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ORCIDS. Sathiya Keerthi
Sathiya Keerthi Selvaraj
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2020 – today
- 2024
[c61]Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna:
A Precise Characterization of SGD Stability Using Loss Surface Geometry. ICLR 2024- [c60]Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi
, Lars Hertel, Qiang Charles Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, S. Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh:
LiRank: Industrial Large Scale Ranking Models at LinkedIn. KDD 2024: 4804-4815 - [c59]Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, S. Sathiya Keerthi, Ajith Muralidharan:
LiMAML: Personalization of Deep Recommender Models via Meta Learning. KDD 2024: 5882-5892 - [c58]Changshuai Wei, Benjamin Zelditch, Joyce Chen, Andre Assuncao Silva T. Ribeiro, Jingyi Kenneth Tay, Borja Ocejo Elizondo, Sathiya Keerthi Selvaraj, Aman Gupta, Licurgo Benemann De Almeida:
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System. KDD 2024: 5938-5949 - [i33]Zirui Liu, Qingquan Song, Qiang Charles Xiao, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Xia Hu:
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference. CoRR abs/2401.04044 (2024) - [i32]Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna:
A Precise Characterization of SGD Stability Using Loss Surface Geometry. CoRR abs/2401.12332 (2024) - [i31]Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi, Lars Hertel, Qiang Charles Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, S. Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh:
LiRank: Industrial Large Scale Ranking Models at LinkedIn. CoRR abs/2402.06859 (2024) - [i30]Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, Sathiya Keerthi Selvaraj, Ajith Muralidharan:
LiMAML: Personalization of Deep Recommender Models via Meta Learning. CoRR abs/2403.00803 (2024) - [i29]Changshuai Wei, Benjamin Zelditch, Joyce Chen, Andre Assuncao Silva T. Ribeiro, Jingyi Kenneth Tay, Borja Ocejo Elizondo, Sathiya Keerthi Selvaraj, Aman Gupta, Licurgo Benemann De Almeida:
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System. CoRR abs/2405.10490 (2024) - 2023
- [c57]Ayan Acharya, Siyuan Gao, Ankan Saha, Borja Ocejo, Kinjal Basu, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Parag Agrawal:
Optimizing for Member Value in an Edge Building Marketplace. CIKM 2023: 5-14 - [c56]Aman Gupta, S. Sathiya Keerthi, Ayan Acharya, Miao Cheng, Borja Ocejo Elizondo, Rohan Ramanath, Rahul Mazumder, Kinjal Basu, J. Kenneth Tay, Rupesh Gupta:
Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference. KDD 2023: 5781-5782 - [c55]Ayan Acharya, Siyuan Gao, Borja Ocejo, Kinjal Basu, Ankan Saha, Sathiya Keerthi Selvaraj, Rahul Mazumder, Parag Agrawal, Aman Gupta:
Promoting Inactive Members in Edge-Building Marketplace. WWW (Companion Volume) 2023: 945-949 - [i28]Kayhan Behdin, Qingquan Song, Aman Gupta, Ayan Acharya, David Durfee, Borja Ocejo, S. Sathiya Keerthi, Rahul Mazumder:
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization. CoRR abs/2302.09693 (2023) - [i27]Kayhan Behdin, Ayan Acharya, Aman Gupta, Sathiya Keerthi Selvaraj, Rahul Mazumder:
QuantEase: Optimization-based Quantization for Language Models - An Efficient and Intuitive Algorithm. CoRR abs/2309.01885 (2023) - 2022
- [c54]Rohan Ramanath, S. Sathiya Keerthi, Yao Pan, Konstantin Salomatin, Kinjal Basu:
Efficient Vertex-Oriented Polytopic Projection for Web-Scale Applications. AAAI 2022: 3821-3829 - [i26]Kayhan Behdin, Qingquan Song, Aman Gupta, David Durfee, Ayan Acharya, S. Sathiya Keerthi, Rahul Mazumder:
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization. CoRR abs/2212.04343 (2022) - 2021
- [i25]Rohan Ramanath, S. Sathiya Keerthi, Yao Pan, Konstantin Salomatin, Kinjal Basu:
Efficient Algorithms for Global Inference in Internet Marketplaces. CoRR abs/2103.05277 (2021) - [i24]Aman Gupta, Rohan Ramanath, Jun Shi, Anika Ramachandran, Sirou Zhou, Mingzhou Zhou, S. Sathiya Keerthi:
Logit Attenuating Weight Normalization. CoRR abs/2108.05839 (2021) - 2020
- [i23]Saurav Manchanda, Pranjul Yadav, Khoa D. Doan, S. Sathiya Keerthi:
Targeted display advertising: the case of preferential attachment. CoRR abs/2002.02879 (2020) - [i22]Saurav Manchanda, Khoa D. Doan, Pranjul Yadav, S. Sathiya Keerthi:
Regression via Implicit Models and Optimal Transport Cost Minimization. CoRR abs/2003.01296 (2020) - [i21]Khoa D. Doan, Saurav Manchanda, Fengjiao Wang, S. Sathiya Keerthi, Avradeep Bhowmik, Chandan K. Reddy:
Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space. CoRR abs/2003.11774 (2020)
2010 – 2019
- 2019
- [c53]Saurav Manchanda, Pranjul Yadav, Khoa D. Doan, S. Sathiya Keerthi:
Targeted display advertising: the case of preferential attachment. IEEE BigData 2019: 1868-1877 - [c52]Karan Aggarwal, Pranjul Yadav, S. Sathiya Keerthi:
Domain adaptation in display advertising: an application for partner cold-start. RecSys 2019: 178-186 - [i20]Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi:
An Empirical Analysis of Deep Learning for Cardinality Estimation. CoRR abs/1905.06425 (2019) - [i19]Karan Aggarwal, Matthieu Kirchmeyer, Pranjul Yadav, S. Sathiya Keerthi, Patrick Gallinari:
Regression with Conditional GAN. CoRR abs/1905.12868 (2019) - 2018
- [j44]Dhruv Mahajan, Nikunj Agrawal, S. Sathiya Keerthi, Sundararajan Sellamanickam, Léon Bottou:
An efficient distributed learning algorithm based on effective local functional approximations. J. Mach. Learn. Res. 19: 74:1-74:37 (2018) - [j43]Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin:
Distributed Newton Methods for Deep Neural Networks. Neural Comput. 30(6) (2018) - [c51]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS 2018: 736-744 - [c50]Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi:
Learning State Representations for Query Optimization with Deep Reinforcement Learning. DEEM@SIGMOD 2018: 4:1-4:4 - [i18]Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin:
Distributed Newton Methods for Deep Neural Networks. CoRR abs/1802.00130 (2018) - [i17]Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi:
Learning State Representations for Query Optimization with Deep Reinforcement Learning. CoRR abs/1803.08604 (2018) - 2017
- [j42]Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan:
A distributed block coordinate descent method for training l1 regularized linear classifiers. J. Mach. Learn. Res. 18: 91:1-91:35 (2017) - [c49]Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh:
Gradient Boosted Decision Trees for High Dimensional Sparse Output. ICML 2017: 3182-3190 - [i16]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR abs/1704.06731 (2017) - [i15]Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sundararajan Sellamanickam, Shravan Narayanamurthy, Rahul Kidambi:
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles. CoRR abs/1711.05482 (2017) - 2016
- [c48]Dhruv Mahajan, Vishwajit Kolathur, Chetan Bansal, Suresh Parthasarathy, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke:
Hashtag Recommendation for Enterprise Applications. CIKM 2016: 893-902 - 2015
- [c47]Shwetabh Khanduja, Vinod Nair, S. Sundararajan, Ameya Raul, Ajesh Babu Shaj, S. Sathiya Keerthi:
Near Real-Time Service Monitoring Using High-Dimensional Time Series. ICDM Workshops 2015: 1624-1627 - [c46]Vinod Nair, Ameya Raul, Shwetabh Khanduja, Vikas Bahirwani, Sundararajan Sellamanickam, S. Sathiya Keerthi, Steve Herbert, Sudheer Dhulipalla:
Learning a Hierarchical Monitoring System for Detecting and Diagnosing Service Issues. KDD 2015: 2029-2038 - [i14]S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna:
Towards a Better Understanding of Predict and Count Models. CoRR abs/1511.02024 (2015) - 2014
- [i13]Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan:
A Distributed Algorithm for Training Nonlinear Kernel Machines. CoRR abs/1405.4543 (2014) - [i12]Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan:
A distributed block coordinate descent method for training $l_1$ regularized linear classifiers. CoRR abs/1405.4544 (2014) - 2013
- [c45]Kai-Wei Chang, S. Sundararajan, S. Sathiya Keerthi:
Tractable Semi-supervised Learning of Complex Structured Prediction Models. ECML/PKDD (3) 2013: 176-191 - [i11]Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan, Léon Bottou:
A Functional Approximation Based Distributed Learning Algorithm. CoRR abs/1310.8418 (2013) - [i10]Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan, Léon Bottou:
A Parallel SGD method with Strong Convergence. CoRR abs/1311.0636 (2013) - [i9]Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi:
A Structured Prediction Approach for Missing Value Imputation. CoRR abs/1311.2137 (2013) - [i8]Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan:
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. CoRR abs/1311.2276 (2013) - [i7]Balamurugan Palaniappan, Shirish K. Shevade, S. Sundararajan, S. Sathiya Keerthi:
An Empirical Evaluation of Sequence-Tagging Trainers. CoRR abs/1311.2378 (2013) - 2012
- [c44]Zhiheng Huang, Yi Chang, Bo Long, Jean-François Crespo, Anlei Dong, S. Sathiya Keerthi, Su-Lin Wu:
Iterative Viterbi A* Algorithm for K-Best Sequential Decoding. ACL (1) 2012: 611-619 - [c43]Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish K. Shevade:
Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses. COLING (Posters) 2012: 1091-1100 - [c42]Sundararajan Sellamanickam, Charu Tiwari, Sathiya Keerthi Selvaraj:
Regularized Structured Output Learning with Partial Labels. SDM 2012: 1059-1070 - [c41]Philip Bohannon, Nilesh N. Dalvi, Yuval Filmus, Nori Jacoby, S. Sathiya Keerthi, Alok Kirpal:
Automatic web-scale information extraction. SIGMOD Conference 2012: 609-612 - [c40]Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam:
Deterministic Annealing for Semi-Supervised Structured Output Learning. AISTATS 2012: 299-307 - [i6]Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj:
Graph Based Classification Methods Using Inaccurate External Classifier Information. CoRR abs/1206.5915 (2012) - [i5]Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj:
Transductive Classification Methods for Mixed Graphs. CoRR abs/1206.6015 (2012) - [i4]Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj:
Predictive Approaches For Gaussian Process Classifier Model Selection. CoRR abs/1206.6038 (2012) - [i3]Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish K. Shevade:
Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses. CoRR abs/1211.0210 (2012) - 2011
- [c39]Sathiya Keerthi Selvaraj, Bigyan Bhar, Sundararajan Sellamanickam, Shirish K. Shevade:
Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset. CIKM 2011: 653-662 - [c38]Sundararajan Sellamanickam, Priyanka Garg, Sathiya Keerthi Selvaraj:
A pairwise ranking based approach to learning with positive and unlabeled examples. CIKM 2011: 663-672 - [c37]Paramveer S. Dhillon, Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj:
Semi-supervised multi-task learning of structured prediction models for web information extraction. CIKM 2011: 957-966 - [c36]Shirish K. Shevade, Balamurugan Palaniappan, S. Sundararajan, S. Sathiya Keerthi:
A Sequential Dual Method for Structural SVMs. SDM 2011: 223-234 - [i2]Chiru Bhattacharyya, S. Sathiya Keerthi:
Mean Field Methods for a Special Class of Belief Networks. CoRR abs/1106.0246 (2011) - 2010
- [j41]Olivier Chapelle, S. Sathiya Keerthi:
Efficient algorithms for ranking with SVMs. Inf. Retr. 13(3): 201-215 (2010)
2000 – 2009
- 2009
- [c35]Nilesh N. Dalvi, Ravi Kumar, Bo Pang, Raghu Ramakrishnan, Andrew Tomkins, Philip Bohannon, S. Sathiya Keerthi, Srujana Merugu:
A web of concepts. PODS 2009: 1-12 - 2008
- [j40]Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
Optimization Techniques for Semi-Supervised Support Vector Machines. J. Mach. Learn. Res. 9: 203-233 (2008) - [j39]Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust Region Newton Method for Logistic Regression. J. Mach. Learn. Res. 9: 627-650 (2008) - [c34]Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan:
A dual coordinate descent method for large-scale linear SVM. ICML 2008: 408-415 - [c33]S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
A sequential dual method for large scale multi-class linear svms. KDD 2008: 408-416 - 2007
- [j38]S. Sundararajan, Shirish K. Shevade, S. Sathiya Keerthi:
Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Neural Comput. 19(1): 283-301 (2007) - [j37]Wei Chu, S. Sathiya Keerthi:
Support Vector Ordinal Regression. Neural Comput. 19(3): 792-815 (2007) - [j36]S. Sathiya Keerthi, Shirish K. Shevade:
A Fast Tracking Algorithm for Generalized LARS/LASSO. IEEE Trans. Neural Networks 18(6): 1826-1830 (2007) - [c32]Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust region Newton methods for large-scale logistic regression. ICML 2007: 561-568 - [c31]Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi:
Semi-Supervised Gaussian Process Classifiers. IJCAI 2007: 1059-1064 - [i1]S. Sathiya Keerthi, John A. Tomlin:
Constructing a maximum utility slate of on-line advertisements. CoRR abs/0706.1318 (2007) - 2006
- [j35]Lijuan Cao, S. Sathiya Keerthi, Chong Jin Ong, P. Uvaraj, Xiu Ju Fu, H. P. Lee:
Developing parallel sequential minimal optimization for fast training support vector machine. Neurocomputing 70(1-3): 93-104 (2006) - [j34]S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste:
Building Support Vector Machines with Reduced Classifier Complexity. J. Mach. Learn. Res. 7: 1493-1515 (2006) - [j33]Lijuan Cao, S. Sathiya Keerthi, Chong Jin Ong, J. Q. Zhang, U. Periyathamby, Xiu Ju Fu, H. P. Lee:
Parallel sequential minimal optimization for the training of support vector machines. IEEE Trans. Neural Networks 17(4): 1039-1049 (2006) - [c30]Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle:
Deterministic annealing for semi-supervised kernel machines. ICML 2006: 841-848 - [c29]Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
Branch and Bound for Semi-Supervised Support Vector Machines. NIPS 2006: 217-224 - [c28]Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi:
Relational Learning with Gaussian Processes. NIPS 2006: 289-296 - [c27]S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle:
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. NIPS 2006: 673-680 - [c26]Vikas Sindhwani, S. Sathiya Keerthi:
Large scale semi-supervised linear SVMs. SIGIR 2006: 477-484 - [p1]Wei Chu, S. Sathiya Keerthi, Chong Jin Ong, Zoubin Ghahramani:
Bayesian Support Vector Machines for Feature Ranking and Selection. Feature Extraction 2006: 403-418 - 2005
- [j32]S. Sathiya Keerthi, Dennis DeCoste:
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs. J. Mach. Learn. Res. 6: 341-361 (2005) - [j31]S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo:
A Fast Dual Algorithm for Kernel Logistic Regression. Mach. Learn. 61(1-3): 151-165 (2005) - [j30]Rakesh Menon, Han Tong Loh, S. Sathiya Keerthi:
Analyzing textual databases using data mining to enable fast product development processes. Reliab. Eng. Syst. Saf. 88(2): 171-180 (2005) - [j29]Wei Chu, Chong Jin Ong, S. Sathiya Keerthi:
An improved conjugate gradient scheme to the solution of least squares SVM. IEEE Trans. Neural Networks 16(2): 498-501 (2005) - [c25]Wei Chu, S. Sathiya Keerthi:
New approaches to support vector ordinal regression. ICML 2005: 145-152 - [c24]S. Sathiya Keerthi:
Generalized LARS as an effective feature selection tool for text classification with SVMs. ICML 2005: 417-424 - [c23]Kaibo Duan, S. Sathiya Keerthi:
Which Is the Best Multiclass SVM Method? An Empirical Study. Multiple Classifier Systems 2005: 278-285 - [c22]S. Sathiya Keerthi, Wei Chu:
A matching pursuit approach to sparse Gaussian process regression. NIPS 2005: 643-650 - 2004
- [j28]Chong Jin Ong, S. Sathiya Keerthi, Elmer G. Gilbert, Z. H. Zhang:
Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning. Autom. 40(11): 1955-1964 (2004) - [j27]Wei Chu, S. Sathiya Keerthi, Chong Jin Ong:
Bayesian support vector regression using a unified loss function. IEEE Trans. Neural Networks 15(1): 29-44 (2004) - [j26]Martin M. S. Lee, S. Sathiya Keerthi, Chong Jin Ong, Dennis DeCoste:
An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. IEEE Trans. Neural Networks 15(3): 750-757 (2004) - [c21]Shirish K. Shevade, S. Sundararajan, S. Sathiya Keerthi:
Predictive Approaches for Sparse Model Learning. ICONIP 2004: 434-439 - 2003
- [j25]Shirish K. Shevade, S. Sathiya Keerthi:
A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinform. 19(17): 2246-2253 (2003) - [j24]Kaibo Duan, S. Sathiya Keerthi, Aun Neow Poo:
Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51: 41-59 (2003) - [j23]Colin Campbell, Chih-Jen Lin, S. Sathiya Keerthi, V. David Sánchez A.:
Special issue on support vector machines. Neurocomputing 55(1-2): 1-3 (2003) - [j22]S. Sathiya Keerthi, Shirish K. Shevade:
SMO Algorithm for Least-Squares SVM Formulation. Neural Comput. 15(2): 487-507 (2003) - [j21]S. Sathiya Keerthi, Chih-Jen Lin:
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Comput. 15(7): 1667-1689 (2003) - [j20]Wei Chu, S. Sathiya Keerthi, Chong Jin Ong:
Bayesian Trigonometric Support Vector Classifier. Neural Comput. 15(9): 2227-2254 (2003) - [c20]Min Shi, David S. Edwin, Rakesh Menon, Lixiang Shen, Jonathan Y. K. Lim, Han Tong Loh, S. Sathiya Keerthi, Chong Jin Ong:
A Machine Learning Approach for the Curation of Biomedical Literature. ECIR 2003: 597-604 - [c19]Rakesh Menon, Han Tong Loh, S. Sathiya Keerthi, Aarnout Brombacher:
Automated Text Classification for Fast Feedback - Investigating the Effects of Document Representation. KES 2003: 1008-1014 - [c18]Kaibo Duan, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, Aun Neow Poo:
Multi-category Classification by Soft-Max Combination of Binary Classifiers. Multiple Classifier Systems 2003: 125-134 - 2002
- [j19]S. Sathiya Keerthi, Elmer G. Gilbert:
Convergence of a Generalized SMO Algorithm for SVM Classifier Design. Mach. Learn. 46(1-3): 351-360 (2002) - [j18]S. Sathiya Keerthi, Chong Jin Ong, Keng Boon Siah, David B. L. Lim, Wei Chu, Min Shi, David S. Edwin, Rakesh Menon, Lixiang Shen, Jonathan Y. K. Lim, Han Tong Loh:
A Machine Learning Approach for the Curation of Biomedical Literature - KDD Cup 2002 (Task 1). SIGKDD Explor. 4(2): 93-94 (2002) - [j17]S. Sathiya Keerthi:
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans. Neural Networks 13(5): 1225-1229 (2002) - [c17]Harshad Jahagirdar, S. Sathiya Keerthi, Marcelo H. Ang Jr.:
Reference governor control of constrained feedback systems using neural networks. ISIC 2002: 223-227 - [c16]S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo:
A Fast Dual Algorithm for Kernel Logistic Regression. ICML 2002: 299-306 - 2001
- [j16]Chiranjib Bhattacharyya, S. Sathiya Keerthi:
Mean Field Methods for a Special Class of Belief Networks. J. Artif. Intell. Res. 15: 91-114 (2001) - [j15]K. Sridharan, S. Sathiya Keerthi:
Computation of a penetration measure between 3D convex polyhedral objects for collision detection. J. Field Robotics 18(11): 623-631 (2001) - [j14]S. Sathiya Keerthi, Shirish K. Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy:
Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Comput. 13(3): 637-649 (2001) - [j13]S. Sundararajan, S. Sathiya Keerthi:
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Comput. 13(5): 1103-1118 (2001) - [j12]K. R. K. Murthy, S. Sathiya Keerthi, M. Narasimha Murty:
Rule prepending and post-pruning approach to incremental learning of decision lists. Pattern Recognit. 34(8): 1697-1699 (2001) - [c15]Wei Chu, S. Sathiya Keerthi, Chong Jin Ong:
A Unified Loss Function in Bayesian Framework for Support Vector Regression. ICML 2001: 51-58 - 2000
- [j11]S. Sathiya Keerthi, Shirish K. Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy:
A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Networks Learn. Syst. 11(1): 124-136 (2000) - [j10]Shirish K. Shevade, S. Sathiya Keerthi, Chiranjib Bhattacharyya, K. R. K. Murthy:
Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Networks Learn. Syst. 11(5): 1188-1193 (2000) - [j9]G. Phanendra Babu, M. Narasimha Murty, S. Sathiya Keerthi:
A stochastic connectionist approach for global optimization with application to pattern clustering. IEEE Trans. Syst. Man Cybern. Part B 30(1): 10-24 (2000) - [c14]Chiranjib Bhattacharyya, S. Sathiya Keerthi:
A Variational Mean-Field Theory for Sigmoidal Belief Networks. NIPS 2000: 374-380
1990 – 1999
- 1999
- [c13]C. S. Sundaresan, S. Sathiya Keerthi:
A Study of Representations for Pen based Handwriting Recognition of Tamil Characters. ICDAR 1999: 422-425 - [c12]K. R. K. Murthy, S. Sathiya Keerthi:
Context Filters for Document-based Information Filtering. ICDAR 1999: 709-712 - [c11]S. Sathiya Keerthi, Chong Jin Ong, Eugene Huang, Elmer G. Gilbert:
EquiDistance Diagram: A New Roadmap Method for Path Planning. ICRA 1999: 682-687 - [c10]S. Sundararajan, S. Sathiya Keerthi:
Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. NIPS 1999: 631-637 - 1998
- [j8]Rachuri Sudarsan, S. Sathiya Keerthi:
Numerical approaches for solution of differential equations on manifolds. Appl. Math. Comput. 92(2-3): 153-193 (1998) - [j7]Rachuri Sudarsan, S. Sathiya Keerthi:
An efficient approach for the numerical simulation of multibody systems. Appl. Math. Comput. 92(2-3): 195-218 (1998) - [j6]Dipti Deodhare, M. Vidyasagar, S. Sathiya Keerthi:
Synthesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Trans. Neural Networks 9(5): 891-900 (1998) - 1995
- [j5]S. Sathiya Keerthi, Makarand S. Phatak:
Regional pole placement of multivariable systems under control structure constraints. IEEE Trans. Autom. Control. 40(2): 272-276 (1995) - [j4]Abhi Dattasharma, S. Sathiya Keerthi:
An Augmented Voronoi Roadmap for 3D Translational Motion Planning for a Convex Polyhedron Moving Amidst Convex Polyhedral Obstacles. Theor. Comput. Sci. 140(2): 205-230 (1995) - [c9]Vijay Chandru, Abhi Dattasharma, S. Sathiya Keerthi, N. K. Sancheti, V. Vinay:
Algorithms for the Optimal Loading of Recursive Neural Nets. SODA 1995: 342-349 - 1994
- [j3]K. Sridharan, Harry E. Stephanou, K. C. Craig, S. Sathiya Keerthi:
Distance Measures on Intersecting Objects and Their Applications. Inf. Process. Lett. 51(4): 181-188 (1994) - 1993
- [c8]Abhi Dattasharma, S. Sathiya Keerthi:
Translational Motion Planning for a Convex Polyhedron in a 3D Polyhedral World Using an Efficient and New Roadmap. CCCG 1993: 449-454 - [c7]K. Sridharan, Harry E. Stephanou, S. Sathiya Keerthi:
On Computing a Distance Measure for Path Planning. ICRA (1) 1993: 554-559 - [c6]Sudhaker Samuel, S. Sathiya Keerthi:
Numerical Determination of Optimal Non-Holonomic Paths in the Presence of Obstacles. ICRA (1) 1993: 826-831 - [c5]Nukala V. R. K. N. Murthy, S. Sathiya Keerthi:
Optimal Control of a Somersaulting Platform Diver: A Numerical Approach. ICRA (1) 1993: 1013-1018 - 1992
- [j2]Makarand S. Phatak, S. Sathiya Keerthi:
A homotopy approach for stabilizing single-input systems with control structure constraints. Autom. 28(5): 981-987 (1992) - [c4]S. Sathiya Keerthi, Rachuri Sudarsan, N. K. Sancheti:
A new approach to the numerical solution of constrained mechanical system dynamics. ICRA 1992: 293-298 - [c3]H. Shashikala, N. K. Sancheti, S. Sathiya Keerthi:
Path planning: an approach based on connecting all the minimizers and maximizers of a potential function. ICRA 1992: 2309-2314 - [c2]N. K. Sancheti, S. Sathiya Keerthi:
Computation of certain measures of proximity between convex polytopes: a complexity viewpoint. ICRA 1992: 2508-2513
1980 – 1989
- 1988
- [j1]Elmer G. Gilbert, Daniel W. Johnson, S. Sathiya Keerthi:
A fast procedure for computing the distance between complex objects in three-dimensional space. IEEE J. Robotics Autom. 4(2): 193-203 (1988) - 1987
- [c1]Elmer G. Gilbert, Daniel W. Johnson, S. Sathiya Keerthi:
A fast procedure for computing the distance between complex objects in three space. ICRA 1987: 1883-1889
Coauthor Index
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