S. Sathiya Keerthi

ORCID

Publication Activity (10 Years)

Years Active: 1987-2024
Publications (10 Years): 27

Top Venues

Publications

Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
A Precise Characterization of SGD Stability Using Loss Surface Geometry. CoRR (2024)
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 (2023)
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)
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 (2022)
Rohan Ramanath, S. Sathiya Keerthi, Yao Pan, Konstantin Salomatin, Kinjal Basu
Efficient Vertex-Oriented Polytopic Projection for Web-Scale Applications. AAAI (2022)
Aman Gupta, Rohan Ramanath, Jun Shi, Anika Ramachandran, Sirou Zhou, Mingzhou Zhou, S. Sathiya Keerthi
Logit Attenuating Weight Normalization. CoRR (2021)
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 (2020)
Saurav Manchanda, Pranjul Yadav, Khoa D. Doan, S. Sathiya Keerthi
Targeted display advertising: the case of preferential attachment. CoRR (2020)
Saurav Manchanda, Khoa Doan, Pranjul Yadav, S. Sathiya Keerthi
Regression via Implicit Models and Optimal Transport Cost Minimization. CoRR (2020)
Saurav Manchanda, Pranjul Yadav, Khoa D. Doan, S. Sathiya Keerthi
Targeted display advertising: the case of preferential attachment. IEEE BigData (2019)
Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi
An Empirical Analysis of Deep Learning for Cardinality Estimation. CoRR (2019)
Karan Aggarwal, Matthieu Kirchmeyer, Pranjul Yadav, S. Sathiya Keerthi, Patrick Gallinari
Regression with Conditional GAN. CoRR (2019)
Karan Aggarwal, Pranjul Yadav, S. Sathiya Keerthi
Domain adaptation in display advertising: an application for partner cold-start. RecSys (2019)
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 (2018)
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 (2018)
Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi
Learning State Representations for Query Optimization with Deep Reinforcement Learning. DEEM@SIGMOD (2018)
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS (2018)
Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi
Learning State Representations for Query Optimization with Deep Reinforcement Learning. CoRR (2018)
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)
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 (2017)
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR (2017)
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan
A distributed block coordinate descent method for training l1 regularized linear classifiers. J. Mach. Learn. Res. 18 (2017)
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)
Dhruv Mahajan, Vishwajit Kolathur, Chetan Bansal, Suresh Parthasarathy, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke
Hashtag Recommendation for Enterprise Applications. CIKM (2016)
S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
Towards a Better Understanding of Predict and Count Models. CoRR (2015)
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)
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)
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan
A distributed block coordinate descent method for training $l_1$ regularized linear classifiers. CoRR (2014)
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan
A Distributed Algorithm for Training Nonlinear Kernel Machines. CoRR (2014)
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi
A Structured Prediction Approach for Missing Value Imputation. CoRR (2013)
Balamurugan Palaniappan, Shirish K. Shevade, S. Sundararajan, S. Sathiya Keerthi
An Empirical Evaluation of Sequence-Tagging Trainers. CoRR (2013)
Kai-Wei Chang, S. Sundararajan, S. Sathiya Keerthi
Tractable Semi-supervised Learning of Complex Structured Prediction Models. ECML/PKDD (3) (2013)
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan, Léon Bottou
A Parallel SGD method with Strong Convergence. CoRR (2013)
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. CoRR (2013)
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan, Léon Bottou
A Functional Approximation Based Distributed Learning Algorithm. CoRR (2013)
Philip Bohannon, Nilesh N. Dalvi, Yuval Filmus, Nori Jacoby, S. Sathiya Keerthi, Alok Kirpal
Automatic web-scale information extraction. SIGMOD Conference (2012)
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam
Deterministic Annealing for Semi-Supervised Structured Output Learning. AISTATS (2012)
Chiru Bhattacharyya, S. Sathiya Keerthi
Mean Field Methods for a Special Class of Belief Networks CoRR (2011)
Shirish K. Shevade, Balamurugan Palaniappan, S. Sundararajan, S. Sathiya Keerthi
A Sequential Dual Method for Structural SVMs. SDM (2011)
Olivier Chapelle, S. Sathiya Keerthi
Efficient algorithms for ranking with SVMs. Inf. Retr. 13 (3) (2010)
Nilesh N. Dalvi, Ravi Kumar, Bo Pang, Raghu Ramakrishnan, Andrew Tomkins, Philip Bohannon, S. Sathiya Keerthi, Srujana Merugu
A web of concepts. PODS (2009)
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
Optimization Techniques for Semi-Supervised Support Vector Machines. J. Mach. Learn. Res. 9 (2008)
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)
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)
Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
Trust Region Newton Method for Logistic Regression. J. Mach. Learn. Res. 9 (2008)
S. Sathiya Keerthi, Shirish K. Shevade
A Fast Tracking Algorithm for Generalized LARS/LASSO. IEEE Trans. Neural Networks 18 (6) (2007)
S. Sathiya Keerthi, John A. Tomlin
Constructing a maximum utility slate of on-line advertisements CoRR (2007)
Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
Trust region Newton methods for large-scale logistic regression. ICML (2007)
Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi
Semi-Supervised Gaussian Process Classifiers. IJCAI (2007)
S. Sundararajan, Shirish K. Shevade, S. Sathiya Keerthi
Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Neural Comput. 19 (1) (2007)
Wei Chu, S. Sathiya Keerthi
Support Vector Ordinal Regression. Neural Comput. 19 (3) (2007)
Vikas Sindhwani, S. Sathiya Keerthi
Large scale semi-supervised linear SVMs. SIGIR (2006)
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
Branch and Bound for Semi-Supervised Support Vector Machines. NIPS (2006)
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) (2006)
Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle
Deterministic annealing for semi-supervised kernel machines. ICML (2006)
Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi
Relational Learning with Gaussian Processes. NIPS (2006)
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) (2006)
S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. NIPS (2006)
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste
Building Support Vector Machines with Reduced Classifier Complexity. J. Mach. Learn. Res. 7 (2006)
S. Sathiya Keerthi, Dennis DeCoste
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs. J. Mach. Learn. Res. 6 (2005)
S. Sathiya Keerthi, Wei Chu
A matching pursuit approach to sparse Gaussian process regression. NIPS (2005)
Wei Chu, S. Sathiya Keerthi
New approaches to support vector ordinal regression. ICML (2005)
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) (2005)
S. Sathiya Keerthi
Generalized LARS as an effective feature selection tool for text classification with SVMs. ICML (2005)
S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo
A Fast Dual Algorithm for Kernel Logistic Regression. Mach. Learn. 61 (1-3) (2005)
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) (2005)
Kaibo Duan, S. Sathiya Keerthi
Which Is the Best Multiclass SVM Method? An Empirical Study. Multiple Classifier Systems (2005)
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) (2004)
Wei Chu, S. Sathiya Keerthi, Chong Jin Ong
Bayesian support vector regression using a unified loss function. IEEE Trans. Neural Networks 15 (1) (2004)
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) (2004)
Shirish K. Shevade, S. Sundararajan, S. Sathiya Keerthi
Predictive Approaches for Sparse Model Learning. ICONIP (2004)
Colin Campbell, Chih-Jen Lin, S. Sathiya Keerthi, V. David Sánchez A.
Special issue on support vector machines. Neurocomputing 55 (1-2) (2003)
Rakesh Menon, Han Tong Loh, S. Sathiya Keerthi, Aarnout Brombacher
Automated Text Classification for Fast Feedback - Investigating the Effects of Document Representation. KES (2003)
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)
Wei Chu, S. Sathiya Keerthi, Chong Jin Ong
Bayesian Trigonometric Support Vector Classifier. Neural Comput. 15 (9) (2003)
Shirish K. Shevade, S. Sathiya Keerthi
A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinform. 19 (17) (2003)
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)
Kaibo Duan, S. Sathiya Keerthi, Aun Neow Poo
Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51 (2003)
S. Sathiya Keerthi, Chih-Jen Lin
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Comput. 15 (7) (2003)
S. Sathiya Keerthi, Shirish K. Shevade
SMO Algorithm for Least-Squares SVM Formulation. Neural Comput. 15 (2) (2003)
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) (2002)
S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo
A Fast Dual Algorithm for Kernel Logistic Regression. ICML (2002)
S. Sathiya Keerthi
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans. Neural Networks 13 (5) (2002)
S. Sathiya Keerthi, Elmer G. Gilbert
Convergence of a Generalized SMO Algorithm for SVM Classifier Design. Mach. Learn. 46 (1-3) (2002)
Harshad Jahagirdar, S. Sathiya Keerthi, Marcelo H. Ang Jr.
Reference governor control of constrained feedback systems using neural networks. ISIC (2002)
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) (2001)
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) (2001)
K. Sridharan, S. Sathiya Keerthi
Computation of a penetration measure between 3D convex polyhedral objects for collision detection. J. Field Robotics 18 (11) (2001)
S. Sundararajan, S. Sathiya Keerthi
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Comput. 13 (5) (2001)
Wei Chu, S. Sathiya Keerthi, Chong Jin Ong
A Unified Loss Function in Bayesian Framework for Support Vector Regression. ICML (2001)
Chiranjib Bhattacharyya, S. Sathiya Keerthi
Mean Field Methods for a Special Class of Belief Networks. J. Artif. Intell. Res. 15 (2001)
Chiranjib Bhattacharyya, S. Sathiya Keerthi
A Variational Mean-Field Theory for Sigmoidal Belief Networks. NIPS (2000)
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) (2000)
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) (2000)
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) (2000)
K. R. K. Murthy, S. Sathiya Keerthi
Context Filters for Document-based Information Filtering. ICDAR (1999)
C. S. Sundaresan, S. Sathiya Keerthi
A Study of Representations for Pen based Handwriting Recognition of Tamil Characters. ICDAR (1999)
S. Sundararajan, S. Sathiya Keerthi
Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. NIPS (1999)
S. Sathiya Keerthi, Chong Jin Ong, Eugene Huang, Elmer G. Gilbert
EquiDistance Diagram: A New Roadmap Method for Path Planning. ICRA (1999)
Dipti Deodhare, M. Vidyasagar, S. Sathiya Keerthi
Synthesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Trans. Neural Networks 9 (5) (1998)

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Publication Activity (10 Years)

Top Topics

Top Venues

S. Sathiya Keerthi