guruswami-Sinop Rounding without Higher Level Lasserre. CoRR abs/1406.7279 (2014) - 2012
- [c11]Amit Deshpande, Ravindran Kannan, Nikhil Srivastava:
Zero-One Rounding of Singular Vectors. ICALP (1) 2012: 278-289 - 2011
- [c10]Amit Deshpande, Madhur Tulsiani, Nisheeth K. Vishnoi:
Algorithms and Hardness for Subspace Approximation. SODA 2011: 482-496 - 2010
- [c9]Amit Deshpande, Luis Rademacher:
Efficient Volume Sampling for Row/Column Subset Selection. FOCS 2010: 329-338 - [i5]Amit Deshpande, Luis Rademacher:
Efficient volume sampling for row/column subset selection. CoRR abs/1004.4057 (2010)
2000 – 2009
- 2009
- [j3]Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat:
NP-hardness of Euclidean sum-of-squares clustering. Mach. Learn. 75(2): 245-248 (2009) - [c8]Ankit Aggarwal, Amit Deshpande, Ravi Kannan:
Adaptive Sampling for k-Means Clustering. APPROX-RANDOM 2009: 15-28 - [c7]Karthekeyan Chandrasekaran, Amit Deshpande, Santosh S. Vempala:
Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry. APPROX-RANDOM 2009: 420-433 - [c6]Atish Das Sarma, Amit Deshpande, Ravi Kannan:
Finding Dense Subgraphs in G(n, 1/2). WAOA 2009: 98-103 - [i4]Karthekeyan Chandrasekaran, Amit Deshpande, Santosh S. Vempala:
The Limit of Convexity Based Isoperimetry: Sampling Harmonic-Concave Functions. CoRR abs/0906.2448 (2009) - [i3]Amit Deshpande, Kasturi R. Varadarajan, Madhur Tulsiani, Nisheeth K. Vishnoi:
Algorithms and Hardness for Subspace Approximation. CoRR abs/0912.1403 (2009) - 2008
- [i2]Atish Das Sarma, Amit Deshpande, Ravi Kannan:
Finding Dense Subgraphs in G(n,1/2). CoRR abs/0807.5111 (2008) - 2007
- [c5]Amit Deshpande, Kasturi R. Varadarajan:
Sampling-based dimension reduction for subspace approximation. STOC 2007: 641-650 - 2006
- [j2]Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang:
Matrix Approximation and Projective Clustering via Volume Sampling. Theory Comput. 2(12): 225-247 (2006) - [c4]Amit Deshpande, Santosh S. Vempala:
Adaptive Sampling and Fast Low-Rank Matrix Approximation. APPROX-RANDOM 2006: 292-303 - [c3]Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang:
Matrix approximation and projective clustering via volume sampling. SODA 2006: 1117-1126 - [i1]Amit Deshpande, Santosh S. Vempala:
Adaptive Sampling and Fast Low-Rank Matrix Approximation. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [j1]Amit Deshpande, Rahul Jain, Telikepalli Kavitha, Satyanarayana V. Lokam, Jaikumar Radhakrishnan:
Lower bounds for adaptive locally decodable codes. Random Struct. Algorithms 27(3): 358-378 (2005) - [c2]Amit Deshpande, Daniel A. Spielman:
Improved Smoothed Analysis of the Shadow Vertex Simplex Method. FOCS 2005: 349-356 - 2002
- [c1]Amit Deshpande, Rahul Jain, Telikepalli Kavitha, Jaikumar Radhakrishnan, Satyanarayana V. Lokam:
Better Lower Bounds for Locally Decodable Codes. CCC 2002: 184-193
Coauthor Index
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