Kohonen's SOM with cache. - S. V. N. Vishwanathan; M. Narasimha Murty (Pattern Recognit., 2000)

Use of Multi-category Proximal SVM for Data Set Reduction. - S. V. N. Vishwanathan; M. Narasimha Murty (HIS, 2001)

Jigsawing : A Method to Create Virtual Examples in OCR data. - S. V. N. Vishwanathan; M. Narasimha Murty (HIS, 2002)

Geometric SVM: A Fast and Intuitive SVM Algorithm. - S. V. N. Vishwanathan; M. Narasimha Murty (ICPR (2), 2002)

Fast Kernels for String and Tree Matching. - S. V. N. Vishwanathan; Alexander J. Smola (NIPS, 2002)

SimpleSVM. - S. V. N. Vishwanathan; Alexander J. Smola; M. Narasimha Murty (ICML, 2003)

Laplace Propagation. - Alexander J. Smola; Vishy Vishwanathan; Eleazar Eskin (NIPS, 2003)

Binet-Cauchy Kernels. - S. V. N. Vishwanathan; Alexander J. Smola (NIPS, 2004)

Kernel Methods for Missing Variables. - Alexander J. Smola; S. V. N. Vishwanathan; Thomas Hofmann 001 (AISTATS, 2005)

Learnability of Probabilistic Automata via Oracles. - Omri Guttman; S. V. N. Vishwanathan; Robert C. Williamson (ALT, 2005)

Leaving the Span. - Manfred K. Warmuth; S. V. N. Vishwanathan (COLT, 2005)

Joint Regularization. - Karsten M. Borgwardt; Omri Guttman; S. V. N. Vishwanathan; Alexander J. Smola (ESANN, 2005)

Protein function prediction via graph kernels. - Karsten M. Borgwardt; Cheng Soon Ong; Stefan Schönauer; S. V. N. Vishwanathan; Alexander J. Smola; Hans-Peter Kriegel (ISMB (Supplement of Bioinformatics), 2005)

Step size-adapted online support vector learning. - Alexandros Karatzoglou; S. V. N. Vishwanathan; Nicol N. Schraudolph; Alexander J. Smola (ISSPA, 2005)

Large-Scale Multiclass Transduction. - Thomas Gärtner 001; Quoc V. Le; Simon Burton 003; Alexander J. Smola; S. V. N. Vishwanathan (NIPS, 2005)

NICTA at TRECVID 2005 Shot Boundary Detection Task. - Zhenghua Yu; S. V. N. Vishwanathan; Alex Smola (TRECVID, 2005)

Boîte à outils SVM simple et rapide. - Gaëlle Loosli; Stéphane Canu; S. V. N. Vishwanathan; Alexander J. Smola; M. Chattopadhyay (Rev. d'Intelligence Artif., 2005)

An Online Discriminative Approach to Background Subtraction. - Li Cheng 001; Shaojun Wang; Dale Schuurmans; Terry Caelli; S. V. N. Vishwanathan (AVSS, 2006)

Fast and space efficient string kernels using suffix arrays. - Choon Hui Teo; S. V. N. Vishwanathan (ICML, 2006)

Accelerated training of conditional random fields with stochastic gradient methods. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Mark W. Schmidt; Kevin P. Murphy (ICML, 2006)

implicit Online Learning with Kernels. - Li Cheng 001; S. V. N. Vishwanathan; Dale Schuurmans; Shaojun Wang; Terry Caelli (NIPS, 2006)

Fast Iterative Kernel PCA. - Nicol N. Schraudolph; Simon Günter; S. V. N. Vishwanathan (NIPS, 2006)

Fast Computation of Graph Kernels. - S. V. N. Vishwanathan; Karsten M. Borgwardt; Nicol N. Schraudolph (NIPS, 2006)

Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels. - Karsten M. Borgwardt; S. V. N. Vishwanathan; Hans-Peter Kriegel (Pacific Symposium on Biocomputing, 2006)

Kernel extrapolation. - S. V. N. Vishwanathan; Karsten M. Borgwardt; Omri Guttman; Alexander J. Smola (Neurocomputing, 2006)

Step Size Adaptation in Reproducing Kernel Hilbert Space. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Alexander J. Smola (J. Mach. Learn. Res., 2006)

Semi-Markov Models for Sequence Segmentation. - Qinfeng Shi; Yasemin Altun; Alexander J. Smola; S. V. N. Vishwanathan (EMNLP-CoNLL, 2007)

Learning to compress images and videos. - Li Cheng 001; S. V. N. Vishwanathan (ICML, 2007)

Conditional random fields for multi-agent reinforcement learning. - Xinhua Zhang; Douglas Aberdeen; S. V. N. Vishwanathan (ICML, 2007)

A scalable modular convex solver for regularized risk minimization. - Choon Hui Teo; Alexander J. Smola; S. V. N. Vishwanathan; Quoc V. Le (KDD, 2007)

An Efficient Sampling Scheme For Comparison of Large Graphs. - Karsten M. Borgwardt; Tobias Petri; S. V. N. Vishwanathan; Hans-Peter Kriegel (MLG, 2007)

Bundle Methods for Machine Learning. - Alexander J. Smola; S. V. N. Vishwanathan; Quoc V. Le (NIPS, 2007)

Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. - S. V. N. Vishwanathan; Alexander J. Smola; René Vidal (Int. J. Comput. Vis., 2007)

Fast Iterative Kernel Principal Component Analysis. - Simon Günter; Nicol N. Schraudolph; S. V. N. Vishwanathan (J. Mach. Learn. Res., 2007)

Entropy Regularized LPBoost. - Manfred K. Warmuth; Karen A. Glocer; S. V. N. Vishwanathan (ALT, 2008)

Consistent image analogies using semi-supervised learning. - Li Cheng 001; S. V. N. Vishwanathan; Xinhua Zhang (CVPR, 2008)

A quasi-Newton approach to non-smooth convex optimization. - Jin Yu; S. V. N. Vishwanathan; Simon Günter; Nicol N. Schraudolph (ICML, 2008)

Tutorial summary: Survey of boosting from an optimization perspective. - Manfred K. Warmuth; S. V. N. Vishwanathan (ICML, 2009)

The Entire Quantile Path of a Risk-Agnostic SVM Classifier. - Jin Yu; S. V. N. Vishwanathan; Jian Zhang 003 (UAI, 2009)

Efficient Approximation Algorithms for Minimum Enclosing Convex Shapes - Ankan Saha; S. V. N. Vishwanathan (CoRR, 2009)

Lower Bounds for BMRM and Faster Rates for Training SVMs - Ankan Saha; Xinhua Zhang; S. V. N. Vishwanathan (CoRR, 2009)

Efficient graphlet kernels for large graph comparison. - Nino Shervashidze; S. V. N. Vishwanathan; Tobias Petri; Kurt Mehlhorn; Karsten M. Borgwardt (AISTATS, 2009)

Hash Kernels. - Qinfeng Shi; James Petterson; Gideon Dror; John Langford 001; Alexander J. Smola; Alexander L. Strehl; Vishy Vishwanathan (AISTATS, 2009)

Hash Kernels for Structured Data. - Qinfeng Shi; James Petterson; Gideon Dror; John Langford 001; Alexander J. Smola; S. V. N. Vishwanathan (J. Mach. Learn. Res., 2009)

Variable Metric Stochastic Approximation Theory. - Peter Sunehag; Jochen Trumpf; S. V. N. Vishwanathan; Nicol N. Schraudolph (AISTATS, 2009)

Tied Kronecker product graph models to capture variance in network populations. - Sebastián Moreno; Sergey Kirshner; Jennifer Neville; S. V. N. Vishwanathan (Allerton, 2010)

Large Scale Max-Margin Multi-Label Classification with Priors. - Bharath Hariharan; Lihi Zelnik-Manor; S. V. N. Vishwanathan; Manik Varma (ICML, 2010)

t-logistic regression. - Nan Ding 002; S. V. N. Vishwanathan (NIPS, 2010)

Multitask Learning without Label Correspondences. - Novi Quadrianto; Alexander J. Smola; Tibério S. Caetano; S. V. N. Vishwanathan; James Petterson (NIPS, 2010)

Multiple Kernel Learning and the SMO Algorithm. - S. V. N. Vishwanathan; Zhaonan Sun; Nawanol Ampornpunt; Manik Varma (NIPS, 2010)

Lower Bounds on Rate of Convergence of Cutting Plane Methods. - Xinhua Zhang; Ankan Saha; S. V. N. Vishwanathan (NIPS, 2010)

Faster Rates for training Max-Margin Markov Networks - Xinhua Zhang; Ankan Saha; S. V. N. Vishwanathan (CoRR, 2010)

Cooperative Autonomous Online Learning - Feng Yan 003; S. V. N. Vishwanathan; Yuan (Alan) Qi (CoRR, 2010)

Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies - Xinhua Zhang; Ankan Saha; S. V. N. Vishwanathan (CoRR, 2010)

Bundle Methods for Regularized Risk Minimization. - Choon Hui Teo; S. V. N. Vishwanathan; Alexander J. Smola; Quoc V. Le (J. Mach. Learn. Res., 2010)

Graph Kernels. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Risi Kondor; Karsten M. Borgwardt (J. Mach. Learn. Res., 2010)

A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. - Jin Yu; S. V. N. Vishwanathan; Simon Günter; Nicol N. Schraudolph (J. Mach. Learn. Res., 2010)

sLLE: Spherical locally linear embedding with applications to tomography. - Yi Fang 006; Mengtian Sun; S. V. N. Vishwanathan; Karthik Ramani (CVPR, 2011)

t-divergence Based Approximate Inference. - Nan Ding 002; S. V. N. Vishwanathan; Yuan (Alan) Qi (NIPS, 2011)

New Approximation Algorithms for Minimum Enclosing Convex Shapes. - Ankan Saha; S. V. N. Vishwanathan; Xinhua Zhang (SODA, 2011)

Heat Walk: Robust Salient Segmentation of Non-rigid Shapes. - William Benjamin; Andrew Wood Polk; S. V. N. Vishwanathan; Karthik Ramani (Comput. Graph. Forum, 2011)

Introduction to the special issue on mining and learning with graphs. - S. V. N. Vishwanathan; Samuel Kaski; Jennifer Neville; Stefan Wrobel (Mach. Learn., 2011)

Robust Classification with Adiabatic Quantum Optimization. - Vasil S. Denchev; Nan Ding 002; S. V. N. Vishwanathan; Hartmut Neven (ICML, 2012)

SPF-GMKL: generalized multiple kernel learning with a million kernels. - Ashesh Jain; S. V. N. Vishwanathan; Manik Varma (KDD, 2012)

Linear support vector machines via dual cached loops. - Shin Matsushima; S. V. N. Vishwanathan; Alexander J. Smola (KDD, 2012)

Fair and balanced: learning to present news stories. - Amr Ahmed 001; Choon Hui Teo; S. V. N. Vishwanathan; Alexander J. Smola (WSDM, 2012)

Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process - Hyokun Yun; S. V. N. Vishwanathan (CoRR, 2012)

Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. - Hyokun Yun; S. V. N. Vishwanathan (AISTATS, 2012)

Smoothing multivariate performance measures. - Xinhua Zhang; Ankan Saha; S. V. N. Vishwanathan (J. Mach. Learn. Res., 2012)

Efficient max-margin multi-label classification with applications to zero-shot learning. - Bharath Hariharan; S. V. N. Vishwanathan; Manik Varma (Mach. Learn., 2012)

Open Problem: Lower bounds for Boosting with Hadamard Matrices. - Jiazhong Nie; Manfred K. Warmuth; S. V. N. Vishwanathan; Xinhua Zhang (COLT, 2013)

NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. - Hyokun Yun; Hsiang-Fu Yu; Cho-Jui Hsieh; S. V. N. Vishwanathan; Inderjit S. Dhillon (CoRR, 2013)

Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties. - Feng Yan 003; Shreyas Sundaram; S. V. N. Vishwanathan; Yuan (Alan) Qi (IEEE Trans. Knowl. Data Eng., 2013)

Juxtapoze: supporting serendipity and creative expression in clipart compositions. - William Benjamin; Senthil K. Chandrasegaran; Devarajan Ramanujan; Niklas Elmqvist; S. V. N. Vishwanathan; Karthik Ramani (CHI, 2014)

Ranking via Robust Binary Classification. - Hyokun Yun; Parameswaran Raman; S. V. N. Vishwanathan (NIPS, 2014)

Modeling Attractiveness and Multiple Clicks in Sponsored Search Results. - Dinesh Govindaraj; Tao Wang; S. V. N. Vishwanathan (CoRR, 2014)

Distributed Stochastic Optimization of the Regularized Risk. - Shin Matsushima; Hyokun Yun; S. V. N. Vishwanathan (CoRR, 2014)

The Structurally Smoothed Graphlet Kernel. - Pinar Yanardag; S. V. N. Vishwanathan (CoRR, 2014)

Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data. - Hyokun Yun; Parameswaran Raman; S. V. N. Vishwanathan (CoRR, 2014)

NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. - Hyokun Yun; Hsiang-Fu Yu; Cho-Jui Hsieh; S. V. N. Vishwanathan; Inderjit S. Dhillon (Proc. VLDB Endow., 2014)

Accelerated training of max-margin Markov networks with kernels. - Xinhua Zhang; Ankan Saha; S. V. N. Vishwanathan (Theor. Comput. Sci., 2014)

Preface. - Guy Lebanon; S. V. N. Vishwanathan (AISTATS, 2015)

Autonomous color theme extraction from images using saliency. - Ali Jahanian 002; S. V. N. Vishwanathan; Jan P. Allebach (IMAWM, 2015)

Learning visual balance from large-scale datasets of aesthetically highly rated images. - Ali Jahanian 002; S. V. N. Vishwanathan; Jan P. Allebach (Human Vision and Electronic Imaging, 2015)

Deep Graph Kernels. - Pinar Yanardag; S. V. N. Vishwanathan (KDD, 2015)

A Structural Smoothing Framework For Robust Graph Comparison. - Pinar Yanardag; S. V. N. Vishwanathan (NIPS, 2015)

A Scalable Asynchronous Distributed Algorithm for Topic Modeling. - Hsiang-Fu Yu; Cho-Jui Hsieh; Hyokun Yun; S. V. N. Vishwanathan; Inderjit S. Dhillon (WWW, 2015)

Colors $-$Messengers of Concepts: Visual Design Mining for Learning Color Semantics. - Ali Jahanian 002; S. V. N. Vishwanathan; Jan P. Allebach (CoRR, 2015)

Totally Corrective Boosting with Cardinality Penalization. - Vasil S. Denchev; Nan Ding 002; Shin Matsushima; S. V. N. Vishwanathan; Hartmut Neven (CoRR, 2015)

WordRank: Learning Word Embeddings via Robust Ranking. - Shihao Ji 001; Hyokun Yun; Pinar Yanardag; Shin Matsushima; S. V. N. Vishwanathan (EMNLP, 2016)

Adaptive, Personalized Diversity for Visual Discovery. - Choon Hui Teo; Houssam Nassif; Daniel N. Hill; Sriram Srinivasan 004; Mitchell Goodman; Vijai Mohan; S. V. N. Vishwanathan (RecSys, 2016)

Nomadic Computing for Big Data Analytics. - Hsiang-Fu Yu; Cho-Jui Hsieh; Hyokun Yun; S. V. N. Vishwanathan; Inderjit S. Dhillon (Computer, 2016)

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies. - Shihao Ji 001; S. V. N. Vishwanathan; Nadathur Satish; Michael J. Anderson; Pradeep Dubey (ICLR, 2016)

Extended Formulation for Online Learning of Combinatorial Objects. - Holakou Rahmanian; S. V. N. Vishwanathan; David P. Helmbold (CoRR, 2016)

DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression. - Parameswaran Raman; Shin Matsushima; Xinhua Zhang; Hyokun Yun; S. V. N. Vishwanathan (CoRR, 2016)

An Efficient Bandit Algorithm for Realtime Multivariate Optimization. - Daniel N. Hill; Houssam Nassif; Yi Liu 033; Anand Iyer; S. V. N. Vishwanathan (KDD, 2017)

Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem. - Shin Matsushima; Hyokun Yun; Xinhua Zhang; S. V. N. Vishwanathan (ECML/PKDD (1), 2017)

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. - Michal Derezinski; Dhruv Mahajan 001; S. Sathiya Keerthi; S. V. N. Vishwanathan; Markus Weimer (CoRR, 2017)

Online Dynamic Programming. - Holakou Rahmanian; S. V. N. Vishwanathan; Manfred K. Warmuth (CoRR, 2017)

Colors - Messengers of Concepts: Visual Design Mining for Learning Color Semantics. - Ali Jahanian 002; Shaiyan Keshvari; S. V. N. Vishwanathan; Jan P. Allebach (ACM Trans. Comput. Hum. Interact., 2017)

Batch-Expansion Training: An Efficient Optimization Framework. - Michal Derezinski; Dhruv Mahajan 001; S. Sathiya Keerthi; S. V. N. Vishwanathan; Markus Weimer (AISTATS, 2018)

Online Learning of Combinatorial Objects via Extended Formulation. - Holakou Rahmanian; David P. Helmbold; S. V. N. Vishwanathan (ALT, 2018)

Diversifying Music Recommendations. - Houssam Nassif; Kemal Oral Cansizlar; Mitchell Goodman; S. V. N. Vishwanathan (CoRR, 2018)

Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. - Jiong Zhang; Parameswaran Raman; Shihao Ji 001; Hsiang-Fu Yu; S. V. N. Vishwanathan; Inderjit S. Dhillon (AISTATS, 2019)

A Zero Attention Model for Personalized Product Search. - Qingyao Ai; Daniel N. Hill; S. V. N. Vishwanathan; W. Bruce Croft (CIKM, 2019)

Whole Page Optimization with Global Constraints. - Weicong Ding; Dinesh Govindaraj; S. V. N. Vishwanathan (KDD, 2019)

Scaling Multinomial Logistic Regression via Hybrid Parallelism. - Parameswaran Raman; Sriram Srinivasan 004; Shin Matsushima; Xinhua Zhang; Hyokun Yun; S. V. N. Vishwanathan (KDD, 2019)

DS-FACTO: Doubly Separable Factorization Machines. - Parameswaran Raman; S. V. N. Vishwanathan (CoRR, 2020)

Embracing Structure in Data for Billion-Scale Semantic Product Search. - Vihan Lakshman; Choon Hui Teo; Xiaowen Chu 002; Priyanka Nigam; Abhinandan Patni; Pooja Maknikar; S. V. N. Vishwanathan (CoRR, 2021)

Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores. - Ashutosh Joshi; Shankar Vishwanath; Choon Hui Teo; Vaclav Petricek; Vishy Vishwanathan; Rahul Bhagat; Jonathan May (NAACL-HLT (Industry Papers), 2022)

Toward Understanding Privileged Features Distillation in Learning-to-Rank. - Shuo Yang; Sujay Sanghavi; Holakou Rahmanian; Jan Bakus; S. V. N. Vishwanathan (NeurIPS, 2022)

On the Value of Behavioral Representations for Dense Retrieval. - Nan Jiang 012; Dhivya Eswaran; Choon Hui Teo; Yexiang Xue; Yesh Dattatreya; Sujay Sanghavi; Vishy Vishwanathan (CoRR, 2022)

Web-Scale Semantic Product Search with Large Language Models. - Aashiq Muhamed; Sriram Srinivasan 004; Choon Hui Teo; Qingjun Cui; Belinda Zeng; Trishul Chilimbi; S. V. N. Vishwanathan (PAKDD (3), 2023)

PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. - Wei-Cheng Chang; Jyun-Yu Jiang; Jiong Zhang; Mutasem Al-Darabsah; Choon Hui Teo; Cho-Jui Hsieh; Hsiang-Fu Yu; S. V. N. Vishwanathan (WSDM, 2024)

Bi-CAT: Improving Robustness of LLM-based Text Rankers to Conditional Distribution Shifts. - Sriram Srinivasan 004; Stephen Sheng; Rishabh Deshmukh; Chen Luo 003; Yesh Dattatreya; Subhajit Sanyal; S. V. N. Vishwanathan (WWW (Companion Volume), 2024)

Retrieval-augmented Encoders for Extreme Multi-label Text Classification. - Yau-Shian Wang; Wei-Cheng Chang; Jyun-Yu Jiang; Jiong Zhang; Hsiang-Fu Yu; S. V. N. Vishwanathan (CoRR, 2025)