Harikrishna Narasimhan (Hari)


I am a Senior Research Scientist at Google Research in Mountain View, USA. I work on machine learning and its applications.

Prior to joining Google, I was a post-doctoral researcher at
SEAS, Harvard University from 2015-2018, working with David C. Parkes. I completed my PhD in Computer Science from the Indian Institute of Science (IISc), Bangalore during 2012-2015, working with Shivani Agarwal, and supported by a Google India PhD Fellowship. During the summer of 2014, I interned at Microsoft Research, Bangalore with Prateek Jain.

You can contact me at h__________@google.com (please replace the dashes with my last name).

My research interests broadly lie in the areas of supervised learning, statistical learning theory, and optimization. The following are some themes I have been excited about:

  • Efficient inference for large language models (LLMs)
  • Algorithmic fairness
  • Learning with real-world objectives and constraints
  • Machine learning for economic design

You can find my resume here (updated on 01/31/2023).

For information about my personal hobbies, feel free to check out my instagram page. Of late, I've taken to baking; here are some recent rainbow cakes I made for pride 🌈.



News/Highlights


👉 Invited talk on "Learning to abstain and beyond" at the 1st AAAI Workshop on Deployable AI (DAI), 2023. [slides]

👉 Co-organized tutorial on "Deep AUC Maximization: From Algorithms to Practice" at CVPR 2022.

👉 Co-authored Google blogpost on "Setting Fairness Goals with the TensorFlow Constrained Optimization Library".




Professional Service


Senior Area Chair: NeurIPS 2024

Area Chair: ICML 2023, 2024; NeurIPS 2021, 2022

Conference Reviewing: NeurIPS 2018, 2019, 2020; ICML 2020; ICLR 2021, 2022, 2023, 2024; FAccT 2021, 2022

Journal Reviewing: JMLR, TPAMI, TEAC, TMLR, JAIR, TKDE, Artificial Intelligence, Pattern Recognition Letters




Journal Publications & Book Chapter


Duetting, P., Feng, Z., Narasimhan, H., Parkes, D.C. and Ravindranath, S.S. Optimal Auctions through Deep Learning: Advances in Differentiable Economics.
Journal of the ACM (JACM), 71(1): 1-53, 2024.
[arXiv:2206.06479]

Narasimhan, H., Ramaswamy, H.G., Tavker, S.K., Khurana, D., Netrapalli, P. and Agarwal, S. Consistent Multiclass Algorithms for Complex Metrics and Constraints.
Journal of Machine Learning Research (JMLR), 2023. Accepted with minor revision.
[arXiv:1706.03459]

Duetting, P., Feng, Z., Narasimhan, H., Parkes, D.C. and Ravindranath, S.S. Optimal Auctions through Deep Learning.
Invited Research Highlight, Communications of the ACM (CACM), 64(8):109-116, 2021.

Duetting, P., Feng, Z., Narasimhan, H., Parkes, D.C. and Ravindranath, S.S. Machine Learning for Optimal Economic Design.
The Future of Economic Design, Springer, 2019.

Narasimhan, H. and Agarwal, S. Support Vector Algorithms for Optimizing the Partial Area Under the ROC curve.
Neural Computation, 29(7):1919-1963, 2017.

Majumder, B., Baraneedharan, U., Thiyagarajan, S., Radhakrishnan, P., Narasimhan, H., Dhandapani, M., Brijwani, N., Pinto, D.D., Prasath, A., Shanthappa, B.U., Thayakumar, A., Surendran, R., Babu, G., Shenoy, A.M., Kuriakose, M.A., Bergthold, G., Horowitz, P., Loda, M., Beroukhim, R., Agarwal, S., Sengupta, S., Sundaram, M. and Majumder, P.K. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.
Nature Communications, 6:6169, 2015.
[paper] [Featured in Business Wire, Genome Web, CIO, The Telegraph]




Preprints


Lukasik, M., Narasimhan, H., Menon, A.K., Yu, F. and Kumar, S. Metric-aware LLM inference for regression and scoring. Manuscript, 2024.
[arXiv:2403.04182]

Cotter, A., Menon, A.K., Narasimhan, H., Rawat, A.S., Reddi, S.J. and Zhou, Y. Distilling Double Descent. Manuscript, 2021.
[arXiv:2102.06849]




Conference Publications


2024

Narasimhan, H., Menon, A.K., Jitkrittum, W., Gupta, N., and Kumar, S. Learning to Reject Meets Long-tail Learning. In the 12th International Conference on Learning Representations (ICLR), 2024. To appear.
Spotlight presentation.

Gupta, N., Narasimhan, H., Jitkrittum, W., Rawat, A.S., Menon, A.K., and Kumar, S. Language Model Cascades: Token-Level Uncertainty And Beyond. In the 12th International Conference on Learning Representations (ICLR), 2024. To appear.

Narasimhan, H., Menon, A.K., Jitkrittum, W., and Kumar, S. Post-hoc Estimators for Selective Classification and OOD Detection. In the 12th International Conference on Learning Representations (ICLR), 2024. To appear.
[arXiv:2301.12386]


2023

Jitkrittum, W., Gupta, N., Menon, A.K., Narasimhan, H., Rawat, A.S., Kumar, S. When Does Confidence-Based Cascade Deferral Suffice? In Advances in Neural Information Processing Systems (NeurIPS), 2023.

Wang, S., Narasimhan, N., Zhou, Y., Hooker, S., Lukasik, M., Menon, A.K. Robust Distillation for Worst-class Performance. In the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

Wei, J., Narasimhan, H., Amid, E., Chu, W.-S., Liu, Y., and Kumar, A. Distributionally Robust Post-hoc Classifiers under Prior Shifts. In the 11th International Conference on Learning Representations (ICLR), 2023.


2022

Narasimhan, H., Menon, A.K., Jitkrittum, W., Rawat, A. S. and Kumar, S. Post-hoc Estimators for Learning to Defer to an Expert. In Advances in Neural Information Processing Systems (NeurIPS), 2022.

Hiranandani, G., Mathur, J., Narasimhan, H., and Koyejo, O. Quadratic Metric Elicitation for Fairness and Beyond. In the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
Oral presentation.
[arXiv:2011.01516]

Jiang, H., Narasimhan, H., Bahri, D., Cotter, A. and Rostamizadeh, A. Churn Reduction via Distillation. In the 10th International Conference on Learning Representations (ICLR), 2022.
Spotlight presentation.
[arXiv:2106.02654]


2021

Narasimhan, H. and Menon, A.K. Training Over-parameterized Models with Non-decomposable Metrics. In Advances in Neural Information Processing Systems (NeurIPS), 2021.

Hiranandani, G., Mathur, J., Narasimhan, H., Fard, M.M. amd Koyejo, O. Optimizing Black-box Metrics with Iterative Example Weighting. In the 38th International Conference on Machine Learning (ICML), 2021.

Kumar, A., Narasimhan, H., and Cotter, A. Implicit Rate-constrained Optimization of Non-decomposable Objectives. In the 38th International Conference on Machine Learning (ICML), 2021.


2020

Wang, S., Guo, W., Narasimhan, H., Cotter, A., Gupta, M. and Jordan, M.I. Robust Optimization for Fairness with Noisy Protected Groups. In Advances in Neural Information Processing Systems (NeurIPS), 2020.

Narasimhan, H., Cotter, A., Zhou, Y., Wang, S., Guo, W. Approximate Heavily-constrained Learning with Lagrange Multiplier Models. In Advances in Neural Information Processing Systems (NeurIPS), 2020.

Hiranandani, G., Narasimhan, H., and Koyejo, O. Fair Performance Metric Elicitation. In Advances in Neural Information Processing Systems (NeurIPS), 2020.

Tavker, S.K., Ramaswamy, H.G., and Narasimhan, H. Consistent Plug-in Classifiers for Complex Objectives and Constraints. In Advances in Neural Information Processing Systems (NeurIPS), 2020.

Jiang, Q., Adigun, O., Narasimhan, H., Fard, M.M., and Gupta M. Optimizing Black-box Metrics with Adaptive Surrogates. In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.

Narasimhan, H., Cotter, A., Gupta, M., and Wang, S. Pairwise Fairness for Ranking and Regression. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.


2019

Narasimhan, H., Cotter, A., and Gupta, M. Optimizing Generalized Rate Metrics with Three Players. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
Oral presentation.

Cotter, A., Narasimhan, H., and Gupta, M. On Making Stochastic Classifiers Deterministic. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
Oral presentation.

Zhao, S., Fard, M.M., Narasimhan, H. and Gupta, M. Metric-optimized Example Weights. In Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.

Duetting, P., Feng, Z., Narasimhan, H., Parkes, D.C. and Ravindranath, S.S. Optimal Auctions through Deep Learning. In Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
Oral presentation.


2018

Narasimhan, H. Learning with Complex Loss Functions and Constraints. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
Spotlight presentation.

Golowich, N., Narasimhan, H. and Parkes, D.C. Deep Learning for Multi-Facility Location Mechanism Design. In Proceedings of the 27th International Joint Con-ference on Artificial Intelligence (IJCAI), 2018.

Feng, Z., Narasimhan, H. and Parkes, D.C. Deep Learning for Revenue-Optimal Auctions with Budgets. In Proceedings of the 17th International Conference onAutonomous Agents and Multiagent Systems (AAMAS), 2018.


2016

Narasimhan, H., Pan, W., Kar, P., Protopapas, P. and Ramaswamy, H.G. 'Optimizing the multiclass F-measure via biconcave programming'. In Proceedings of the 16th IEEE International Conference on Data Mining (ICDM). 2016.

Kar, P., Li, S., Narasimhan, H., Chawla, S. and Sebastiani, F. 'Online optimization methods for the quantification problem'. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge, Discovery and Data Mining (KDD), 2016.

Narasimhan, H., Agarwal, S. and Parkes, D.C. 'Automated Mechanism Design without Money via Machine Learning'. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.

Narasimhan, H., and Parkes, D.C. 'A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms'. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2016.


2015

Narasimhan, H., Parkes, D.C. and Singer, Y. 'Learnability of influence in networks'. In Advances in Neural Information Processing Systems (NIPS), 2015.

Ahmed, S., Narasimhan, H. and Agarwal, S. 'Bayes optimal feature selection for supervised learning with general performance measures'. In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015.

Narasimhan, H.*, Ramaswamy, H.G.*, Saha, A. and Agarwal, S. 'Consistent multiclass algorithms for complex performance measures'. In Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.
(*both authors contributed equally to the paper)

Narasimhan, H., Kar, P., and Jain, P. 'Optimizing non-decomposable performance measures: A tale of two classes'. In Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.

Kar, P., Narasimhan, H., and Jain, P. 'Surrogate functions for maximizing precision at the top'. In Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.


2014

Narasimhan, H.*, Vaish, R.* and Agarwal, S., 'On the statistical consistency of plug-in classifiers for non-decomposable performance measures'. In Advances in Neural Information Processing Systems (NIPS), 2014.
(*both authors contributed equally to the paper)

Kar, P., Narasimhan, H., and Jain, P. 'Online and stochastic gradient methods for non-decomposable loss functions'. In Advances in Neural Information Processing Systems (NIPS), 2014.

Saha, A., Dewangan, C., Narasimhan, H., Sriram, S., and Agarwal, S. 'Learning score systems for patient mortality prediction in intensive care units via orthogonal matching pursuit'. In Proceedings of the 13th International Conference on Machine Learning and Applications (ICMLA), 2014.

Agarwal, A., Narasimhan, H., Kalyanakrishnan, S. and Agarwal, S., 'GEV-canonical regression for accurate binary class probability estimation when one class is rare'. In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.


2013

Narasimhan, H. and Agarwal, S., 'On the relationship between binary classification, bipartite ranking, and binary class probability estimation'. In Advances in Neural Information Processing Systems (NIPS), 2013.
Spotlight presentation.

Narasimhan, H. and Agarwal, S., 'SVM_pAUC^tight: A new support vector method for optimizing partial AUC based on a tight convex upper bound'. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge, Discovery and Data Mining (KDD), 2013.

Menon, A. K., Narasimhan, H., Agarwal, S. and Chawla, S., 'On the statistical consistency of algorithms for binary classification under class imbalance'. In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
[paper] [supplementary material]

Narasimhan, H. and Agarwal, S., 'A structural SVM based approach for optimizing partial AUC'. In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
[paper] [supplementary material]