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2020 – today
- 2023
[i62]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR abs/2301.07040 (2023)- [i61]Burak Varici, Emre Acarturk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer:
Score-based Causal Representation Learning with Interventions. CoRR abs/2301.08230 (2023) - [i60]Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai:
InfoNCE Loss Provably Learns Cluster-Preserving Representations. CoRR abs/2302.07920 (2023) - 2022
- [c66]Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das:
Fourier Representations for Black-Box Optimization over Categorical Variables. AAAI 2022: 10156-10165 - [c65]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: Impact and Design. AAAI 2022: 12651-12657 - [c64]Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja:
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. AISTATS 2022: 5538-5562 - [c63]Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Dharmashankar Subramanian:
Process Independence Testing in Proximal Graphical Event Models. CLeaR 2022: 144-161 - [c62]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferable Representations. ICLR 2022 - [c61]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference 2022: 276-285 - [c60]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Intervention target estimation in the presence of latent variables. UAI 2022: 2013-2023 - [i59]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferrable Representations. CoRR abs/2202.01011 (2022) - [i58]Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das:
Fourier Representations for Black-Box Optimization over Categorical Variables. CoRR abs/2202.03712 (2022) - [i57]Advait Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai:
PAC Generalization via Invariant Representations. CoRR abs/2205.15196 (2022) - [i56]Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam:
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR abs/2207.07174 (2022) - [i55]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Causal Bandits for Linear Structural Equation Models. CoRR abs/2208.12764 (2022) - 2021
- [j7]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri
, Kush R. Varshney:
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23(12): 1571 (2021) - [c59]Qing Wang, Larisa Shwartz, Genady Ya. Grabarnik, Vijay Arya, Karthikeyan Shanmugam:
Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models. CLOUD 2021: 558-565 - [c58]Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar:
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. AISTATS 2021: 1270-1278 - [c57]Kristjan H. Greenewald, Karthikeyan Shanmugam, Dmitriy A. Katz-Rogozhnikov:
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. AISTATS 2021: 2224-2232 - [c56]Vijay Arya, Karthikeyan Shanmugam, Pooja Aggarwal, Qing Wang, Prateeti Mohapatra, Seema Nagar:
Evaluation of Causal Inference Techniques for AIOps. COMAD/CODS 2021: 188-192 - [c55]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360 Toolkit. COMAD/CODS 2021: 376-379 - [c54]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP 2021: 5005-5009 - [c53]Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney:
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR 2021 - [c52]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu:
Leveraging Latent Features for Local Explanations. KDD 2021: 1139-1149 - [c51]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. NeurIPS 2021: 1494-1505 - [c50]Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. NeurIPS 2021: 21440-21452 - [c49]Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney:
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS 2021: 21668-21680 - [c48]Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu:
Conditionally independent data generation. UAI 2021: 2050-2060 - [i54]Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants. CoRR abs/2102.01567 (2021) - [i53]Kanthi K. Sarpatwar, Karthik Nandakumar, Nalini K. Ratha, James T. Rayfield, Karthikeyan Shanmugam, Sharath Pankanti, Roman Vaculín:
Efficient Encrypted Inference on Ensembles of Decision Trees. CoRR abs/2103.03411 (2021) - [i52]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation using Invariant Risk Minimization. CoRR abs/2103.07788 (2021) - [i51]Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja:
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. CoRR abs/2106.11560 (2021) - [i50]Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. CoRR abs/2106.12729 (2021) - [i49]Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai:
Episodic Bandits with Stochastic Experts. CoRR abs/2107.03263 (2021) - [i48]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: Impact and Design. CoRR abs/2109.12151 (2021) - [i47]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. CoRR abs/2111.07512 (2021) - 2020
- [j6]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21: 130:1-130:6 (2020) - [c47]Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian:
Event-Driven Continuous Time Bayesian Networks. AAAI 2020: 3259-3266 - [c46]Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei:
A Multi-Channel Neural Graphical Event Model with Negative Evidence. AAAI 2020: 3946-3953 - [c45]Kanthi K. Sarpatwar, Nalini K. Ratha, Karthik Nandakumar, Karthikeyan Shanmugam, James T. Rayfield, Sharath Pankanti, Roman Vaculín:
Privacy Enhanced Decision Tree Inference. CVPR Workshops 2020: 154-159 - [c44]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI explainability 360: hands-on tutorial. FAT* 2020: 696 - [c43]Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar:
Invariant Risk Minimization Games. ICML 2020: 145-155 - [c42]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss:
Enhancing Simple Models by Exploiting What They Already Know. ICML 2020: 2525-2534 - [c41]Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan:
Combinatorial Black-Box Optimization with Expert Advice. KDD 2020: 1918-1927 - [c40]Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. NeurIPS 2020 - [c39]Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai:
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. NeurIPS 2020 - [c38]Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. NeurIPS 2020 - [c37]Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar:
Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS 2020 - [c36]Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam:
Active Structure Learning of Causal DAGs via Directed Clique Trees. NeurIPS 2020 - [c35]Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Lingzhou Xue:
Hawkesian Graphical Event Models. PGM 2020: 569-580 - [i46]Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes. CoRR abs/2002.00874 (2020) - [i45]Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar:
Invariant Risk Minimization Games. CoRR abs/2002.04692 (2020) - [i44]Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar:
Learning Global Transparent Models from Local Contrastive Explanations. CoRR abs/2002.08247 (2020) - [i43]Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai:
Warm Starting Bandits with Side Information from Confounded Data. CoRR abs/2002.08405 (2020) - [i42]Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei:
A Multi-Channel Neural Graphical Event Model with Negative Evidence. CoRR abs/2002.09575 (2020) - [i41]Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan:
Combinatorial Black-Box Optimization with Expert Advice. CoRR abs/2006.03963 (2020) - [i40]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Fair Data Integration. CoRR abs/2006.06053 (2020) - [i39]Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar:
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. CoRR abs/2010.15234 (2020) - [i38]Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney:
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR abs/2010.16412 (2020) - [i37]Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam:
Active Structure Learning of Causal DAGs via Directed Clique Tree. CoRR abs/2011.00641 (2020) - [i36]Advait Parulekar, Soumya Basu, Aditya Gopalan, Karthikeyan Shanmugam, Sanjay Shakkottai:
Stochastic Linear Bandits with Protected Subspace. CoRR abs/2011.01016 (2020)
2010 – 2019
- 2019
- [j5]Giuseppe Vettigli, Mingyue Ji, Karthikeyan Shanmugam, Jaime Llorca, Antonia Maria Tulino, Giuseppe Caire:
Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks. Entropy 21(3): 324 (2019) - [c34]Tongfei Chen, Jirí Navrátil, Vijay S. Iyengar, Karthikeyan Shanmugam:
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. AISTATS 2019: 1467-1475 - [c33]Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler:
Size of Interventional Markov Equivalence Classes in random DAG models. AISTATS 2019: 3234-3243 - [c32]Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler:
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. AISTATS 2019: 3400-3409 - [c31]Kanthi K. Sarpatwar, Venkata Sitaramagiridharganesh Ganapavarapu, Karthikeyan Shanmugam, Akond Rahman, Roman Vaculín:
Blockchain Enabled AI Marketplace: The Price You Pay for Trust. CVPR Workshops 2019: 2857-2866 - [c30]Kristjan H. Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adserà, Guy Bresler:
Sample Efficient Active Learning of Causal Trees. NeurIPS 2019: 14279-14289 - [c29]Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim:
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. NeurIPS 2019: 14346-14356 - [c28]Kanthi K. Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculín:
Differentially Private Distributed Data Summarization under Covariate Shift. NeurIPS 2019: 14432-14442 - [i35]Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler:
Size of Interventional Markov Equivalence Classes in Random DAG Models. CoRR abs/1903.02054 (2019) - [i34]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu:
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR abs/1905.12698 (2019) - [i33]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss:
Leveraging Simple Model Predictions for Enhancing its Performance. CoRR abs/1905.13565 (2019) - [i32]Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri:
Model Agnostic Contrastive Explanations for Structured Data. CoRR abs/1906.00117 (2019) - [i31]Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai:
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. CoRR abs/1907.10154 (2019) - [i30]Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR abs/1909.03012 (2019) - [i29]Kanthi K. Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculín:
Differentially Private Distributed Data Summarization under Covariate Shift. CoRR abs/1910.12832 (2019) - 2018
- [c27]Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai:
Contextual Bandits with Stochastic Experts. AISTATS 2018: 852-861 - [c26]Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. NeurIPS 2018: 590-601 - [c25]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen:
Improving Simple Models with Confidence Profiles. NeurIPS 2018: 10317-10327 - [i28]Yitao Chen, Karthikeyan Shanmugam, Alexandros G. Dimakis:
From Centralized to Decentralized Coded Caching. CoRR abs/1801.07734 (2018) - [i27]Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. CoRR abs/1802.07623 (2018) - [i26]Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai:
Contextual Bandits with Stochastic Experts. CoRR abs/1802.08737 (2018) - [i25]Tongfei Chen, Jirí Navrátil, Vijay S. Iyengar, Karthikeyan Shanmugam:
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. CoRR abs/1805.05396 (2018) - [i24]Bernat Guillen Pegueroles, Bhanukiran Vinzamuri, Karthikeyan Shanmugam, Steve Hedden, Jonathan D. Moyer, Kush R. Varshney:
Structure Learning from Time Series with False Discovery Control. CoRR abs/1805.09909 (2018) - [i23]Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, Sreeram Kannan:
Mimic and Classify : A meta-algorithm for Conditional Independence Testing. CoRR abs/1806.09708 (2018) - [i22]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen:
Improving Simple Models with Confidence Profiles. CoRR abs/1807.07506 (2018) - 2017
- [c24]Karthikeyan Shanmugam, Alexandros G. Dimakis, Jaime Llorca, Antonia Maria Tulino:
A unified Ruzsa-Szemerédi framework for finite-length coded caching. ACSSC 2017: 631-635 - [c23]Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai:
Contextual Bandits with Latent Confounders: An NMF Approach. AISTATS 2017: 518-527 - [c22]Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Identifying Best Interventions through Online Importance Sampling. ICML 2017: 3057-3066 - [c21]Karthikeyan Shanmugam, Antonia Maria Tulino, Alexandros G. Dimakis:
Coded caching with linear subpacketization is possible using Ruzsa-Szeméredi graphs. ISIT 2017: 1237-1241 - [c20]Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Model-Powered Conditional Independence Test. NIPS 2017: 2951-2961 - [c19]Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Experimental Design for Learning Causal Graphs with Latent Variables. NIPS 2017: 7018-7028 - [i21]Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Causal Best Intervention Identification via Importance Sampling. CoRR abs/1701.02789 (2017) - [i20]Karthikeyan Shanmugam, Antonia Maria Tulino, Alexandros G. Dimakis:
Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs. CoRR abs/1701.07115 (2017) - [i19]Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sujay Sanghavi:
Sparse Quadratic Logistic Regression in Sub-quadratic Time. CoRR abs/1703.02682 (2017) - [i18]Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam:
TIP: Typifying the Interpretability of Procedures. CoRR abs/1706.02952 (2017) - [i17]Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam:
A Formal Framework to Characterize Interpretability of Procedures. CoRR abs/1707.03886 (2017) - [i16]Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai:
Model-Powered Conditional Independence Test. CoRR abs/1709.06138 (2017) - 2016
- [j4]Karthikeyan Shanmugam, Mingyue Ji, Antonia Maria Tulino, Jaime Llorca, Alexandros G. Dimakis:
Finite-Length Analysis of Caching-Aided Coded Multicasting. IEEE Trans. Inf. Theory 62(10): 5524-5537 (2016) - [c18]Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis:
Distributed Estimation of Graph 4-Profiles. WWW 2016: 483-493 - [i15]Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai:
Latent Contextual Bandits: A Non-Negative Matrix Factorization Approach. CoRR abs/1606.00119 (2016) - 2015
- [c17]Mingyue Ji, Karthikeyan Shanmugam, Giuseppe Vettigli, Jaime Llorca, Antonia Maria Tulino, Giuseppe Caire:
An efficient multiple-groupcast coded multicasting scheme for finite fractional caching. ICC 2015: 3801-3806 - [c16]Karthikeyan Shanmugam, Megasthenis Asteris, Alexandros G. Dimakis:
On approximating the sum-rate for multiple-unicasts. ISIT 2015: 381-385 - [c15]Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis:
Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs. KDD 2015: 229-238 - [c14]Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath:
Learning Causal Graphs with Small Interventions. NIPS 2015: 3195-3203 - [i14]Karthikeyan Shanmugam, Megasthenis Asteris, Alexandros G. Dimakis:
On Approximating the Sum-Rate for Multiple-Unicasts. CoRR abs/1504.05294 (2015) - [i13]Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis:
Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs. CoRR abs/1506.06671 (2015) - [i12]Karthikeyan Shanmugam, Mingyue Ji, Antonia Maria Tulino, Jaime Llorca, Alexandros G. Dimakis:
Finite Length Analysis of Caching-Aided Coded Multicasting. CoRR abs/1508.05175 (2015) - [i11]Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis:
Distributed Estimation of Graph 4-Profiles. CoRR abs/1510.02215 (2015) - [i10]Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath:
Learning Causal Graphs with Small Interventions. CoRR abs/1511.00041 (2015) - [i9]Mingyue Ji, Karthikeyan Shanmugam, Giuseppe Vettigli, Jaime Llorca, Antonia Maria Tulino, Giuseppe Caire:
An Efficient Multiple-Groupcast Coded Multicasting Scheme for Finite Fractional Caching. CoRR abs/1511.07539 (2015) - 2014
- [j3]Karthikeyan Shanmugam, Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Giuseppe Caire:
A Repair Framework for Scalar MDS Codes. IEEE J. Sel. Areas Commun. 32(5): 998-1007 (2014) - [c13]Karthikeyan Shanmugam, Mingyue Ji, Antonia Maria Tulino, Jaime Llorca, Alexandros G. Dimakis:
Finite length analysis of caching-aided coded multicasting. Allerton 2014: 914-920 - [c12]Karthikeyan Shanmugam, Alexandros G. Dimakis, Michael Langberg:
Graph theory versus minimum rank for index coding. ISIT 2014: 291-295 - [c11]Karthikeyan Shanmugam, Alexandros G. Dimakis:
Bounding multiple unicasts through index coding and Locally Repairable Codes. ISIT 2014: 296-300 - [c10]Rashish Tandon, Karthikeyan Shanmugam, Pradeep Ravikumar, Alexandros G. Dimakis:
On the Information Theoretic Limits of Learning Ising Models. NIPS 2014: 2303-2311 - [c9]Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam R. Klivans:
Sparse Polynomial Learning and Graph Sketching. NIPS 2014: 3122-3130 - [i8]Karthikeyan Shanmugam, Alexandros G. Dimakis:
Bounding Multiple Unicasts through Index Coding and Locally Repairable Codes. CoRR abs/1402.3895 (2014) - [i7]Karthikeyan Shanmugam, Alexandros G. Dimakis, Michael Langberg:
Graph Theory versus Minimum Rank for Index Coding. CoRR abs/1402.3898 (2014) - [i6]Alexandros G. Dimakis, Adam R. Klivans, Murat Kocaoglu, Karthikeyan Shanmugam:
A Smoothed Analysis for Learning Sparse Polynomials. CoRR abs/1402.3902 (2014) - [i5]Karthikeyan Shanmugam, Rashish Tandon, Alexandros G. Dimakis, Pradeep Ravikumar:
On the Information Theoretic Limits of Learning Ising Models. CoRR abs/1411.1434 (2014) - 2013
- [j2]Karthikeyan Shanmugam, Negin Golrezaei, Alexandros G. Dimakis, Andreas F. Molisch, Giuseppe Caire:
FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers. IEEE Trans. Inf. Theory 59(12): 8402-8413 (2013) - [c8]Karthikeyan Shanmugam, Alexandros G. Dimakis, Giuseppe Caire:
Index coding problem with side information repositories. Allerton 2013: 1525-1530 - [c7]Karthikeyan Shanmugam, Alexandros G. Dimakis, Michael Langberg:
Local graph coloring and index coding. ISIT 2013: 1152-1156 - [i4]