S. V. N. Vishwanathan

ORCID

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 29

Top Venues

Publications

Sriram Srinivasan, Stephen Sheng, Rishabh Deshmukh, Chen Luo, Yesh Dattatreya, Subhajit Sanyal, S. V. N. Vishwanathan
Bi-CAT: Improving Robustness of LLM-based Text Rankers to Conditional Distribution Shifts. WWW (Companion Volume) (2024)
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. WSDM (2024)
Aashiq Muhamed, Sriram Srinivasan, Choon Hui Teo, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, S. V. N. Vishwanathan
Web-Scale Semantic Product Search with Large Language Models. PAKDD (3) (2023)
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. CoRR (2023)
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan
Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS (2022)
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan
Toward Understanding Privileged Features Distillation in Learning-to-Rank. CoRR (2022)
Vihan Lakshman, Choon Hui Teo, Xiaowen Chu, Priyanka Nigam, Abhinandan Patni, Pooja Maknikar, S. V. N. Vishwanathan
Embracing Structure in Data for Billion-Scale Semantic Product Search. CoRR (2021)
Parameswaran Raman, S. V. N. Vishwanathan
DS-FACTO: Doubly Separable Factorization Machines. CoRR (2020)
Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan
Scaling Multinomial Logistic Regression via Hybrid Parallelism. KDD (2019)
Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. AISTATS (2019)
Weicong Ding, Dinesh Govindaraj, S. V. N. Vishwanathan
Whole Page Optimization with Global Constraints. KDD (2019)
Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft
A Zero Attention Model for Personalized Product Search. CoRR (2019)
Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft
A Zero Attention Model for Personalized Product Search. CIKM (2019)
Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan
Online Learning of Combinatorial Objects via Extended Formulation. ALT (2018)
Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, S. V. N. Vishwanathan
Diversifying Music Recommendations. CoRR (2018)
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS (2018)
Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan
Adaptive, Personalized Diversity for Visual Discovery. CoRR (2018)
Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. CoRR (2018)
Ali Jahanian, Shaiyan Keshvari, S. V. N. Vishwanathan, Jan P. Allebach
Colors - Messengers of Concepts: Visual Design Mining for Learning Color Semantics. ACM Trans. Comput. Hum. Interact. 24 (1) (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)
Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. KDD (2017)
Holakou Rahmanian, S. V. N. Vishwanathan, Manfred K. Warmuth
Online Dynamic Programming. CoRR (2017)
Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan
Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem. ECML/PKDD (1) (2017)
Parameswaran Raman, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression. CoRR (2016)
Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan
Adaptive, Personalized Diversity for Visual Discovery. RecSys (2016)
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon
Nomadic Computing for Big Data Analytics. Computer 49 (4) (2016)
Holakou Rahmanian, S. V. N. Vishwanathan, David P. Helmbold
Extended Formulation for Online Learning of Combinatorial Objects. CoRR (2016)
Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan
WordRank: Learning Word Embeddings via Robust Ranking. EMNLP (2016)
Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson, Pradeep Dubey
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies. ICLR (2016)
Pinar Yanardag, S. V. N. Vishwanathan
A Structural Smoothing Framework For Robust Graph Comparison. NIPS (2015)
Vasil S. Denchev, Nan Ding, Shin Matsushima, S. V. N. Vishwanathan, Hartmut Neven
Totally Corrective Boosting with Cardinality Penalization. CoRR (2015)
Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach
Autonomous color theme extraction from images using saliency. IMAWM (2015)
Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach
Colors $-$Messengers of Concepts: Visual Design Mining for Learning Color Semantics. CoRR (2015)
Pinar Yanardag, S. V. N. Vishwanathan
Deep Graph Kernels. KDD (2015)
Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan
WordRank: Learning Word Embeddings via Robust Ranking. CoRR (2015)
Guy Lebanon, S. V. N. Vishwanathan
Preface. AISTATS (2015)
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. WWW (2015)
Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach
Learning visual balance from large-scale datasets of aesthetically highly rated images. Human Vision and Electronic Imaging (2015)
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. CoRR (2014)
Pinar Yanardag, S. V. N. Vishwanathan
The Structurally Smoothed Graphlet Kernel. CoRR (2014)
Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan
Ranking via Robust Binary Classification. NIPS (2014)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Accelerated training of max-margin Markov networks with kernels. Theor. Comput. Sci. 519 (2014)
Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data. CoRR (2014)
Shin Matsushima, Hyokun Yun, S. V. N. Vishwanathan
Distributed Stochastic Optimization of the Regularized Risk. CoRR (2014)
William Benjamin, Senthil K. Chandrasegaran, Devarajan Ramanujan, Niklas Elmqvist, S. V. N. Vishwanathan, Karthik Ramani
Juxtapoze: supporting serendipity and creative expression in clipart compositions. CHI (2014)
Dinesh Govindaraj, Tao Wang, S. V. N. Vishwanathan
Modeling Attractiveness and Multiple Clicks in Sponsored Search Results. CoRR (2014)
Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon
NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. Proc. VLDB Endow. 7 (11) (2014)
Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang
Open Problem: Lower bounds for Boosting with Hadamard Matrices. COLT (2013)
Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. CoRR (2013)
Feng Yan, Shreyas Sundaram, S. V. N. Vishwanathan, Yuan (Alan) Qi
Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties. IEEE Trans. Knowl. Data Eng. 25 (11) (2013)
Amr Ahmed, Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola
Fair and balanced: learning to present news stories. WSDM (2012)
Ashesh Jain, S. V. N. Vishwanathan, Manik Varma
SPF-GMKL: generalized multiple kernel learning with a million kernels. KDD (2012)
Hyokun Yun, S. V. N. Vishwanathan
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. AISTATS (2012)
Jin Yu, S. V. N. Vishwanathan, Jian Zhang
The Entire Quantile Path of a Risk-Agnostic SVM Classifier CoRR (2012)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Smoothing multivariate performance measures. J. Mach. Learn. Res. 13 (2012)
Bharath Hariharan, S. V. N. Vishwanathan, Manik Varma
Efficient max-margin multi-label classification with applications to zero-shot learning. Mach. Learn. 88 (1-2) (2012)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Smoothing Multivariate Performance Measures CoRR (2012)
Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven
Robust Classification with Adiabatic Quantum Optimization. ICML (2012)
Hyokun Yun, S. V. N. Vishwanathan
Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process CoRR (2012)
Shin Matsushima, S. V. N. Vishwanathan, Alexander J. Smola
Linear support vector machines via dual cached loops. KDD (2012)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Accelerated Training of Max-Margin Markov Networks with Kernels. ALT (2011)
Hyokun Yun, S. V. N. Vishwanathan
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs CoRR (2011)
Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang
New Approximation Algorithms for Minimum Enclosing Convex Shapes. SODA (2011)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Smoothing Multivariate Performance Measures. UAI (2011)
William Benjamin, Andrew Wood Polk, S. V. N. Vishwanathan, Karthik Ramani
Heat Walk: Robust Salient Segmentation of Non-rigid Shapes. Comput. Graph. Forum 30 (7) (2011)
Nan Ding, S. V. N. Vishwanathan, Yuan (Alan) Qi
t-divergence Based Approximate Inference. NIPS (2011)
Yi Fang, Mengtian Sun, S. V. N. Vishwanathan, Karthik Ramani
sLLE: Spherical locally linear embedding with applications to tomography. CVPR (2011)
S. V. N. Vishwanathan, Samuel Kaski, Jennifer Neville, Stefan Wrobel
Introduction to the special issue on mining and learning with graphs. Mach. Learn. 82 (2) (2011)
Feng Yan, S. V. N. Vishwanathan, Yuan (Alan) Qi
Cooperative Autonomous Online Learning CoRR (2010)
Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola, Quoc V. Le
Bundle Methods for Regularized Risk Minimization. J. Mach. Learn. Res. 11 (2010)
Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. J. Mach. Learn. Res. 11 (2010)
Nan Ding, S. V. N. Vishwanathan
t-logistic regression. NIPS (2010)
S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Ampornpunt, Manik Varma
Multiple Kernel Learning and the SMO Algorithm. NIPS (2010)
Sebastián Moreno, Sergey Kirshner, Jennifer Neville, S. V. N. Vishwanathan
Tied Kronecker product graph models to capture variance in network populations. Allerton (2010)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Faster Rates for training Max-Margin Markov Networks CoRR (2010)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Lower Bounds on Rate of Convergence of Cutting Plane Methods. NIPS (2010)
Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson
Multitask Learning without Label Correspondences. NIPS (2010)
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies CoRR (2010)
S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt
Graph Kernels. J. Mach. Learn. Res. 11 (2010)
Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma
Large Scale Max-Margin Multi-Label Classification with Priors. ICML (2010)
Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt
Efficient graphlet kernels for large graph comparison. AISTATS (2009)
Manfred K. Warmuth, S. V. N. Vishwanathan
Tutorial summary: Survey of boosting from an optimization perspective. ICML (2009)
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan
Hash Kernels for Structured Data. J. Mach. Learn. Res. 10 (2009)
Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph
Variable Metric Stochastic Approximation Theory. AISTATS (2009)
Ankan Saha, Xinhua Zhang, S. V. N. Vishwanathan
Lower Bounds for BMRM and Faster Rates for Training SVMs CoRR (2009)
Jin Yu, S. V. N. Vishwanathan, Jian Zhang
The Entire Quantile Path of a Risk-Agnostic SVM Classifier. UAI (2009)
Ankan Saha, S. V. N. Vishwanathan
Efficient Approximation Algorithms for Minimum Enclosing Convex Shapes CoRR (2009)
S. V. N. Vishwanathan, Karsten M. Borgwardt, Imre Risi Kondor, Nicol N. Schraudolph
Graph Kernels CoRR (2008)
Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan
Entropy Regularized LPBoost. ALT (2008)
Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang
Consistent image analogies using semi-supervised learning. CVPR (2008)
Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph
A quasi-Newton approach to non-smooth convex optimization. ICML (2008)
Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le
Bundle Methods for Machine Learning. NIPS (2007)
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan
Conditional random fields for multi-agent reinforcement learning. ICML (2007)
Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan
Fast Iterative Kernel Principal Component Analysis. J. Mach. Learn. Res. 8 (2007)
S. V. N. Vishwanathan, Alexander J. Smola, René Vidal
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. Int. J. Comput. Vis. 73 (1) (2007)
Li Cheng, S. V. N. Vishwanathan
Learning to compress images and videos. ICML (2007)
Choon Hui Teo, Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le
A scalable modular convex solver for regularized risk minimization. KDD (2007)
Karsten M. Borgwardt, Tobias Petri, S. V. N. Vishwanathan, Hans-Peter Kriegel
An Efficient Sampling Scheme For Comparison of Large Graphs. MLG (2007)
Qinfeng Shi, Yasemin Altun, Alexander J. Smola, S. V. N. Vishwanathan
Semi-Markov Models for Sequence Segmentation. EMNLP-CoNLL (2007)
S. V. N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph
Fast Computation of Graph Kernels. NIPS (2006)

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Publication Activity (10 Years)

Top Topics

Top Venues

S. V. N. Vishwanathan