Karthikeyan Shanmugam

Office:

Room. 30-218

1101 Route 134 Kitchawan Rd

Yorktown Heights, NY 10598

Email: (firstname)(lastname)88 AT gmail DOT com, (firstname).(lastname)2 AT ibm DOT com

I am currently a Research Staff Member with the IBM Research AI group, NY since 2017. Previously, I was a Herman Goldstine Postdoctoral Fellow in the Math Sciences Division at IBM Research, NY. I obtained my Ph.D. in Electrical and Computer Engineering from UT Austin in summer 2016. My advisor at UT was Alex Dimakis. I obtained my MS degree in Electrical Engineering (2010-2012) from the University of Southern California, B.Tech and M.Tech degrees in Electrical Engineering from IIT Madras in 2010.

My research interests broadly lie in Graph algorithms, Machine learning, Optimization, Coding Theory and Information Theory. In machine learning, my recent focus is on graphical model learning, causal inference and explainability. I also work on problems relating to information flow, storage and caching over networks.

[ My CV] [ Google Scholar] [Dissertation]


Publications

Journals (Accepted/Submitted)

J1. A Repair Framework for Scalar MDS Codes

K. Shanmugam, D.S. Papailiopoulos , A.G. Dimakis and G. Caire

IEEE JSAC Special issue on Distributed Storage, Vol:32(5), 998 -1007, 2014. [ arxiv ]

(Conference version C1 )

J2. FemtoCaching: Wireless Content Delivery through Distributed Caching Helpers

K. Shanmugam, N. Golrezaei , A.G. Dimakis, A.F. Molisch and G. Caire

IEEE Transactions on Information Theory, 8402-8413, Vol:59(12), Dec 2013. [ arxiv version ]

(Conference versions C2 and C3 )

J3.Finite Length Analysis of Caching-Aided Coded Multicasting

Karthikeyan Shanmugam, Mingyue Ji, Antonia M.Tulino, Jaime Llorca, Alexandros G. Dimakis.

IEEE Transactions on Information Theory, 2016. [ arxiv version] [IEEE version]

J4.Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks

G. Vettigli, M. Ji, K. Shanmugam, J. Llorca and A.M. Tulino and Giuseppe Caire

Entropy 2019 [link]

J5.AI Explainability 360. An Extensible Toolkit of AI Explainability Algorithms.

V. Arya, R. K. E. Bellamy, P. Chen, A. Dhurandhar, M. Hind, S. C. Hoffman, S. Houde, Q. V. Liao, R. Luss, A. Mojsilović, S. Mourad, P. Pedemonte, R. Raghavendra, J. Richards, P. Sattigeri, K. Shanmugam, M. Singh, K. R. Varshney, D. Wei, Y. Zhang

Journal of Machine Learning Research (JMLR), 2020 [ link ]

Conferences

Machine Learning


Conditionally Independent Data Generation

K. Ahuja, P. Sattigeri, K. Shanmugam, D.Wei, K.N. Ramamurthy, M. Kocaoglu

UAI 2021. [link]

Leveraging Latent Features for Local Explanations

R. Luss, Pin-Yu Chen, A. Dhurandhar, P. Sattigeri, Y. Zhang, K. Shanmugam, Chun-Chen Tu,

KDD 2021. [arxiv]

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation

Kristjan Greenewald, Dmitriy Katz-Rogozhnikov, Karthik Shanmugam

AISTATS 2021 [arxiv]

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions

Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar

AISTATS 2021 [arxiv][code]

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

ICLR 2021 [arxiv][code]

Treatment Effect Estimation using Invariant Risk Minimization

Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush Varshney, Amit Dhurandhar

ICASSP 2021 [arxiv]

Active Structure Learning of Causal DAGs via Directed Clique Trees

Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu and Karthikeyan Shanmugam

NeurIPS 2020 [ arxiv ] [ code ]

Learning Global Transparent Models consistent with Local Contrastive Explanations

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam and Amit Dhurandhar

NeurIPS 2020 [ arxiv ]

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis and Sanjay Shakkottai

NeurIPS 2020 [ arxiv ][ code ]

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning

Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam and Elias Bareinboim

NeurIPS 2020 [ pdf ]

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes

Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai and Karthikeyan Shanmugam

NeurIPS 2020 [ arxiv]

Hawkesian Graphical Event Models

Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian and Lingzhou Xue.

PGM 2020 [ pdf ]

Evaluation of Causal Inference Techniques for AIOps

Vijay Arya, Karthikeyan Shanmugam, Pooja Aggarwal, Qing Wang, Prateeti Mohapatra and Seema Nagar

(Research Track - Short Paper) CODS-COMAD 2020 [ link ]

Enhancing Simple Models by Exploiting What They Already Know

Amit Dhurandhar,Karthikeyan Shanmugam and Ronny Luss

ICML 2020 [ arxiv]

Invariant Risk Minimization Games

Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar

ICML 2020 [ arxiv][ code]

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam,Debarun Bhattacharjya, Nicholas Mattei

AAAI 2020 [ arxiv]

Event Driven Continuous Time Bayesian Networks

Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao,Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian

AAAI 2020 [ pdf ]

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

Murat Kocaoglu*, Amin Jaber*, Karthikeyan Shanmugam*, Elias Bareinboim

NeurIPS 2019. [ link ] (* - Equal Contribution)

Sample Efficient Active Learning of Causal Trees

Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler

NeurIPS 2019. [ link ]

Differentially Private Distributed Data Summarization under Covariate Shift

Kanthi Sarpatwar*, Karthikeyan Shanmugam*, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculin

NeurIPS 2019. [ arxiv] (* - Equal Contribution)

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

R. Agrawal, C.Squires, K. Yang, K. Shanmugam and C. Uhler

AISTATS 2019. [ arxiv]

Size of Interventional Markov Equivalence Classes in random DAG models

D. Katz, K. Shanmugam, C. Squires and C. Uhler

AISTATS 2019. [ arxiv]

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

T. Chen, J. Navratil, V. Iyengar and K Shanmugam

AISTATS 2019. [ arxiv]

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

A. Dhurandhar, P. Chen, R. Luss, C. Tu, P. Ting, K. Shanmugam, P. Das

Neural Information Processing Systems 2018. [ arxiv]

Improving Simple Models with Confidence Profiles

A. Dhurandhar*, K. Shanmugam*, R. Luss and P. Olsen

Neural Information Processing Systems 2018. [ arxiv] (* - Equal Contribution)

Contextual Bandits with Stochastic Experts

R. Sen, K. Shanmugam and S. Shakkottai

AISTATS 2018. [arxiv]

Model-Powered Conditional Independence Test

R. Sen, A.T. Suresh, K. Shanmugam, Sanjay Shakkottai and Alex Dimakis

Neural Information Processing Systems 2017. [ arxiv][ code ]

Experimental Design for Learning Causal Graphs with Latent Variables

M. Kocaoglu, K. Shanmugam and Elias Bareinboim

Neural Information Processing Systems 2017.[Full Version ]

Identifying Best Interventions through Online Importance Sampling

Rajat Sen, K.Shanmugam, Alex Dimakis and Sanjay Shakkottai

accepted to ICML 2017. [ arxiv]

Contextual Bandits with Latent Confounders: An NMF Approach

Rajat Sen, K.Shanmugam, M. Kocaoglu Alex Dimakis and Sanjay Shakkottai

AISTATS 2017. [ arxiv]

Distributed Estimation of Graph 4-Profiles

E.R.Elenberg, K.Shanmugam, M.Borokhovich, and A.G.Dimakis.

World Wide Web Conference(WWW) 2016 [ arxiv][ code].

Learning Causal Graphs with Small Interventions

K. Shanmugam*, M. Kocaoglu*, A.G.Dimakis and S. Vishwanath

Neural Information Processing Systems, 2015. [ arxiv] ( * - equally contributing student authors )

Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs

E.R.Elenberg, K.Shanmugam, M.Borokhovich, and A.G.Dimakis

Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015. [ arxiv][ code]

On the Information Theoretic Limits of Learning Ising Models

K. Shanmugam*, R. Tandon*, A. G. Dimakis, P. Ravikumar

Neural Information Processing Systems, 2014. [ arxiv ] ( * - equally contributing student authors )

Sparse Polynomial Learning and Graph Sketching

M. Kocaoglu*, K. Shanmugam*, A. G. Dimakis, A. Klivans

Neural Information Processing Systems, 2014. (Full Oral Presentation) [ arxiv] ( * - equally contributing student authors )



Information and Coding Theory

Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs

K. Shanmugam, Antonia M. Tulino and Alex Dimakis

accepted to ISIT 2017 [ arxiv]

On approximating the sum-rate for multiple unicasts

K. Shanmugam, M. Asteris and A.G. Dimakis

International Symposium on Information Theory, ISIT 2015. [ arxiv ]

An Efficient Multiple-Groupcast Coded Multicasting Scheme for Finite Fractional Caching

M.Ji, K.Shanmugam, G.Vettigli, J.Llorca, A.Tulino and G.Caire

IEEE International Conference on Communications, ICC 2015. [link]

Finite Length Analysis of Caching-Aided Coded Multicasting

K. Shanmugam, M. Ji, A.M. Tulino, J. Llorca and A.G. Dimakis

in 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2014 (invited) [link]

Bounding Multiple Unicasts through Index Coding and Locally Repairable Codes

K. Shanmugam and A.G. Dimakis

International Symposium on Information Theory (ISIT 2014). [ arxiv ]

Graph Theory versus Minimum Rank for Index Coding

K. Shanmugam, A.G. Dimakis and M. Langberg

International Symposium on Information Theory (ISIT 2014). [ arxiv ]

Index Coding Problem with Side Information Repositories

K. Shanmugam, A.G. Dimakis and G. Caire

51st Annual Allerton Conference on Communications, Control and Computing, Monticello, Illinois, 2013. [ extended arxiv version ]

Local Graph Coloring and Index Coding

K. Shanmugam, A.G. Dimakis and M. Langberg

International Symposium on Information Theory (ISIT 2013), Istanbul, 2013. [ extended arxiv version ]

A Repair Framework for Scalar MDS Codes (C1)

K. Shanmugam, D.S. Papailiopoulos, A.G. Dimakis and G. Caire

in 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012 [ conf version ]

Wireless downloading delay under proportional fair scheduling with coupled service and requests: An approximated analysis

K. Shanmugam and G. Caire

in IEEE International Symposium on Information Theory Proceedings (ISIT), Boston, 2012 [ conf version ]

FemtoCaching: Wireless video content delivery through distributed caching helpers(C2)

N. Golrezaei, K. Shanmugam, A.G. Dimakis, A.F. Molisch and G. Caire

in Proceedings of IEEE INFOCOM, 2012 [ conf version ]

Wireless Video Content Delivery through Coded Distributed Caching(C3)

N. Golrezaei, K. Shanmugam, A.G. Dimakis, A.F. Molisch and G. Caire

in IEEE International Conference on Communications (ICC) 2012. [ conf version ]



Undergraduate stuff

Rate Gap Analysis for Rate-adaptive Antenna Selection and Beamforming Schemes

K. Shanmugam and S. Bhashyam

in the Proceedings of IEEE GLOBECOM 2010, Miami, FL, USA, Dec 2010. [ conf version ]

Enterprise Communications Platform Support for Integrated Location-Based Applications

J. Buford, Xiaotao Wu, R. Bajpai, S. Karthikeyan and V. Krishnaswamy

in The Second International Conference on Next Generation Mobile Applications, Services and Technologies, 2008. NGMAST 2008. [ conf version ]