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ORCIDKarthikeyan Shanmugam
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
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2020 – today
- 2024
[j10]Nihal Sharma
, Rajat Sen, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai:
Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes. ACM Trans. Model. Perform. Evaluation Comput. Syst. 9(3): 12:1-12:33 (2024)- [c79]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: 12331-12339 - [c78]Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer:
General Identifiability and Achievability for Causal Representation Learning. AISTATS 2024: 2314-2322 - [c77]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: 1540-1548 - [c76]Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer:
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation. ICLR 2024 - [c75]Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy:
Learning model uncertainty as variance-minimizing instance weights. ICLR 2024 - [i73]Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer:
Score-based Causal Representation Learning: Linear and General Transformations. CoRR abs/2402.00849 (2024) - [i72]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 abs/2403.10638 (2024) - [i71]Harshit Varma, Dheeraj Nagaraj, Karthikeyan Shanmugam:
Glauber Generative Model: Discrete Diffusion Models via Binary Classification. CoRR abs/2405.17035 (2024) - [i70]Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer:
Linear Causal Representation Learning from Unknown Multi-node Interventions. CoRR abs/2406.05937 (2024) - 2023
- [j9]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): 869-887 (2023) - [j8]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Causal Bandits for Linear Structural Equation Models. J. Mach. Learn. Res. 24: 297:1-297:59 (2023) - [c74]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: 15738-15744 - [c73]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. AISTATS 2023: 7540-7577 - [c72]Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai:
InfoNCE Loss Provably Learns Cluster-Preserving Representations. COLT 2023: 1914-1961 - [c71]Advait U. Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai:
PAC Generalization via Invariant Representations. ICML 2023: 27378-27400 - [c70]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. NeurIPS 2023 - [c69]Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu:
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge. NeurIPS 2023 - [c68]Jiaqi Zhang, Kristjan H. Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler:
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. NeurIPS 2023 - [i69]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR abs/2301.07040 (2023) - [i68]Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer:
Score-based Causal Representation Learning with Interventions. CoRR abs/2301.08230 (2023) - [i67]Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai:
InfoNCE Loss Provably Learns Cluster-Preserving Representations. CoRR abs/2302.07920 (2023) - [i66]Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai Suggala:
Optimal Best-Arm Identification in Bandits with Access to Offline Data. CoRR abs/2306.09048 (2023) - [i65]Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu:
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge. CoRR abs/2306.11008 (2023) - [i64]Jiaqi Zhang, Chandler Squires, Kristjan H. Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler:
Identifiability Guarantees for Causal Disentanglement from Soft Interventions. CoRR abs/2307.06250 (2023) - [i63]Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy, Aravindan Raghuveer:
Improving Fairness-Accuracy tradeoff with few Test Samples under Covariate Shift. CoRR abs/2310.07535 (2023) - [i62]Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam, Aravindan Raghuveer:
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation. CoRR abs/2310.08056 (2023) - [i61]Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer:
General Identifiability and Achievability for Causal Representation Learning. CoRR abs/2310.15450 (2023) - [i60]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. CoRR abs/2311.03376 (2023) - 2022
- [c67]Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das:
Fourier Representations for Black-Box Optimization over Categorical Variables. AAAI 2022: 10156-10165 - [c66]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 - [c65]Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja:
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. AISTATS 2022: 5538-5562 - [c64]Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Dharmashankar Subramanian:
Process Independence Testing in Proximal Graphical Event Models. CLeaR 2022: 144-161 - [c63]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferable Representations. ICLR 2022 - [c62]Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam:
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. NeurIPS 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 M. 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 M. 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.