Karthikeyan Shanmugam
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
51
Venues
17
Active years
2010–2026
Best venue rank
A*
Where they publish
Papers
51 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | WACV | Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation. | Aditi Agarwal, Anjali Jain, Nikita Saxena, Ishan Deshpande, Michal Kazmierski, Abigail Annkah, Nadav Sherman, Karthikeyan Shanmugam, Alok Talekar, Vaibhav Rajan |
| 2025 | AISTATS | Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. | Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer |
| 2025 | ICLR | Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? | Sravanti Addepalli, Yerram Varun, Arun Suggala, Karthikeyan Shanmugam, Prateek Jain |
| 2025 | ICLR | Glauber Generative Model: Discrete Diffusion Models via Binary Classification. | Harshit Varma, Dheeraj Mysore Nagaraj, Karthikeyan Shanmugam |
| 2025 | WWW | Online Bidding under RoS Constraints without Knowing the Value. | Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam, Arun Suggala, Di Wang |
| 2024 | AAAI | Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples. | Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy, Aravindan Raghuveer |
| 2024 | AISTATS | General Identifiability and Achievability for Causal Representation Learning. | Burak Varici, Emre Acartrk, Karthikeyan Shanmugam, Ali Tajer |
| 2024 | ICLR | Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation. | Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer |
| 2024 | ICLR | Learning model uncertainty as variance-minimizing instance weights. | Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy |
| 2023 | AAAI | Fault Injection Based Interventional Causal Learning for Distributed Applications. | Qing Wang, Jesus Rios, Saurabh Jha, Karthikeyan Shanmugam, Frank Bagehorn, Xi Yang, Robert Filepp, Naoki Abe, Larisa Shwartz |
| 2023 | AISTATS | Optimal Algorithms for Latent Bandits with Cluster Structure. | Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain |
| 2023 | COLT | InfoNCE Loss Provably Learns Cluster-Preserving Representations. | Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai |
| 2023 | ICML | PAC Generalization via Invariant Representations. | Advait U. Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai |
| 2022 | AAAI | AI Explainability 360: Impact and Design. | 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 |
| 2022 | AAAI | Fourier Representations for Black-Box Optimization over Categorical Variables. | Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das |
| 2022 | AISTATS | Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. | Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja |
| 2022 | ICLR | Auto-Transfer: Learning to Route Transferable Representations. | Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar |
| 2022 | SIGMOD | Causal Feature Selection for Algorithmic Fairness. | Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney |
| 2022 | UAI | Intervention target estimation in the presence of latent variables. | Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer |
| 2021 | AISTATS | Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. | Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar |
| 2021 | AISTATS | High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. | Kristjan H. Greenewald, Karthikeyan Shanmugam, Dmitriy A. Katz |
| 2021 | COMAD | AI Explainability 360 Toolkit. | 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 |
| 2021 | COMAD | Evaluation of Causal Inference Techniques for AIOps. | Vijay Arya, Karthikeyan Shanmugam, Pooja Aggarwal, Qing Wang, Prateeti Mohapatra, Seema Nagar |
| 2021 | ICASSP | Treatment Effect Estimation Using Invariant Risk Minimization. | Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar |
| 2021 | ICLR | Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. | Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney |
| 2021 | KDD | Leveraging Latent Features for Local Explanations. | Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu |
| 2021 | UAI | Conditionally independent data generation. | Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu |
| 2020 | AAAI | Event-Driven Continuous Time Bayesian Networks. | Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush R. Varshney, Dharmashankar Subramanian |
| 2020 | AAAI | A Multi-Channel Neural Graphical Event Model with Negative Evidence. | Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei |
| 2020 | CVPR | Privacy Enhanced Decision Tree Inference. | Kanthi K. Sarpatwar, Nalini K. Ratha, Karthik Nandakumar, Karthikeyan Shanmugam, James T. Rayfield, Sharath Pankanti, Roman Vaculn |
| 2020 | ICML | Invariant Risk Minimization Games. | Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar |
| 2020 | ICML | Enhancing Simple Models by Exploiting What They Already Know. | Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss |
| 2020 | KDD | Combinatorial Black-Box Optimization with Expert Advice. | Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan |
| 2019 | AISTATS | ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. | Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler |
| 2019 | AISTATS | Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. | Tongfei Chen, Jir Navrtil, Vijay S. Iyengar, Karthikeyan Shanmugam |
| 2019 | AISTATS | Size of Interventional Markov Equivalence Classes in random DAG models. | Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler |
| 2019 | CVPR | Blockchain Enabled AI Marketplace: The Price You Pay for Trust. | Kanthi K. Sarpatwar, Venkata Sitaramagiridharganesh Ganapavarapu, Karthikeyan Shanmugam, Akond Rahman, Roman Vaculn |
| 2018 | AISTATS | Contextual Bandits with Stochastic Experts. | Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai |
| 2017 | ACSSC | A unified Ruzsa-Szemerdi framework for finite-length coded caching. | Karthikeyan Shanmugam, Alexandros G. Dimakis, Jaime Llorca, Antonia M. Tulino |
| 2017 | AISTATS | Contextual Bandits with Latent Confounders: An NMF Approach. | Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai |
| 2017 | ICML | Identifying Best Interventions through Online Importance Sampling. | Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai |
| 2017 | ISIT | Coded caching with linear subpacketization is possible using Ruzsa-Szemredi graphs. | Karthikeyan Shanmugam, Antonia M. Tulino, Alexandros G. Dimakis |
| 2016 | WWW | Distributed Estimation of Graph 4-Profiles. | Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis |
| 2015 | ISIT | On approximating the sum-rate for multiple-unicasts. | Karthikeyan Shanmugam, Megasthenis Asteris, Alexandros G. Dimakis |
| 2015 | KDD | Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs. | Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis |
| 2014 | ISIT | Bounding multiple unicasts through index coding and Locally Repairable Codes. | Karthikeyan Shanmugam, Alexandros G. Dimakis |
| 2014 | ISIT | Graph theory versus minimum rank for index coding. | Karthikeyan Shanmugam, Alexandros G. Dimakis, Michael Langberg |
| 2013 | ISIT | Local graph coloring and index coding. | Karthikeyan Shanmugam, Alexandros G. Dimakis, Michael Langberg |
| 2012 | INFOCOM | FemtoCaching: Wireless video content delivery through distributed caching helpers. | Negin Golrezaei, Karthikeyan Shanmugam, Alexandros G. Dimakis, Andreas F. Molisch, Giuseppe Caire |
| 2012 | ISIT | Wireless downloading delay under proportional fair scheduling with coupled service and requests: An approximated analysis. | Karthikeyan Shanmugam, Giuseppe Caire |
| 2010 | GLOBECOM | Rate Gap Analysis for Rate-Adaptive Antenna Selection and Beamforming Schemes. | Karthikeyan Shanmugam, Srikrishna Bhashyam |