S. Keerthi

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SMO algorithm for least squares SVM

S. Keerthi, S. Shevade, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

SMO Algorithm for Least-Squares SVM Formulations

S. Keerthi, S. Shevade, 2003, Neural Computation.

Trust region Newton methods for large-scale logistic regression

Chih-Jen Lin, S. Sathiya Keerthi, Ruby C. Weng, 2007, ICML '07.

A fast procedure for computing the distance between complex objects in three-dimensional space

S. Sathiya Keerthi, Elmer G. Gilbert, Daniel W. Johnson, 1988, IEEE J. Robotics Autom..

A dual coordinate descent method for large-scale linear SVM

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, ICML '08.

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

S. Sathiya Keerthi, Dennis DeCoste, D. DeCoste, 2005, J. Mach. Learn. Res..

IEEE Transactions on Automation Science and Engineering

Leyuan Shi, Giuseppe Carlo Calafiore, Jing Xiao, 2009, IEEE Transactions on Automation Science and Engineering.

Relational Learning with Gaussian Processes

Wei Chu, Zoubin Ghahramani, S. Sathiya Keerthi, 2006, NIPS.

Improvements to Platt's SMO Algorithm for SVM Classifier Design

S. Sathiya Keerthi, Chiranjib Bhattacharyya, Shirish K. Shevade, 2001, Neural Computation.

Trust Region Newton Method for Logistic Regression

Chih-Jen Lin, S. Sathiya Keerthi, Ruby C. Weng, 2008, J. Mach. Learn. Res..

A fast iterative nearest point algorithm for support vector machine classifier design

S. Sathiya Keerthi, Chiranjib Bhattacharyya, Shirish K. Shevade, 2000, IEEE Trans. Neural Networks Learn. Syst..

A machine learning approach for the curation of biomedical literature: KDD Cup 2002 (task 1)

Wei Chu, Han Tong Loh, S. Sathiya Keerthi, 2002, SKDD.

Improvements to the SMO algorithm for SVM regression

S. Sathiya Keerthi, Chiranjib Bhattacharyya, Shirish K. Shevade, 2000, IEEE Trans. Neural Networks Learn. Syst..

Parallel sequential minimal optimization for the training of support vector machines

S. Sathiya Keerthi, Chong Jin Ong, Lijuan Cao, 2006, IEEE Trans. Neural Networks.

A fast procedure for computing the distance between complex objects in three space

S. Sathiya Keerthi, Elmer G. Gilbert, Daniel W. Johnson, 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

A sequential dual method for large scale multi-class linear svms

Chih-Jen Lin, Cho-Jui Hsieh, Kai-Wei Chang, 2008, KDD.

Building Support Vector Machines with Reduced Classifier Complexity

S. Sathiya Keerthi, Dennis DeCoste, Olivier Chapelle, 2006, J. Mach. Learn. Res..

Convergence of a Generalized SMO Algorithm for SVM Classifier Design

S. Sathiya Keerthi, Elmer G. Gilbert, E. Gilbert, 2002, Machine Learning.

Efficient algorithms for ranking with SVMs

S. Sathiya Keerthi, Olivier Chapelle, O. Chapelle, 2010, Information Retrieval.

Learning a Hierarchical Monitoring System for Detecting and Diagnosing Service Issues

Shwetabh Khanduja, Vinod Nair, Ameya Raul, 2015, KDD.

A Quantitative Evaluation Framework for Missing Value Imputation Algorithms pdf

Johannes Gehrke, Vinod Nair, S. Sathiya Keerthi, 2013, ArXiv.

A simple and efficient algorithm for gene selection using sparse logistic regression

S. Sathiya Keerthi, Shirish K. Shevade, S. Keerthi, 2003, Bioinform..

Large scale semi-supervised linear SVMs

S. Sathiya Keerthi, Vikas Sindhwani, V. Sindhwani, 2006, SIGIR.

Improvements to SMO Algorithm for SVM Regression 1

S. Keerthi, S. Shevade, C. Bhattacharyya, 1999 .

Predictive Approaches for Choosing Hyperparameters in Gaussian Processes

S. Sundararajan, S. Sathiya Keerthi, S. Keerthi, 1999, Neural Computation.

Learning State Representations for Query Optimization with Deep Reinforcement Learning

Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi, 2018, DEEM@SIGMOD.

Special issue on support vector machines

Chih-Jen Lin, Colin Campbell, S. Sathiya Keerthi, 2003, Neurocomputing.

Information geometry and Plefka's mean-field theory

S. Keerthi, C. Bhattacharyya, 2000 .

C3 Reinforcement Learning C3.1 Introduction

Balaraman Ravindran, S. Keerthi, 2007 .

Newton Methods for Deep Learning

K. L. Tan, D. Mahajan, S. Keerthi, 2017 .

A general formulation for support vector machines

S. Keerthi, Wei Chu, C. Ong, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Supplementary Materials for “Distributed Newton Methods for Deep Neural Networks”

K. L. Tan, D. Mahajan, S. Keerthi, 2018 .

FastGeneralizedCross-ValidationAlgorithmforSparseModel Learning

S. Keerthi, S. Shevade, S. Sundararajan, 2007 .

A Note on Least Squares Support Vector Machines

S. Keerthi, Wei Chu, C. Ong, 2007 .

Delayed reinforcement learning

Balaraman Ravindran, S. Keerthi, 1997 .

Graph Based Classication Methods Using Inaccurate External Classier Information

S. Keerthi, S. Sundararajan, 2012 .

Conditional Generative Adversarial Networks for Regression

S. Sathiya Keerthi, Patrick Gallinari, Karan Aggarwal, 2019 .

SMO Algorithm for Least-Squares SVM Formulation

S. Sathiya Keerthi, Shirish K. Shevade, S. Keerthi, 2003, Neural Computation.

Delayed reinforcement learning methods

Balaraman Ravindran, S Sathiya Keerthi, Balaraman Ravindran, 1997 .

Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization

R. Mazumder, S. Keerthi, A. Acharya, 2022, ArXiv.

C3 Reinforcement Learning C3.1 Introduction

Balaraman Ravindran, S. Keerthi, 2007 .

StabilityRegionsforConstrainedNonlinearSystemsand TheirFunctionalCharacterizationvia Support-Vector-MachineLearning

Chong Jin Ong, S. S. Keerthi, E. G. Gilbert, 2004 .

A Structured Prediction Approach for Missing Value Imputation pdf

Vinod Nair, S. Sathiya Keerthi, Sundararajan Sellamanickam, 2013, ArXiv.

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, NIPS.

Multi-category Classification by Soft-Max Combination of Binary Classifiers

Wei Chu, S. Sathiya Keerthi, Aun Neow Poo, 2003, Multiple Classifier Systems.

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

S. Sathiya Keerthi, S. Keerthi, 2002, IEEE Trans. Neural Networks.

Optimization Techniques for Semi-Supervised Support Vector Machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2008, J. Mach. Learn. Res..

Gradient Boosted Decision Trees for High Dimensional Sparse Output

Inderjit S. Dhillon, Cho-Jui Hsieh, Huan Zhang, 2017, ICML.

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Chih-Jen Lin, S. Sathiya Keerthi, Chih-Jen Lin, 2003, Neural Computation.

Regularized Structured Output Learning with Partial Labels

S. Sathiya Keerthi, Charu Tiwari, Sundararajan Sellamanickam, 2012, SDM.

Branch and Bound for Semi-Supervised Support Vector Machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, NIPS.

Deterministic annealing for semi-supervised kernel machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, ICML.

Evaluation of simple performance measures for tuning SVM hyperparameters

S. Sathiya Keerthi, Aun Neow Poo, Kaibo Duan, 2003, Neurocomputing.

mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization

R. Mazumder, S. Keerthi, A. Acharya, 2023, 2302.09693.

A Machine Learning Approach for the Curation of Biomedical Literature

Han Tong Loh, S. Sathiya Keerthi, Min Shi, 2003, ECIR.

Gradient Boosting Neural Networks: GrowNet pdf

Xuanqing Liu, Sarkhan Badirli, Zhengming Xing, 2020, ArXiv.

A web of concepts

Ravi Kumar, Andrew Tomkins, S. Sathiya Keerthi, 2009, PODS.

Fast Generalized Cross-Validation Algorithm for Sparse Model Learning

S. Sundararajan, S. Sathiya Keerthi, Shirish K. Shevade, 2007, Neural Computation.

Distributed Newton Methods for Deep Neural Networks

Chih-Jen Lin, S. Sundararajan, S. Sathiya Keerthi, 2018, Neural Computation.

A stochastic connectionist approach for global optimization with application to pattern clustering

M. Narasimha Murty, S. Sathiya Keerthi, G. Phanendra Babu, 2000, IEEE Trans. Syst. Man Cybern. Part B.

Predictive Approaches For Gaussian Process Classifier Model Selection pdf

S. Sathiya Keerthi, Sundararajan Sellamanickam, S. Keerthi, 2012, ArXiv.

Synthesis of fault-tolerant feedforward neural networks using minimax optimization

M. Vidyasagar, S. Keerthi, Dipti Deodhare, 1998, IEEE Trans. Neural Networks.

Use of neural and other function approximators in reinforcement learning

Balaraman Ravindran, S Sathiya Keerthi, Balaraman Ravindran, 1997 .

Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network—support vector machine model

Aun-Neow Poo, R Ramesh, S. S. Keerthi, 2003 .

Semi-supervised multi-task learning of structured prediction models for web information extraction

S. Sathiya Keerthi, Paramveer S. Dhillon, Sundararajan Sellamanickam, 2011, CIKM '11.

Support Vector Ordinal Regression

Wei Chu, S. Sathiya Keerthi, S. Keerthi, 2007, Neural Computation.

New approaches to support vector ordinal regression

Wei Chu, S. Sathiya Keerthi, S. Keerthi, 2005, ICML.

Which Is the Best Multiclass SVM Method? An Empirical Study

S. Sathiya Keerthi, Kaibo Duan, S. Keerthi, 2005, Multiple Classifier Systems.

Efficient Algorithms for Global Inference in Internet Marketplaces

S. Keerthi, Kinjal Basu, Konstantin Salomatin, 2021, arXiv.org.

Iterative Viterbi A* Algorithm for K-Best Sequential Decoding

Yi Chang, Zhiheng Huang, Anlei Dong, 2012, ACL.

An Empirical Analysis of Deep Learning for Cardinality Estimation pdf

Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi, 2019, ArXiv.

Predictive Approaches for Choosing Hyperparameters in Generalised Linear Regression Models

S. Keerthi, S. Sundararajan, 2002 .

Analyzing textual databases using data mining to enable fast product development processes

Han Tong Loh, S. Sathiya Keerthi, Rakesh Menon, 2005, Reliab. Eng. Syst. Saf..

Bayesian Trigonometric Support Vector Classifier

Wei Chu, S. Sathiya Keerthi, Chong Jin Ong, 2003, Neural Computation.

A Fast Dual Algorithm for Kernel Logistic Regression

S. Sathiya Keerthi, Aun Neow Poo, Shirish K. Shevade, 2002, 2007 International Joint Conference on Neural Networks.

A Unified Loss Function in Bayesian Framework for Support Vector Regression

Wei Chu, S. Sathiya Keerthi, Chong Jin Ong, 2001, ICML.

Batch-Expansion Training: An Efficient Optimization Framework

S. V. N. Vishwanathan, S. Sathiya Keerthi, Markus Weimer, 2017, AISTATS.

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning pdf

S. V. N. Vishwanathan, S. Sathiya Keerthi, Markus Weimer, 2017, ArXiv.

Bayesian support vector regression using a unified loss function

Wei Chu, S. Sathiya Keerthi, Chong Jin Ong, 2004, IEEE Transactions on Neural Networks.

A distributed block coordinate descent method for training $l_1$ regularized linear classifiers pdf

S. Sundararajan, S. Sathiya Keerthi, Dhruv Kumar Mahajan, 2014, J. Mach. Learn. Res..

Transductive Classification Methods for Mixed Graphs pdf

S. Sathiya Keerthi, Sundararajan Sellamanickam, S. Keerthi, 2012, ArXiv.

Graph Based Classification Methods Using Inaccurate External Classifier Information pdf

S. Sathiya Keerthi, Sundararajan Sellamanickam, S. Keerthi, 2012, ArXiv.

Logit Attenuating Weight Normalization pdf

S. Sathiya Keerthi, Aman Gupta, Rohan Ramanath, 2021, ArXiv.

Bayesian Support Vector Machines for Feature Ranking and Selection

Wei Chu, Zoubin Ghahramani, S. Sathiya Keerthi, 2006, Feature Extraction.

Domain adaptation in display advertising: an application for partner cold-start

S. Sathiya Keerthi, Karan Aggarwal, Pranjul Yadav, 2019, RecSys.

A Sequential Dual Method for Structural SVMs

S. Sundararajan, S. Sathiya Keerthi, Shirish K. Shevade, 2011, SDM.

Developing parallel sequential minimal optimization for fast training support vector machine

S. Sathiya Keerthi, Chong Jin Ong, Lijuan Cao, 2006, Neurocomputing.

Semi-Supervised Gaussian Process Classifiers

Wei Chu, S. Sathiya Keerthi, Vikas Sindhwani, 2007, IJCAI.

IEEE Transactions on Automation Science and Engineering

M. P. Fanti, G. Calafiore, S. Bhatnagar, 2019, IEEE Transactions on Automation Science and Engineering.

Regression via Implicit Models and Optimal Transport Cost Minimization pdf

S. Sathiya Keerthi, Saurav Manchanda, Khoa Doan, 2020, ArXiv.

Regression with Conditional GAN pdf

S. Sathiya Keerthi, Patrick Gallinari, Karan Aggarwal, 2019, ArXiv.

Benchmarking Regression Methods: A comparison with CGAN

P. Gallinari, S. Keerthi, Pranjul Yadav, 2019, 1905.12868.

An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels

S. Sathiya Keerthi, Dennis DeCoste, Chong Jin Ong, 2004, IEEE Transactions on Neural Networks.

Mean Field Methods for a Special Class of Belief Networks pdf

S. Sathiya Keerthi, Chiranjib Bhattacharyya, S. Keerthi, 2001, J. Artif. Intell. Res..

A Variational Mean-Field Theory for Sigmoidal Belief Networks

S. Sathiya Keerthi, Chiranjib Bhattacharyya, S. Keerthi, 2000, NIPS.

Predictive Approaches for Choosing Hyperparameters in

S. S. Keerthi, S. Sundararajan, S. Keerthi, 2001 .

Tractable Semi-supervised Learning of Complex Structured Prediction Models

Kai-Wei Chang, S. Sundararajan, S. Sathiya Keerthi, 2013, ECML/PKDD.

Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses pdf

S. Sathiya Keerthi, Sundararajan Sellamanickam, Shirish K. Shevade, 2012, COLING.

Context filters for document-based information filtering

S. Sathiya Keerthi, K. R. K. Murthy, S. Keerthi, 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space pdf

Chandan K. Reddy, Khoa D. Doan, Saurav Manchanda, 2020, ArXiv.

Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles pdf

S. Sathiya Keerthi, Vivek Gupta, Dhruv Kumar Mahajan, 2017, ArXiv.

Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning

S. Sathiya Keerthi, Z. H. Zhang, Elmer G. Gilbert, 2004, Autom..

Near Real-Time Service Monitoring Using High-Dimensional Time Series

Shwetabh Khanduja, Vinod Nair, S. Sundararajan, 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

S. Keerthi, 2002, IEEE Trans. Neural Networks.

A Fast Tracking Algorithm for Generalized LARS/LASSO

S. Sathiya Keerthi, Shirish K. Shevade, S. Keerthi, 2007, IEEE Transactions on Neural Networks.

Generalized LARS as an effective feature selection tool for text classification with SVMs

S. Sathiya Keerthi, S. Keerthi, 2005, ICML.

Extracting the knowledge embedded in support vector machines

Xiuju Fu, Gih Guang Hung, Liping Goh, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).