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ORCIDS. V. N. Vishwanathan
Vishy Vishwanathan
Person information
- affiliation: Purdue University, Department of Statistics and Computer Science, West Lafayette, IN, USA
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Journal Articles
- 2017
[j19]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): 2:1-2:39 (2017)- 2016
- [j18]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Nomadic Computing for Big Data Analytics. Computer 49(4): 52-60 (2016) - 2014
- [j17]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): 975-986 (2014) - [j16]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Accelerated training of max-margin Markov networks with kernels. Theor. Comput. Sci. 519: 88-102 (2014) - 2013
- [j15]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): 2483-2493 (2013) - 2012
- [j14]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing multivariate performance measures. J. Mach. Learn. Res. 13: 3623-3680 (2012) - [j13]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): 127-155 (2012) - 2011
- [j12]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): 2097-2106 (2011) - [j11]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): 91-93 (2011) - 2010
- [j10]Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola, Quoc V. Le:
Bundle Methods for Regularized Risk Minimization. J. Mach. Learn. Res. 11: 311-365 (2010) - [j9]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: 1145-1200 (2010) - [j8]S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt:
Graph Kernels. J. Mach. Learn. Res. 11: 1201-1242 (2010) - 2009
- [j7]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: 2615-2637 (2009) - 2007
- [j6]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): 95-119 (2007) - [j5]Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan:
Fast Iterative Kernel Principal Component Analysis. J. Mach. Learn. Res. 8: 1893-1918 (2007) - 2006
- [j4]S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola:
Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006) - [j3]S. V. N. Vishwanathan, Nicol N. Schraudolph, Alexander J. Smola:
Step Size Adaptation in Reproducing Kernel Hilbert Space. J. Mach. Learn. Res. 7: 1107-1133 (2006) - 2005
- [j2]Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay:
Boîte à outils SVM simple et rapide. Rev. d'Intelligence Artif. 19(4-5): 741-767 (2005) - 2000
- [j1]S. V. N. Vishwanathan, M. Narasimha Murty:
Kohonen's SOM with cache. Pattern Recognit. 33(11): 1927-1929 (2000)
Conference and Workshop Papers
- 2024
- [c77]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: 77-86 - [c76]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: 1626-1633 - 2023
- [c75]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: 73-85 - 2022
- [c74]Ashutosh Joshi, Shankar Vishwanath, Choon Hui Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May:
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores. NAACL-HLT (Industry Papers) 2022: 160-167 - [c73]Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS 2022 - 2019
- [c72]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: 935-943 - [c71]Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft:
A Zero Attention Model for Personalized Product Search. CIKM 2019: 379-388 - [c70]Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan:
Scaling Multinomial Logistic Regression via Hybrid Parallelism. KDD 2019: 1460-1470 - [c69]Weicong Ding, Dinesh Govindaraj, S. V. N. Vishwanathan:
Whole Page Optimization with Global Constraints. KDD 2019: 3153-3161 - 2018
- [c68]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS 2018: 736-744 - [c67]Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan:
Online Learning of Combinatorial Objects via Extended Formulation. ALT 2018: 702-724 - 2017
- [c66]Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan:
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. KDD 2017: 1813-1821 - [c65]Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan:
Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem. ECML/PKDD (1) 2017: 460-476 - 2016
- [c64]Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan:
WordRank: Learning Word Embeddings via Robust Ranking. EMNLP 2016: 658-668 - [c63]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: 35-38 - [c62]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 - 2015
- [c61]Guy Lebanon, S. V. N. Vishwanathan:
Preface. AISTATS 2015 - [c60]Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach:
Autonomous color theme extraction from images using saliency. IMAWM 2015: 940807 - [c59]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: 93940Y - [c58]Pinar Yanardag, S. V. N. Vishwanathan:
Deep Graph Kernels. KDD 2015: 1365-1374 - [c57]Pinar Yanardag, S. V. N. Vishwanathan:
A Structural Smoothing Framework For Robust Graph Comparison. NIPS 2015: 2134-2142 - [c56]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: 1340-1350 - 2014
- [c55]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: 341-350 - [c54]Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan:
Ranking via Robust Binary Classification. NIPS 2014: 2582-2590 - 2013
- [c53]Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang:
Open Problem: Lower bounds for Boosting with Hadamard Matrices. COLT 2013: 1076-1079 - 2012
- [c52]Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven:
Robust Classification with Adiabatic Quantum Optimization. ICML 2012 - [c51]Shin Matsushima, S. V. N. Vishwanathan, Alexander J. Smola:
Linear support vector machines via dual cached loops. KDD 2012: 177-185 - [c50]Ashesh Jain, S. V. N. Vishwanathan, Manik Varma:
SPF-GMKL: generalized multiple kernel learning with a million kernels. KDD 2012: 750-758 - [c49]Amr Ahmed, Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola:
Fair and balanced: learning to present news stories. WSDM 2012: 333-342 - [c48]Hyokun Yun, S. V. N. Vishwanathan:
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. AISTATS 2012: 1389-1397 - 2011
- [c47]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Accelerated Training of Max-Margin Markov Networks with Kernels. ALT 2011: 292-307 - [c46]Yi Fang, Mengtian Sun, S. V. N. Vishwanathan, Karthik Ramani:
sLLE: Spherical locally linear embedding with applications to tomography. CVPR 2011: 1129-1136 - [c45]Nan Ding, S. V. N. Vishwanathan, Yuan (Alan) Qi:
t-divergence Based Approximate Inference. NIPS 2011: 1494-1502 - [c44]Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang:
New Approximation Algorithms for Minimum Enclosing Convex Shapes. SODA 2011: 1146-1160 - [c43]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing Multivariate Performance Measures. UAI 2011: 814-821 - 2010
- [c42]Sebastián Moreno, Sergey Kirshner, Jennifer Neville, S. V. N. Vishwanathan:
Tied Kronecker product graph models to capture variance in network populations. Allerton 2010: 1137-1144 - [c41]Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma:
Large Scale Max-Margin Multi-Label Classification with Priors. ICML 2010: 423-430 - [c40]Nan Ding, S. V. N. Vishwanathan:
t-logistic regression. NIPS 2010: 514-522 - [c39]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson:
Multitask Learning without Label Correspondences. NIPS 2010: 1957-1965 - [c38]S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Ampornpunt, Manik Varma:
Multiple Kernel Learning and the SMO Algorithm. NIPS 2010: 2361-2369 - [c37]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Lower Bounds on Rate of Convergence of Cutting Plane Methods. NIPS 2010: 2541-2549 - 2009
- [c36]Manfred K. Warmuth, S. V. N. Vishwanathan:
Tutorial summary: Survey of boosting from an optimization perspective. ICML 2009: 15 - [c35]Jin Yu, S. V. N. Vishwanathan, Jian Zhang:
The Entire Quantile Path of a Risk-Agnostic SVM Classifier. UAI 2009: 623-630 - [c34]Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt:
Efficient graphlet kernels for large graph comparison. AISTATS 2009: 488-495 - [c33]Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan:
Hash Kernels. AISTATS 2009: 496-503 - [c32]Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph:
Variable Metric Stochastic Approximation Theory. AISTATS 2009: 560-566 - 2008
- [c31]Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan:
Entropy Regularized LPBoost. ALT 2008: 256-271 - [c30]Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang:
Consistent image analogies using semi-supervised learning. CVPR 2008 - [c29]Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph:
A quasi-Newton approach to non-smooth convex optimization. ICML 2008: 1216-1223 - 2007
- [c28]Qinfeng Shi, Yasemin Altun, Alexander J. Smola, S. V. N. Vishwanathan:
Semi-Markov Models for Sequence Segmentation. EMNLP-CoNLL 2007: 640-648 - [c27]Li Cheng, S. V. N. Vishwanathan:
Learning to compress images and videos. ICML 2007: 161-168 - [c26]Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan:
Conditional random fields for multi-agent reinforcement learning. ICML 2007: 1143-1150 - [c25]Choon Hui Teo, Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
A scalable modular convex solver for regularized risk minimization. KDD 2007: 727-736 - [c24]Karsten M. Borgwardt, Tobias Petri, S. V. N. Vishwanathan, Hans-Peter Kriegel:
An Efficient Sampling Scheme For Comparison of Large Graphs. MLG 2007 - [c23]Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
Bundle Methods for Machine Learning. NIPS 2007: 1377-1384 - 2006
- [c22]Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli, S. V. N. Vishwanathan:
An Online Discriminative Approach to Background Subtraction. AVSS 2006: 2 - [c21]Choon Hui Teo, S. V. N. Vishwanathan:
Fast and space efficient string kernels using suffix arrays. ICML 2006: 929-936 - [c20]S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy:
Accelerated training of conditional random fields with stochastic gradient methods. ICML 2006: 969-976 - [c19]Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli:
implicit Online Learning with Kernels. NIPS 2006: 249-256 - [c18]Nicol N. Schraudolph, Simon Günter, S. V. N. Vishwanathan:
Fast Iterative Kernel PCA. NIPS 2006: 1225-1232 - [c17]S. V. N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph:
Fast Computation of Graph Kernels. NIPS 2006: 1449-1456 - [c16]Karsten M. Borgwardt, S. V. N. Vishwanathan, Hans-Peter Kriegel:
Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels. Pacific Symposium on Biocomputing 2006: 547-558 - 2005
- [c15]Alexander J. Smola, S. V. N. Vishwanathan, Thomas Hofmann:
Kernel Methods for Missing Variables. AISTATS 2005: 325-332 - [c14]Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson:
Learnability of Probabilistic Automata via Oracles. ALT 2005: 171-182 - [c13]Manfred K. Warmuth, S. V. N. Vishwanathan:
Leaving the Span. COLT 2005: 366-381 - [c12]Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola:
Joint Regularization. ESANN 2005: 455-460 - [c11]Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel:
Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56 - [c10]Alexandros Karatzoglou, S. V. N. Vishwanathan, Nicol N. Schraudolph, Alexander J. Smola:
Step size-adapted online support vector learning. ISSPA 2005: 823-826 - [c9]Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, S. V. N. Vishwanathan:
Large-Scale Multiclass Transduction. NIPS 2005: 411-418 - [c8]Zhenghua Yu, S. V. N. Vishwanathan, Alex Smola:
NICTA at TRECVID 2005 Shot Boundary Detection Task. TRECVID 2005 - 2004
- [c7]S. V. N. Vishwanathan, Alexander J. Smola:
Binet-Cauchy Kernels. NIPS 2004: 1441-1448 - 2003
- [c6]S. V. N. Vishwanathan, Alexander J. Smola, M. Narasimha Murty:
SimpleSVM. ICML 2003: 760-767 - [c5]Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin:
Laplace Propagation. NIPS 2003: 441-448 - 2002
- [c4]S. V. N. Vishwanathan, M. Narasimha Murty:
Jigsawing : A Method to Create Virtual Examples in OCR data. HIS 2002: 690-696 - [c3]S. V. N. Vishwanathan, M. Narasimha Murty:
Geometric SVM: A Fast and Intuitive SVM Algorithm. ICPR (2) 2002: 56-59 - [c2]S. V. N. Vishwanathan, Alexander J. Smola:
Fast Kernels for String and Tree Matching. NIPS 2002: 569-576 - 2001
- [c1]S. V. N. Vishwanathan, M. Narasimha Murty:
Use of Multi-category Proximal SVM for Data Set Reduction. HIS 2001: 19-24
Editorship
- 2017
- [e2]Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017 [contents] - 2015
- [e1]Guy Lebanon, S. V. N. Vishwanathan:
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015. JMLR Workshop and Conference Proceedings 38, JMLR.org 2015 [contents]
Informal and Other Publications
- 2023
- [i32]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 abs/2312.02429 (2023) - 2022
- [i31]Nan Jiang, Dhivya Eswaran, Choon Hui Teo, Yexiang Xue, Yesh Dattatreya, Sujay Sanghavi, Vishy Vishwanathan:
On the Value of Behavioral Representations for Dense Retrieval. CoRR abs/2208.05663 (2022) - [i30]Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. CoRR abs/2209.08754 (2022) - 2021
- [i29]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 abs/2110.06125 (2021) - 2020
- [i28]Parameswaran Raman, S. V. N. Vishwanathan:
DS-FACTO: Doubly Separable Factorization Machines. CoRR abs/2004.13940 (2020) - 2019
- [i27]Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft:
A Zero Attention Model for Personalized Product Search. CoRR abs/1908.11322 (2019) - 2018
- [i26]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 abs/1810.01477 (2018) - [i25]Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, S. V. N. Vishwanathan:
Diversifying Music Recommendations. CoRR abs/1810.01482 (2018) - [i24]Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan:
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. CoRR abs/1810.09558 (2018) - 2017
- [i23]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR abs/1704.06731 (2017) - [i22]Holakou Rahmanian, S. V. N. Vishwanathan, Manfred K. Warmuth:
Online Dynamic Programming. CoRR abs/1706.00834 (2017) - 2016
- [i21]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 abs/1604.04706 (2016) - [i20]Holakou Rahmanian, S. V. N. Vishwanathan, David P. Helmbold:
Extended Formulation for Online Learning of Combinatorial Objects. CoRR abs/1609.05374 (2016) - 2015
- [i19]Vasil S. Denchev, Nan Ding, Shin Matsushima, S. V. N. Vishwanathan, Hartmut Neven:
Totally Corrective Boosting with Cardinality Penalization. CoRR abs/1504.01446 (2015) - [i18]Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach:
Colors $-$Messengers of Concepts: Visual Design Mining for Learning Color Semantics. CoRR abs/1505.06532 (2015) - [i17]Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan:
WordRank: Learning Word Embeddings via Robust Ranking. CoRR abs/1506.02761 (2015) - 2014
- [i16]Dinesh Govindaraj, Tao Wang, S. V. N. Vishwanathan:
Modeling Attractiveness and Multiple Clicks in Sponsored Search Results. CoRR abs/1401.0255 (2014) - [i15]Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan:
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data. CoRR abs/1402.2676 (2014) - [i14]Pinar Yanardag, S. V. N. Vishwanathan:
The Structurally Smoothed Graphlet Kernel. CoRR abs/1403.0598 (2014) - [i13]Shin Matsushima, Hyokun Yun, S. V. N. Vishwanathan:
Distributed Stochastic Optimization of the Regularized Risk. CoRR abs/1406.4363 (2014) - [i12]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. CoRR abs/1412.4986 (2014) - 2013
- [i11]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 abs/1312.0193 (2013) - 2012
- [i10]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing Multivariate Performance Measures. CoRR abs/1202.3776 (2012) - [i9]Hyokun Yun, S. V. N. Vishwanathan:
Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process. CoRR abs/1202.6001 (2012) - [i8]Jin Yu, S. V. N. Vishwanathan, Jian Zhang:
The Entire Quantile Path of a Risk-Agnostic SVM Classifier. CoRR abs/1205.2602 (2012) - 2011
- [i7]Hyokun Yun, S. V. N. Vishwanathan:
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. CoRR abs/1110.5383 (2011) - 2010
- [i6]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Faster Rates for training Max-Margin Markov Networks. CoRR abs/1003.1354 (2010) - [i5]Feng Yan, S. V. N. Vishwanathan, Yuan (Alan) Qi:
Cooperative Autonomous Online Learning. CoRR abs/1006.4039 (2010) - [i4]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies. CoRR abs/1011.0472 (2010) - 2009
- [i3]Ankan Saha, S. V. N. Vishwanathan:
Efficient Approximation Algorithms for Minimum Enclosing Convex Shapes. CoRR abs/0909.1062 (2009) - [i2]Ankan Saha, Xinhua Zhang, S. V. N. Vishwanathan:
Lower Bounds for BMRM and Faster Rates for Training SVMs. CoRR abs/0909.1334 (2009) - 2008
- [i1]S. V. N. Vishwanathan, Karsten M. Borgwardt, Imre Risi Kondor, Nicol N. Schraudolph:
Graph Kernels. CoRR abs/0807.0093 (2008)
Coauthor Index
Alexander J. Smola
aka: Alex Smola
aka: Alex Smola
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-16 18:31 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
