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Karthikeyan Shanmugam
Karthikeyan Shanmugam
Research Scientist, Google Research India
Geverifieerd e-mailadres voor google.com - Homepage
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Femtocaching: Wireless content delivery through distributed caching helpers
K Shanmugam, N Golrezaei, AG Dimakis, AF Molisch, G Caire
IEEE Transactions on Information Theory 59 (12), 8402-8413, 2013
22612013
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
A Dhurandhar, PY Chen, R Luss, CC Tu, P Ting, K Shanmugam, P Das
Advances in neural information processing systems 31, 2018
7452018
One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ...
arXiv preprint arXiv:1909.03012, 2019
658*2019
Invariant risk minimization games
K Ahuja, K Shanmugam, K Varshney, A Dhurandhar
International Conference on Machine Learning, 145-155, 2020
2852020
Finite-length analysis of caching-aided coded multicasting
K Shanmugam, M Ji, AM Tulino, J Llorca, AG Dimakis
IEEE Transactions on Information Theory 62 (10), 5524-5537, 2016
2042016
Coded caching with linear subpacketization is possible using Ruzsa-Szeméredi graphs
K Shanmugam, AM Tulino, AG Dimakis
2017 IEEE International Symposium on Information Theory (ISIT), 1237-1241, 2017
1282017
Learning causal graphs with small interventions
K Shanmugam, M Kocaoglu, AG Dimakis, S Vishwanath
Advances in Neural Information Processing Systems 28, 2015
1142015
Local graph coloring and index coding
K Shanmugam, AG Dimakis, M Langberg
2013 IEEE International Symposium on Information Theory, 1152-1156, 2013
1122013
Causal discovery from soft interventions with unknown targets: Characterization and learning
A Jaber, M Kocaoglu, K Shanmugam, E Bareinboim
Advances in neural information processing systems 33, 9551-9561, 2020
1052020
Model-powered conditional independence test
R Sen, AT Suresh, K Shanmugam, AG Dimakis, S Shakkottai
Advances in neural information processing systems 30, 2017
1052017
Experimental design for learning causal graphs with latent variables
M Kocaoglu, K Shanmugam, E Bareinboim
Advances in Neural Information Processing Systems 30, 2017
932017
Leveraging latent features for local explanations
R Luss, PY Chen, A Dhurandhar, P Sattigeri, Y Zhang, K Shanmugam, ...
Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021
91*2021
Finite-sample analysis of contractive stochastic approximation using smooth convex envelopes
Z Chen, ST Maguluri, S Shakkottai, K Shanmugam
Advances in Neural Information Processing Systems 33, 8223-8234, 2020
90*2020
Empirical or invariant risk minimization? a sample complexity perspective
K Ahuja, J Wang, A Dhurandhar, K Shanmugam, KR Varshney
arXiv preprint arXiv:2010.16412, 2020
872020
Causal Best Intervention Identification via Importance Sampling.
R Sen, K Shanmugam, AG Dimakis, S Shakkottai
CoRR, 2017
86*2017
Model agnostic contrastive explanations for structured data
A Dhurandhar, T Pedapati, A Balakrishnan, PY Chen, K Shanmugam, ...
arXiv preprint arXiv:1906.00117, 2019
852019
Abcd-strategy: Budgeted experimental design for targeted causal structure discovery
R Agrawal, C Squires, K Yang, K Shanmugam, C Uhler
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
762019
A lyapunov theory for finite-sample guarantees of markovian stochastic approximation
Z Chen, ST Maguluri, S Shakkottai, K Shanmugam
Operations Research 72 (4), 1352-1367, 2024
74*2024
A repair framework for scalar MDS codes
K Shanmugam, DS Papailiopoulos, AG Dimakis, G Caire
IEEE Journal on Selected Areas in Communications 32 (5), 998-1007, 2014
732014
Improving simple models with confidence profiles
A Dhurandhar, K Shanmugam, R Luss, PA Olsen
Advances in Neural Information Processing Systems 31, 2018
702018
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Artikelen 1–20