Karthikeyan Shanmugam

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 136

Top Venues

Publications

Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
Score-based Causal Representation Learning: Linear and General Transformations. CoRR (2024)
Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy, Aravindan Raghuveer
Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples. AAAI (2024)
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
Linear Causal Representation Learning from Unknown Multi-node Interventions. CoRR (2024)
Harshit Varma, Dheeraj Nagaraj, Karthikeyan Shanmugam
Glauber Generative Model: Discrete Diffusion Models via Binary Classification. CoRR (2024)
Conor M. Artman, Aditya Mate, Ezinne Nwankwo, Aliza Heching, Tsuyoshi Idé, Jirí Navrátil, Karthikeyan Shanmugam, Wei Sun, Kush R. Varshney, Lauri Goldkind, Gidi Kroch, Jaclyn Sawyer, Ian Watson
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food. CoRR (2024)
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
General Identifiability and Achievability for Causal Representation Learning. AISTATS (2024)
Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy
Learning model uncertainty as variance-minimizing instance weights. ICLR (2024)
Soumyabrata Pal, Milind Tambe, Arun Sai Suggala, Karthikeyan Shanmugam, Aparna Taneja
Improving Mobile Maternal and Child Health Care Programs: Collaborative Bandits for Time Slot Selection. AAMAS (2024)
Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation. ICLR (2024)
Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation. CoRR (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. NeurIPS (2023)
Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge. NeurIPS (2023)
Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy, Aravindan Raghuveer
Improving Fairness-Accuracy tradeoff with few Test Samples under Covariate Shift. CoRR (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Optimal Algorithms for Latent Bandits with Cluster Structure. AISTATS (2023)
Qing Wang, Jesus Rios, Saurabh Jha, Karthikeyan Shanmugam, Frank Bagehorn, Xi Yang, Robert Filepp, Naoki Abe, Larisa Shwartz
Fault Injection Based Interventional Causal Learning for Distributed Applications. AAAI (2023)
Vijayabanu Chidambaram, Karthikeyan Shanmugam, Satyanarayana Parayitam
Parental neglect and emotional wellbeing among adolescent students from India: social network addiction as a mediator and gender as a moderator. Behav. Inf. Technol. 42 (7) (2023)
Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. NeurIPS (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR (2023)
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. CoRR (2023)
Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai Suggala
Optimal Best-Arm Identification in Bandits with Access to Offline Data. CoRR (2023)
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
General Identifiability and Achievability for Causal Representation Learning. CoRR (2023)
Advait U. Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai
PAC Generalization via Invariant Representations. ICML (2023)
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai
InfoNCE Loss Provably Learns Cluster-Preserving Representations. COLT (2023)
Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge. CoRR (2023)
Burak Varici, Emre Acarturk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
Score-based Causal Representation Learning with Interventions. CoRR (2023)
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai
InfoNCE Loss Provably Learns Cluster-Preserving Representations. CoRR (2023)
Jiaqi Zhang, Chandler Squires, Kristjan H. Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. CoRR (2023)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Intervention target estimation in the presence of latent variables. UAI (2022)
Advait Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai
PAC Generalization via Invariant Representations. CoRR (2022)
Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. AISTATS (2022)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Causal Bandits for Linear Structural Equation Models. CoRR (2022)
Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das
Fourier Representations for Black-Box Optimization over Categorical Variables. AAAI (2022)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference (2022)
Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Dharmashankar Subramanian
Process Independence Testing in Proximal Graphical Event Models. CLeaR (2022)
Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das
Fourier Representations for Black-Box Optimization over Categorical Variables. CoRR (2022)
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)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferable Representations. ICLR (2022)
Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. NeurIPS (2022)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferrable Representations. CoRR (2022)
Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR (2022)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR (2021)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23 (12) (2021)
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. CoRR (2021)
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 (2021)
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. AISTATS (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation using Invariant Risk Minimization. CoRR (2021)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Scalable Intervention Target Estimation in Linear Models. NeurIPS (2021)
Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu
Conditionally independent data generation. UAI (2021)
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. NeurIPS (2021)
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)
Qing Wang, Larisa Shwartz, Genady Ya. Grabarnik, Vijay Arya, Karthikeyan Shanmugam
Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models. CLOUD (2021)
Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. CoRR (2021)
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 (2021)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
Leveraging Latent Features for Local Explanations. KDD (2021)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Scalable Intervention Target Estimation in Linear Models. CoRR (2021)
Kristjan H. Greenewald, Karthikeyan Shanmugam, Dmitriy A. Katz-Rogozhnikov
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. AISTATS (2021)
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 (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP (2021)
Vijay Arya, Karthikeyan Shanmugam, Pooja Aggarwal, Qing Wang, Prateeti Mohapatra, Seema Nagar
Evaluation of Causal Inference Techniques for AIOps. COMAD/CODS (2021)
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS (2021)
Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai
Episodic Bandits with Stochastic Experts. CoRR (2021)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR (2020)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models from Local Contrastive Explanations. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. ICML (2020)
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)
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes. CoRR (2020)
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 (2020)
Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei
A Multi-Channel Neural Graphical Event Model with Negative Evidence. AAAI (2020)
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)
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)
Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Lingzhou Xue
Hawkesian Graphical Event Models. PGM (2020)
Advait Parulekar, Soumya Basu, Aditya Gopalan, Karthikeyan Shanmugam, Sanjay Shakkottai
Stochastic Linear Bandits with Protected Subspace. CoRR (2020)
Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei
A Multi-Channel Neural Graphical Event Model with Negative Evidence. CoRR (2020)
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)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Enhancing Simple Models by Exploiting What They Already Know. ICML (2020)
Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. NeurIPS (2020)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Fair Data Integration. CoRR (2020)
Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai
Warm Starting Bandits with Side Information from Confounded Data. CoRR (2020)
Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
Active Structure Learning of Causal DAGs via Directed Clique Tree. CoRR (2020)
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)
Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian
Event-Driven Continuous Time Bayesian Networks. AAAI (2020)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. CoRR (2020)
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. NeurIPS (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. CoRR (2020)
Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan
Combinatorial Black-Box Optimization with Expert Advice. CoRR (2020)
Kanthi K. Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculín
Differentially Private Distributed Data Summarization under Covariate Shift. CoRR (2019)
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)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR (2019)
Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. NeurIPS (2019)
Tongfei Chen, Jirí Navrátil, Vijay S. Iyengar, Karthikeyan Shanmugam
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. AISTATS (2019)
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 (2019)
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) (2019)
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler
Size of Interventional Markov Equivalence Classes in random DAG models. AISTATS (2019)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Leveraging Simple Model Predictions for Enhancing its Performance. CoRR (2019)
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler
Size of Interventional Markov Equivalence Classes in Random DAG Models. CoRR (2019)
Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. AISTATS (2019)
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)
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 (2019)
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
Model Agnostic Contrastive Explanations for Structured Data. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Karthikeyan Shanmugam