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ORCIDHarikrishna Narasimhan
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2020 – today
- 2024
[j3]Paul Dütting
, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning: Advances in Differentiable Economics. J. ACM 71(1): 5:1-5:53 (2024)- [c48]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-Level Uncertainty And Beyond. ICLR 2024 - [c47]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar:
Learning to Reject Meets Long-tail Learning. ICLR 2024 - [c46]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Plugin estimators for selective classification with out-of-distribution detection. ICLR 2024 - [i28]Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, Sanjiv Kumar:
Metric-aware LLM inference. CoRR abs/2403.04182 (2024) - [i27]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-level uncertainty and beyond. CoRR abs/2404.10136 (2024) - [i26]Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar:
Faster Cascades via Speculative Decoding. CoRR abs/2405.19261 (2024) - [i25]Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go:
Cascade-Aware Training of Language Models. CoRR abs/2406.00060 (2024) - 2023
- [c45]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. ICLR 2023 - [c44]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? NeurIPS 2023 - [c43]Serena Lutong Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon:
Robust distillation for worst-class performance: on the interplay between teacher and student objectives. UAI 2023: 2237-2247 - [i24]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Learning to reject meets OOD detection: Are all abstentions created equal? CoRR abs/2301.12386 (2023) - [i23]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? CoRR abs/2307.02764 (2023) - [i22]Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar:
Distributionally Robust Post-hoc Classifiers under Prior Shifts. CoRR abs/2309.08825 (2023) - 2022
- [c42]Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh:
Churn Reduction via Distillation. ICLR 2022 - [c41]Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Post-hoc estimators for learning to defer to an expert. NeurIPS 2022 - [c40]Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Quadratic metric elicitation for fairness and beyond. UAI 2022: 811-821 - [i21]Serena Lutong Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon:
Robust Distillation for Worst-class Performance. CoRR abs/2206.06479 (2022) - [i20]Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Metrics and Constraints. CoRR abs/2210.09695 (2022) - 2021
- [j2]Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal auctions through deep learning. Commun. ACM 64(8): 109-116 (2021) - [c39]Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Sanmi Koyejo:
Optimizing Black-box Metrics with Iterative Example Weighting. ICML 2021: 4239-4249 - [c38]Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter:
Implicit rate-constrained optimization of non-decomposable objectives. ICML 2021: 5861-5871 - [c37]Harikrishna Narasimhan, Aditya Krishna Menon:
Training Over-parameterized Models with Non-decomposable Objectives. NeurIPS 2021: 18165-18181 - [i19]Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sashank J. Reddi, Yichen Zhou:
Distilling Double Descent. CoRR abs/2102.06849 (2021) - [i18]Gaurush Hiranandani, Jatin Mathur, Oluwasanmi Koyejo, Mahdi Milani Fard, Harikrishna Narasimhan:
Optimizing Black-box Metrics with Iterative Example Weighting. CoRR abs/2102.09492 (2021) - [i17]Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh:
Churn Reduction via Distillation. CoRR abs/2106.02654 (2021) - [i16]Harikrishna Narasimhan, Aditya Krishna Menon:
Training Over-parameterized Models with Non-decomposable Objectives. CoRR abs/2107.04641 (2021) - [i15]Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter:
Implicit Rate-Constrained Optimization of Non-decomposable Objectives. CoRR abs/2107.10960 (2021) - 2020
- [c36]Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Lutong Wang:
Pairwise Fairness for Ranking and Regression. AAAI 2020: 5248-5255 - [c35]Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta:
Optimizing Black-box Metrics with Adaptive Surrogates. ICML 2020: 4784-4793 - [c34]Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Fair Performance Metric Elicitation. NeurIPS 2020 - [c33]Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Lutong Wang, Wenshuo Guo:
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. NeurIPS 2020 - [c32]Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan:
Consistent Plug-in Classifiers for Complex Objectives and Constraints. NeurIPS 2020 - [c31]Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. NeurIPS 2020 - [i14]Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta:
Optimizing Black-box Metrics with Adaptive Surrogates. CoRR abs/2002.08605 (2020) - [i13]Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. CoRR abs/2002.09343 (2020) - [i12]Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Fair Performance Metric Elicitation. CoRR abs/2006.12732 (2020) - [i11]Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Quadratic Metric Elicitation with Application to Fairness. CoRR abs/2011.01516 (2020)
2010 – 2019
- 2019
- [c30]Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning. ICML 2019: 1706-1715 - [c29]Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya R. Gupta:
Metric-Optimized Example Weights. ICML 2019: 7533-7542 - [c28]Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta:
Optimizing Generalized Rate Metrics with Three Players. NeurIPS 2019: 10746-10757 - [c27]Andrew Cotter, Maya R. Gupta, Harikrishna Narasimhan:
On Making Stochastic Classifiers Deterministic. NeurIPS 2019: 10910-10920 - [i10]Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Lutong Wang:
Pairwise Fairness for Ranking and Regression. CoRR abs/1906.05330 (2019) - [i9]Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta:
Optimizing Generalized Rate Metrics through Game Equilibrium. CoRR abs/1909.02939 (2019) - 2018
- [c26]Harikrishna Narasimhan:
Learning with Complex Loss Functions and Constraints. AISTATS 2018: 1646-1654 - [c25]Zhe Feng, Harikrishna Narasimhan, David C. Parkes:
Deep Learning for Revenue-Optimal Auctions with Budgets. AAMAS 2018: 354-362 - [c24]Noah Golowich, Harikrishna Narasimhan, David C. Parkes:
Deep Learning for Multi-Facility Location Mechanism Design. IJCAI 2018: 261-267 - 2017
- [j1]Harikrishna Narasimhan, Shivani Agarwal:
Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve. Neural Comput. 29(7): 1919-1963 (2017) - [i8]Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes:
Optimal Auctions through Deep Learning. CoRR abs/1706.03459 (2017) - 2016
- [c23]Harikrishna Narasimhan, Weiwei Pan, Purushottam Kar, Pavlos Protopapas, Harish G. Ramaswamy:
Optimizing the Multiclass F-Measure via Biconcave Programming. ICDM 2016: 1101-1106 - [c22]Harikrishna Narasimhan, Shivani Agarwal, David C. Parkes:
Automated Mechanism Design without Money via Machine Learning. IJCAI 2016: 433-439 - [c21]Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani:
Online Optimization Methods for the Quantification Problem. KDD 2016: 1625-1634 - [c20]Harikrishna Narasimhan, David C. Parkes:
A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms. UAI 2016 - [i7]Shuai Li, Harikrishna Narasimhan, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani:
Stochastic Optimization Techniques for Quantification Performance Measures. CoRR abs/1605.04135 (2016) - [i6]Harikrishna Narasimhan, Shivani Agarwal:
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve. CoRR abs/1605.04337 (2016) - 2015
- [c19]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Surrogate Functions for Maximizing Precision at the Top. ICML 2015: 189-198 - [c18]Harikrishna Narasimhan, Purushottam Kar, Prateek Jain:
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. ICML 2015: 199-208 - [c17]Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Performance Measures. ICML 2015: 2398-2407 - [c16]Harikrishna Narasimhan, David C. Parkes, Yaron Singer:
Learnability of Influence in Networks. NIPS 2015: 3186-3194 - [c15]C. G. Saneem Ahmed, Harikrishna Narasimhan, Shivani Agarwal:
Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures. UAI 2015: 171-180 - [i5]Harish G. Ramaswamy, Harikrishna Narasimhan, Shivani Agarwal:
Consistent Classification Algorithms for Multi-class Non-Decomposable Performance Metrics. CoRR abs/1501.00287 (2015) - [i4]Harikrishna Narasimhan, Purushottam Kar, Prateek Jain:
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. CoRR abs/1505.06812 (2015) - [i3]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Surrogate Functions for Maximizing Precision at the Top. CoRR abs/1505.06813 (2015) - 2014
- [c14]Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal:
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare. ICML 2014: 1989-1997 - [c13]Aadirupa Saha, Chandrahas Dewangan, Harikrishna Narasimhan, Sriram Sampath, Shivani Agarwal:
Learning Score Systems for Patient Mortality Prediction in Intensive Care Units via Orthogonal Matching Pursuit. ICMLA 2014: 93-98 - [c12]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. NIPS 2014: 694-702 - [c11]Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal:
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures. NIPS 2014: 1493-1501 - [i2]Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. CoRR abs/1410.6776 (2014) - 2013
- [c10]Harikrishna Narasimhan, Shivani Agarwal:
A Structural SVM Based Approach for Optimizing Partial AUC. ICML (1) 2013: 516-524 - [c9]Aditya Krishna Menon, Harikrishna Narasimhan, Shivani Agarwal, Sanjay Chawla:
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance. ICML (3) 2013: 603-611 - [c8]Harikrishna Narasimhan, Shivani Agarwal:
SVMpAUCtight: a new support vector method for optimizing partial AUC based on a tight convex upper bound. KDD 2013: 167-175 - [c7]Harikrishna Narasimhan, Shivani Agarwal:
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation. NIPS 2013: 2913-2921 - 2010
- [c6]Harikrishna Narasimhan, Sanjeev Satheesh, Dinesh Sriram:
Automatic summarization of cricket video events using genetic algorithm. GECCO (Companion) 2010: 2051-2054 - [c5]Harikrishna Narasimhan, Venkatanathan Varadarajan, C. Pandu Rangan:
Game Theoretic Resistance to Denial of Service Attacks Using Hidden Difficulty Puzzles. ISPEC 2010: 359-376 - [c4]Harikrishna Narasimhan, Venkatanathan Varadarajan, C. Pandu Rangan:
Towards a Cooperative Defense Model Against Network Security Attacks. WEIS 2010
2000 – 2009
- 2009
- [c3]Sanjeev Satheesh, Harikrishna Narasimhan, Pramala Senthil:
Evolving player-specific content for level based arcade games. FDG 2009: 329-330 - [c2]Harikrishna Narasimhan:
Parallel Artificial Bee Colony (PABC) Algorithm. NaBIC 2009: 306-311 - [c1]Harikrishna Narasimhan, Sanjeev Satheesh:
A Randomized Iterative Improvement Algorithm for Photomosaic Generation. NaBIC 2009: 777-781 - [i1]Harikrishna Narasimhan, Venkatanathan Varadarajan, C. Pandu Rangan:
Game Theoretic Resistance to Denial of Service Attacks Using Hidden Difficulty Puzzles. IACR Cryptol. ePrint Arch. 2009: 350 (2009)
Coauthor Index
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