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ORCIDShivani Agarwal 0001
Person information
- affiliation: University of Pennsylvania, USA
- affiliation: Indian Institute of Science, Bangalore
Other persons with the same name
- Shivani Agarwal 0002
![[0000-0002-6999-650X]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-6999-650X")
— Ashoka Trust for Research in Ecology and the Environment, Bangalore, India (and 1 more) - Shivani Agarwal 0003 — KIET Group of Institutions, Ghaziabad, India
- Shivani Agarwal 0004 — Chandigarh University, Apex Institute of Technology, Punjab, India
- Shivani Agarwal 0005 — Indira Gandhi Institute of Medical Sciences, India
- Shivani Agarwal 0006 — Duke University, Department of Computer Science, USA
- Shivani Agarwal 0007 — Ajay Kumar Garg Engineering College, Ghaziabad, India
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2020 – today
- 2024
[c45]Mingyuan Zhang, Shivani Agarwal:
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures. AISTATS 2024: 2170-2178- [i8]Mingyuan Zhang, Shivani Agarwal:
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures. CoRR abs/2402.01055 (2024) - 2022
- [i7]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
- [c44]Arpit Agarwal, Shivani Agarwal, Prathamesh Patil:
Stochastic Dueling Bandits with Adversarial Corruption. ALT 2021: 217-248 - [c43]Mingyuan Zhang, Jane H. Lee, Shivani Agarwal:
Learning from Noisy Labels with No Change to the Training Process. ICML 2021: 12468-12478 - 2020
- [c42]Jacob D. Abernethy, Shivani Agarwal:
Conference on Learning Theory 2020: Preface. COLT 2020: 1-2 - [c41]Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil:
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions. ICML 2020: 85-95 - [c40]Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal:
Convex Calibrated Surrogates for the Multi-Label F-Measure. ICML 2020: 11246-11255 - [c39]Arpit Agarwal, Nicholas Johnson, Shivani Agarwal:
Choice Bandits. NeurIPS 2020 - [c38]Mingyuan Zhang, Shivani Agarwal:
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class. NeurIPS 2020 - [e1]Jacob D. Abernethy, Shivani Agarwal:
Conference on Learning Theory, COLT 2020, 9-12 July 2020, Virtual Event [Graz, Austria]. Proceedings of Machine Learning Research 125, PMLR 2020 [contents] - [i6]Mingyuan Zhang, Harish G. Ramaswamy, Shivani Agarwal:
Convex Calibrated Surrogates for the Multi-Label F-Measure. CoRR abs/2009.07801 (2020)
2010 – 2019
- 2018
- [c37]Arpit Agarwal, Prathamesh Patil, Shivani Agarwal:
Accelerated Spectral Ranking. ICML 2018: 70-79 - 2017
- [j8]Harikrishna Narasimhan, Shivani Agarwal:
Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve. Neural Comput. 29(7): 1919-1963 (2017) - [c36]Qizhen Zhang, Tengyuan Ye, Meryem Essaidi, Shivani Agarwal, Vincent Liu, Boon Thau Loo:
Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis. CIKM 2017: 1937-1946 - [c35]Arpit Agarwal, Shivani Agarwal, Sepehr Assadi, Sanjeev Khanna:
Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons. COLT 2017: 39-75 - 2016
- [j7]Harish G. Ramaswamy, Shivani Agarwal:
Convex Calibration Dimension for Multiclass Loss Matrices. J. Mach. Learn. Res. 17: 14:1-14:45 (2016) - [c34]Rohit Vaish, Neeldhara Misra, Shivani Agarwal, Avrim Blum:
On the Computational Hardness of Manipulating Pairwise Voting Rules. AAMAS 2016: 358-367 - [c33]Arun Rajkumar, Shivani Agarwal:
When can we rank well from comparisons of \(O(n\log(n))\) non-actively chosen pairs? COLT 2016: 1376-1401 - [c32]Harikrishna Narasimhan, Shivani Agarwal, David C. Parkes:
Automated Mechanism Design without Money via Machine Learning. IJCAI 2016: 433-439 - [c31]Shivani Agarwal:
On Ranking and Choice Models. IJCAI 2016: 4050-4053 - [c30]Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal:
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions. NIPS 2016: 1253-1261 - [i5]Harikrishna Narasimhan, Shivani Agarwal:
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve. CoRR abs/1605.04337 (2016) - 2015
- [c29]Arpit Agarwal, Shivani Agarwal:
On Consistent Surrogate Risk Minimization and Property Elicitation. COLT 2015: 4-22 - [c28]Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal:
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top. ICML 2015: 665-673 - [c27]Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal:
Convex Calibrated Surrogates for Hierarchical Classification. ICML 2015: 1852-1860 - [c26]Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Performance Measures. ICML 2015: 2398-2407 - [c25]C. G. Saneem Ahmed, Harikrishna Narasimhan, Shivani Agarwal:
Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures. UAI 2015: 171-180 - [i4]Harish G. Ramaswamy, Harikrishna Narasimhan, Shivani Agarwal:
Consistent Classification Algorithms for Multi-class Non-Decomposable Performance Metrics. CoRR abs/1501.00287 (2015) - [i3]Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal:
Consistent Algorithms for Multiclass Classification with a Reject Option. CoRR abs/1505.04137 (2015) - 2014
- [j6]Shivani Agarwal:
Surrogate regret bounds for bipartite ranking via strongly proper losses. J. Mach. Learn. Res. 15(1): 1653-1674 (2014) - [c24]Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson:
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. COLT 2014: 885-902 - [c23]Arun Rajkumar, Shivani Agarwal:
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data. ICML 2014: 118-126 - [c22]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 - [c21]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 - [c20]Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal:
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures. NIPS 2014: 1493-1501 - [c19]Arun Rajkumar, Shivani Agarwal:
Online Decision-Making in General Combinatorial Spaces. NIPS 2014: 3482-3490 - [i2]Harish G. Ramaswamy, Shivani Agarwal:
Convex Calibration Dimension for Multiclass Loss Matrices. CoRR abs/1408.2764 (2014) - 2013
- [c18]Shivani Agarwal:
Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses. COLT 2013: 338-353 - [c17]Harikrishna Narasimhan, Shivani Agarwal:
A Structural SVM Based Approach for Optimizing Partial AUC. ICML (1) 2013: 516-524 - [c16]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 - [c15]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 - [c14]Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari:
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses. NIPS 2013: 1475-1483 - [c13]Harikrishna Narasimhan, Shivani Agarwal:
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation. NIPS 2013: 2913-2921 - 2012
- [c12]Harish G. Ramaswamy, Shivani Agarwal:
Classification Calibration Dimension for General Multiclass Losses. NIPS 2012: 2087-2095 - [c11]Arun Rajkumar, Shivani Agarwal:
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification. AISTATS 2012: 933-941 - [i1]Shivani Agarwal:
Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses. CoRR abs/1207.0268 (2012) - 2011
- [c10]Shivani Agarwal:
The Infinite Push: A New Support Vector Ranking Algorithm that Directly Optimizes Accuracy at the Absolute Top of the List. SDM 2011: 839-850 - 2010
- [j5]Shivani Agarwal, Deepak Dugar, Shiladitya Sengupta:
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach. J. Chem. Inf. Model. 50(5): 716-731 (2010) - [j4]Shivani Agarwal:
Learning to rank on graphs. Mach. Learn. 81(3): 333-357 (2010) - [c9]Shivani Agarwal, Michael Collins:
Maximum Margin Ranking Algorithms for Information Retrieval. ECIR 2010: 332-343
2000 – 2009
- 2009
- [j3]Shivani Agarwal, Partha Niyogi:
Generalization Bounds for Ranking Algorithms via Algorithmic Stability. J. Mach. Learn. Res. 10: 441-474 (2009) - 2008
- [c8]Shivani Agarwal:
Generalization Bounds for Some Ordinal Regression Algorithms. ALT 2008: 7-21 - 2006
- [c7]Shivani Agarwal:
Ranking on graph data. ICML 2006: 25-32 - 2005
- [b1]Shivani Agarwal:
A Study of the Bipartite Ranking Problem in Machine Learning. University of Illinois Urbana-Champaign, USA, 2005 - [j2]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth:
Generalization Bounds for the Area Under the ROC Curve. J. Mach. Learn. Res. 6: 393-425 (2005) - [c6]Shivani Agarwal, Sariel Har-Peled, Dan Roth:
A Uniform Convergence Bound for the Area Under the ROC Curve. AISTATS 2005: 1-8 - [c5]Shivani Agarwal, Dan Roth:
Learnability of Bipartite Ranking Functions. COLT 2005: 16-31 - [c4]Shivani Agarwal, Partha Niyogi:
Stability and Generalization of Bipartite Ranking Algorithms. COLT 2005: 32-47 - 2004
- [j1]Shivani Agarwal, Aatif Awan, Dan Roth:
Learning to Detect Objects in Images via a Sparse, Part-Based Representation. IEEE Trans. Pattern Anal. Mach. Intell. 26(11): 1475-1490 (2004) - [c3]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004: 9-16 - 2002
- [c2]Shivani Agarwal, Dan Roth:
Learning a Sparse Representation for Object Detection. ECCV (4) 2002: 113-130 - [c1]Ashutosh Garg, Shivani Agarwal, Thomas S. Huang:
Fusion of Global and Local Information for Object Detection. ICPR (3) 2002: 723-
Coauthor Index
aka: Harish Guruprasad Ramaswamy
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last updated on 2024-10-01 21:39 CEST by the dblp team
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