Vishwanathan, S.V.N.

48 publications

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

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

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

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

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

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

NeurIPS 2015 A Structural Smoothing Framework for Robust Graph Comparison Pinar Yanardag, S.V.N. Vishwanathan

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

MLJ 2012 Efficient Max-Margin Multi-Label Classification with Applications to Zero-Shot Learning Bharath Hariharan, S. V. N. Vishwanathan, Manik Varma

AISTATS 2012 Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs Hyokun Yun, S V N Vishwanathan

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

JMLR 2012 Smoothing Multivariate Performance Measures Xinhua Zhang, Ankan Saha, S.V.N. Vishwanathan

ALT 2011 Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

UAI 2011 Smoothing Multivariate Performance Measures Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

NeurIPS 2011 T-Divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan

CVPR 2011 sLLE: Spherical Locally Linear Embedding with Applications to Tomography Yi Fang, Mengtian Sun, S. V. N. Vishwanathan, Karthik Ramani

JMLR 2010 A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning Jin Yu, S.V.N. Vishwanathan, Simon Günter, Nicol N. Schraudolph

JMLR 2010 Graph Kernels S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt

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

NeurIPS 2010 Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang, Ankan Saha, S.v.n. Vishwanathan

NeurIPS 2010 Multiple Kernel Learning and the SMO Algorithm Zhaonan Sun, Nawanol Ampornpunt, Manik Varma, S.v.n. Vishwanathan

NeurIPS 2010 Multitask Learning Without Label Correspondences Novi Quadrianto, James Petterson, Tibério S. Caetano, Alex J. Smola, S.v.n. Vishwanathan

NeurIPS 2010 T-Logistic Regression Nan Ding, S.v.n. Vishwanathan

AISTATS 2009 Hash Kernels Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, S. V. N. Vishwanathan

JMLR 2009 Hash Kernels for Structured Data Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan

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

ICML 2009 Tutorial Summary: Survey of Boosting from an Optimization Perspective Manfred K. Warmuth, S. V. N. Vishwanathan

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

ICML 2008 A Quasi-Newton Approach to Non-Smooth Convex Optimization Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph

CVPR 2008 Consistent Image Analogies Using Semi-Supervised Learning Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang

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

NeurIPS 2007 Bundle Methods for Machine Learning Quoc V. Le, Alex J. Smola, S.v.n. Vishwanathan

ICML 2007 Conditional Random Fields for Multi-Agent Reinforcement Learning Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan

JMLR 2007 Fast Iterative Kernel Principal Component Analysis Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan

ICML 2007 Learning to Compress Images and Videos Li Cheng, 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

NeurIPS 2006 Fast Computation of Graph Kernels Karsten Borgwardt, Nicol N. Schraudolph, S.v.n. Vishwanathan

NeurIPS 2006 Fast Iterative Kernel PCA Nicol N. Schraudolph, Simon Günter, S.v.n. Vishwanathan

ICML 2006 Fast and Space Efficient String Kernels Using Suffix Arrays Choon Hui Teo, S. V. N. Vishwanathan

NeurIPS 2006 Implicit Online Learning with Kernels Li Cheng, Dale Schuurmans, Shaojun Wang, Terry Caelli, S.v.n. Vishwanathan

JMLR 2006 Step Size Adaptation in Reproducing Kernel Hilbert Space S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola

AISTATS 2005 Kernel Methods for Missing Variables Alex J. Smola, S. V. N. Vishwanathan, Thomas Hofmann

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

COLT 2005 Leaving the Span Manfred K. Warmuth, S. V. N. Vishwanathan

NeurIPS 2004 Binet-Cauchy Kernels Alex J. Smola, S.v.n. Vishwanathan

NeurIPS 2003 Laplace Propagation Eleazar Eskin, Alex J. Smola, S.v.n. Vishwanathan

ICML 2003 SimpleSVM S. V. N. Vishwanathan, Alexander J. Smola, M. Narasimha Murty

NeurIPS 2002 Fast Kernels for String and Tree Matching Alex J. Smola, S.v.n. Vishwanathan