Amit Deshpande

Affiliations:
  • Microsoft Research, Bangalore, India
  • MIT, Cambridge, MA, USA (former)


According to our database1, Amit Deshpande authored at least 47 papers between 2002 and 2024.

Collaborative distances:

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Bibliography

2024
Rethinking Robustness of Model Attributions.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
One-Pass Additive-Error Subset Selection for ℓ <sub>p</sub> Subspace Approximation and (k, p)-Clustering.
Algorithmica, October, 2023

How Far Can Fairness Constraints Help Recover From Biased Data?
CoRR, 2023

Improved Outlier Robust Seeding for k-means.
CoRR, 2023

On Testing and Comparing Fair classifiers under Data Bias.
CoRR, 2023

2022
One-Pass Additive-Error Subset Selection for ℓ<sub>p</sub> Subspace Approximation.
Proceedings of the 49th International Colloquium on Automata, Languages, and Programming, 2022

On the Power of Randomization in Fair Classification and Representation.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Universalization of Any Adversarial Attack using Very Few Test Examples.
Proceedings of the CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), Bangalore, India, January 8, 2022

Learning and Generalization in Overparameterized Normalizing Flows.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Sampling-based dimension reduction for subspace approximation with outliers.
Theor. Comput. Sci., 2021

On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling.
CoRR, 2021

Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Rawlsian Fair Adaptation of Deep Learning Classifiers.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
How do SGD hyperparameters in natural training affect adversarial robustness?
CoRR, 2020

On Universalized Adversarial and Invariant Perturbations.
CoRR, 2020

Universalization of any adversarial attack using very few test examples.
CoRR, 2020

Invariance vs. Robustness of Neural Networks.
CoRR, 2020

Robust k-means++.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Subspace Approximation with Outliers.
Proceedings of the Computing and Combinatorics - 26th International Conference, 2020

2018
Fair and Diverse DPP-Based Data Summarization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Depth separation and weight-width trade-offs for sigmoidal neural networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Blood Count on a Smartphone Microscope: Challenges.
Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, 2017

On Robust Concepts and Small Neural Nets.
Proceedings of the 5th International Conference on Learning Representations, 2017

On the Complexity of Constrained Determinantal Point Processes.
Proceedings of the Approximation, 2017

2016
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes.
CoRR, 2016

How to be Fair and Diverse?
CoRR, 2016

Batched Gaussian Process Bandit Optimization via Determinantal Point Processes.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1.
Proceedings of the 36th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2016

2015
Embedding approximately low-dimensional ℓ<sub>2<sup>2</sup></sub> metrics into ℓ<sub>1</sub>.
CoRR, 2015

On Greedy Maximization of Entropy.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Guruswami-Sinop Rounding without Higher Level Lasserre.
Proceedings of the Approximation, 2014

2012
Zero-One Rounding of Singular Vectors.
Proceedings of the Automata, Languages, and Programming - 39th International Colloquium, 2012

2011
Algorithms and Hardness for Subspace Approximation.
Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, 2011

2010
Efficient Volume Sampling for Row/Column Subset Selection.
Proceedings of the 51th Annual IEEE Symposium on Foundations of Computer Science, 2010

2009
NP-hardness of Euclidean sum-of-squares clustering.
Mach. Learn., 2009

Algorithms and Hardness for Subspace Approximation
CoRR, 2009

The Limit of Convexity Based Isoperimetry: Sampling Harmonic-Concave Functions
CoRR, 2009

Finding Dense Subgraphs in <i>G</i>(<i>n</i>, 1/2).
Proceedings of the Approximation and Online Algorithms, 7th International Workshop, 2009

Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry.
Proceedings of the Approximation, 2009

Adaptive Sampling for k-Means Clustering.
Proceedings of the Approximation, 2009

2008
Finding Dense Subgraphs in G(n,1/2)
CoRR, 2008

2007
Sampling-based dimension reduction for subspace approximation.
Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 2007

2006
Matrix Approximation and Projective Clustering via Volume Sampling.
Theory Comput., 2006

Adaptive Sampling and Fast Low-Rank Matrix Approximation.
Electron. Colloquium Comput. Complex., 2006

2005
Lower bounds for adaptive locally decodable codes.
Random Struct. Algorithms, 2005

Improved Smoothed Analysis of the Shadow Vertex Simplex Method.
Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 2005

2002
Better Lower Bounds for Locally Decodable Codes.
Proceedings of the 17th Annual IEEE Conference on Computational Complexity, 2002


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