Vikas Sindhwani

According to our database, Vikas Sindhwani authored at least 103 papers between 2001 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Single-Level Differentiable Contact Simulation.

Simon Le Cleac'h, Mac Schwager, Zachary Manchester, Vikas Sindhwani, Pete Florence, Sumeet Singh

IEEE Robotics Autom. Lett., July, 2023

SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention.

Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael S. Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamás Sarlós, Ken Oslund, Karol Hausman, Kanishka Rao

CoRR, 2023

Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models.

Sumeet Singh, Stephen Tu, Vikas Sindhwani

CoRR, 2023

Robotic Table Tennis: A Case Study into a High Speed Learning System.

David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan, Michael Ahn, Alex Bewley, Justin Boyd, Krzysztof Choromanski, Omar Cortes, Erwin Coumans, Tianli Ding, Wenbo Gao, Laura Graesser, Atil Iscen, Navdeep Jaitly, Deepali Jain, Juhana Kangaspunta, Satoshi Kataoka, Gus Kouretas, Yuheng Kuang, Nevena Lazic, Corey Lynch, Reza Mahjourian, Sherry Q. Moore, Thinh Nguyen, Ken Oslund, Barney J. Reed, Krista Reymann, Pannag R. Sanketi, Anish Shankar, Pierre Sermanet, Vikas Sindhwani, Avi Singh, Vincent Vanhoucke, Grace Vesom, Peng Xu

CoRR, 2023

Barkour: Benchmarking Animal-level Agility with Quadruped Robots.

Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, Jose Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Fereshteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan

CoRR, 2023

Safely Learning Dynamical Systems.

Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

CoRR, 2023

Demonstrating Large Language Models on Robots.

Andy Zeng, Brian Ichter, Fei Xia, Ted Xiao, Vikas Sindhwani

Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

Robotic Table Tennis: A Case Study into a High Speed Learning System.

David B. D'Ambrosio, Navdeep Jaitly, Vikas Sindhwani, Ken Oslund, Peng Xu, Nevena Lazic, Anish Shankar, Tianli Ding, Jonathan Abelian, Erwin Coumans, Gus Kouretas, Thinh Nguyen, Justin Boyd, Atil Iscen, Reza Mahjourian, Vincent Vanhoucke, Alex Bewley, Yuheng Kuang, Michael Ahn, Deepali Jain, Satoshi Kataoka, Omar E. Cortes, Pierre Sermanet, Corey Lynch, Pannag R. Sanketi, Krzysztof Choromanski, Wenbo Gao, Juhana Kangaspunta, Krista Reymann, Grace Vesom, Sherry Q. Moore, Avi Singh, Saminda Wishwajith Abeyruwan, Laura Graesser

Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

Mnemosyne: Learning to Train Transformers with Transformers.

Deepali Jain, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Agile Catching with Whole-Body MPC and Blackbox Policy Learning.

Saminda Abeyruwan, Alex Bewley, Nicholas Matthew Boffi, Krzysztof Marcin Choromanski, David B. D'Ambrosio, Deepali Jain, Pannag R. Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques E. Slotine, Stephen Tu

Proceedings of the Learning for Dynamics and Control Conference, 2023

A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations.

Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, Gaurav Aggarwal

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization.

Thomas Lew, Sumeet Singh, Mario Prats, Jeffrey Bingham, Jonathan Weisz, Benjie Holson, Xiaohan Zhang, Vikas Sindhwani, Yao Lu, Fei Xia, Peng Xu, Tingnan Zhang, Jie Tan, Montserrat Gonzalez

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Trajectory Optimization with Optimization-Based Dynamics.

Taylor A. Howell, Simon Le Cleac'h, Sumeet Singh, Peter R. Florence, Zachary Manchester, Vikas Sindhwani

IEEE Robotics Autom. Lett., 2022

Implicit Two-Tower Policies.

Yunfan Zhao, Qingkai Pan, Krzysztof Choromanski, Deepali Jain, Vikas Sindhwani

CoRR, 2022

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence

CoRR, 2022

Multiscale Sensor Fusion and Continuous Control with Neural CDEs.

Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Optimizing Trajectories with Closed-Loop Dynamic SQP.

Sumeet Singh, Jean-Jacques E. Slotine, Vikas Sindhwani

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Hybrid Random Features.

Krzysztof Marcin Choromanski, Han Lin, Haoxian Chen, Arijit Sehanobish, Yuanzhe Ma, Deepali Jain, Jake Varley, Andy Zeng, Michael S. Ryoo, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation.

Xuesu Xiao, Tingnan Zhang, Krzysztof Marcin Choromanski, Tsang-Wei Edward Lee, Anthony G. Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani

Proceedings of the Conference on Robot Learning, 2022

2021

Learning stabilizable nonlinear dynamics with contraction-based regularization.

Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

Int. J. Robotics Res., 2021

Safely Learning Dynamical Systems from Short Trajectories.

Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks.

Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles.

Bachir El Khadir, Jean-Bernard Lasserre, Vikas Sindhwani

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

An Ode to an ODE.

Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

CoRR, 2020

Time Dependence in Non-Autonomous Neural ODEs.

Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques E. Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani

CoRR, 2020

Ode to an ODE.

Krzysztof Marcin Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint.

Malayandi Palan, Shane T. Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen P. Boyd

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Robotic Table Tennis with Model-Free Reinforcement Learning.

Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Unsupervised Anomaly Detection for Self-flying Delivery Drones.

Vikas Sindhwani, Hakim Sidahmed, Krzysztof Choromanski, Brandon Jones

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Stochastic Flows and Geometric Optimization on the Orthogonal Group.

Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani

Proceedings of the 37th International Conference on Machine Learning, 2020

Transporter Networks: Rearranging the Visual World for Robotic Manipulation.

Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, Johnny Lee

Proceedings of the 4th Conference on Robot Learning, 2020

Learning Stability Certificates from Data.

Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Teleoperator Imitation with Continuous-time Safety.

Bachir El Khadir, Jake Varley, Vikas Sindhwani

CoRR, 2019

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies.

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani

CoRR, 2019

Teleoperator Imitation with Continuous-Time Safety.

Bachir El Khadir, Jacob Varley, Vikas Sindhwani

Proceedings of the Robotics: Science and Systems XV, 2019

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization.

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Data Efficient Reinforcement Learning for Legged Robots.

Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Provably Robust Blackbox Optimization for Reinforcement Learning.

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018

Learning Contracting Vector Fields For Stable Imitation Learning.

Vikas Sindhwani, Stephen Tu, Mohi Khansari

CoRR, 2018

Learning Stabilizable Dynamical Systems via Control Contraction Metrics.

Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

Learning-based Air Data System for Safe and Efficient Control of Fixed-wing Aerial Vehicles.

Krzysztof Choromanski, Vikas Sindhwani, Brandon Jones, Damien Jourdan, Maciej Chociej, Byron Boots

Proceedings of the 2018 IEEE International Symposium on Safety, 2018

Optimizing Simulations with Noise-Tolerant Structured Exploration.

Krzysztof Choromanski, Atil Iscen, Vikas Sindhwani, Jie Tan, Erwin Coumans

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization.

Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller

Proceedings of the 35th International Conference on Machine Learning, 2018

Policies Modulating Trajectory Generators.

Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

The Geometry of Random Features.

Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Recommender Systems.

Prem Melville, Vikas Sindhwani

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Hierarchically Compositional Kernels for Scalable Nonparametric Learning.

Jie Chen, Haim Avron, Vikas Sindhwani

J. Mach. Learn. Res., 2017

Manifold Regularization for Kernelized LSTD.

Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani

CoRR, 2017

Geometry of 3D Environments and Sum of Squares Polynomials.

Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani

Proceedings of the Robotics: Science and Systems XIII, 2017

On Blackbox Backpropagation and Jacobian Sensing.

Krzysztof Marcin Choromanski, Vikas Sindhwani

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Sequential operator splitting for constrained nonlinear optimal control.

Vikas Sindhwani, Rebecca Roelofs, Mrinal Kalakrishnan

Proceedings of the 2017 American Control Conference, 2017

2016

High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization.

Haim Avron, Vikas Sindhwani

Technometrics, 2016

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.

Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney

J. Mach. Learn. Res., 2016

Recycling Randomness with Structure for Sublinear time Kernel Expansions.

Krzysztof Choromanski, Vikas Sindhwani

Proceedings of the 33nd International Conference on Machine Learning, 2016

Learning compact recurrent neural networks.

Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015

Near-separable Non-negative Matrix Factorization with ℓ₁ and Bregman Loss Functions.

Abhishek Kumar, Vikas Sindhwani

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Structured Transforms for Small-Footprint Deep Learning.

Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Learning Machines Implemented on Non-Deterministic Hardware.

Suyog Gupta, Vikas Sindhwani, Kailash Gopalakrishnan

CoRR, 2014

Personalized classifiers: evolving a classifier from a large reference knowledge graph.

Ramakrishna B. Bairi, Ganesh Ramakrishnan, Vikas Sindhwani

Proceedings of the 18th International Database Engineering & Applications Symposium, 2014

Kernel methods match Deep Neural Networks on TIMIT.

Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran

Proceedings of the IEEE International Conference on Acoustics, 2014

Random Laplace Feature Maps for Semigroup Kernels on Histograms.

Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality.

Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Sketching Structured Matrices for Faster Nonlinear Regression.

Haim Avron, Vikas Sindhwani, David P. Woodruff

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization.

Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur

Proceedings of the 30th International Conference on Machine Learning, 2013

Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets.

Tara N. Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, Bhuvana Ramabhadran

Proceedings of the IEEE International Conference on Acoustics, 2013

2012

Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference

Vikas Sindhwani, Aurélie C. Lozano, Ha Quang Minh

CoRR, 2012

Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization.

Ankan Saha, Vikas Sindhwani

Proceedings of the Fifth International Conference on Web Search and Web Data Mining, 2012

Large-scale distributed non-negative sparse coding and sparse dictionary learning.

Vikas Sindhwani, Amol Ghoting

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization.

Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Extracting insights from social media with large-scale matrix approximations.

Vikas Sindhwani, Amol Ghoting, Edison Ting, Richard D. Lawrence

IBM J. Res. Dev., 2011

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels.

Vikas Sindhwani, Aurélie C. Lozano

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Concept Labeling: Building Text Classifiers with Minimal Supervision.

Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani, Richard D. Lawrence

Proceedings of the IJCAI 2011, 2011

Vector-valued Manifold Regularization.

Ha Quang Minh, Vikas Sindhwani

Proceedings of the 28th International Conference on Machine Learning, 2011

SystemML: Declarative machine learning on MapReduce.

Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan

Proceedings of the 27th International Conference on Data Engineering, 2011

Emerging topic detection using dictionary learning.

Shiva Prasad Kasiviswanathan, Prem Melville, Arindam Banerjee, Vikas Sindhwani

Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

2010

Recommender Systems.

Prem Melville, Vikas Sindhwani

Proceedings of the Encyclopedia of Machine Learning, 2010

Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations.

Tao Li, Vikas Sindhwani, Chris H. Q. Ding, Yi Zhang

Proceedings of the SIAM International Conference on Data Mining, 2010

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference.

Aurélie C. Lozano, Vikas Sindhwani

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

One-Class Matrix Completion with Low-Density Factorizations.

Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu, Aleksandra Mojsilovic

Proceedings of the ICDM 2010, 2010

2009

Multiview point cloud kernels for semisupervised learning [Lecture Notes].

David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, Partha Niyogi

IEEE Signal Process. Mag., 2009

Winning the KDD Cup Orange Challenge with Ensemble Selection.

Alexandru Niculescu-Mizil, Claudia Perlich, Grzegorz Swirszcz, Vikas Sindhwani, Yan Liu, Prem Melville, Dong Wang, Jing Xiao, Jianying Hu, Moninder Singh, Wei Xiong Shang, Yanfeng Zhu

Proceedings of KDD-Cup 2009 competition, Paris, France, June 28, 2009, 2009

Leveraging social networks for corporate staffing and expert recommendation.

Vijil Chenthamarakshan, Kuntal Dey, Jianying Hu, Aleksandra Mojsilovic, W. Riddle, Vikas Sindhwani

IBM J. Res. Dev., 2009

Knowledge transformation for cross-domain sentiment classification.

Tao Li, Vikas Sindhwani, Chris H. Q. Ding, Yi Zhang

Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009

Uncertainty sampling and transductive experimental design for active dual supervision.

Vikas Sindhwani, Prem Melville, Richard D. Lawrence

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework.

Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikonda

Proceedings of the ICDM Workshops 2009, 2009

A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge.

Tao Li, Yi Zhang, Vikas Sindhwani

Proceedings of the ACL 2009, 2009

2008

Optimization Techniques for Semi-Supervised Support Vector Machines.

Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi

J. Mach. Learn. Res., 2008

Regularized Co-Clustering with Dual Supervision.

Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

An RKHS for multi-view learning and manifold co-regularization.

Vikas Sindhwani, David S. Rosenberg

Proceedings of the Machine Learning, 2008

Document-Word Co-regularization for Semi-supervised Sentiment Analysis.

Vikas Sindhwani, Prem Melville

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2007

Semi-Supervised Gaussian Process Classifiers.

Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi

Proceedings of the IJCAI 2007, 2007

2006

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples.

Mikhail Belkin, Partha Niyogi, Vikas Sindhwani

J. Mach. Learn. Res., 2006

Large scale semi-supervised linear SVMs.

Vikas Sindhwani, S. Sathiya Keerthi

Proceedings of the SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models.

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Relational Learning with Gaussian Processes.

Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Branch and Bound for Semi-Supervised Support Vector Machines.

Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Deterministic annealing for semi-supervised kernel machines.

Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle

Proceedings of the Machine Learning, 2006

The Geometric Basis of Semi-Supervised Learning.

Vikas Sindhwani, Misha Belkin, Partha Niyogi

Proceedings of the Semi-Supervised Learning, 2006

2005

Beyond the point cloud: from transductive to semi-supervised learning.

Vikas Sindhwani, Partha Niyogi, Mikhail Belkin

Proceedings of the Machine Learning, 2005

On Manifold Regularization.

Misha Belkin, Partha Niyogi, Vikas Sindhwani

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Feature selection in MLPs and SVMs based on maximum output information.

Vikas Sindhwani, Subrata Rakshit, Dipti Deodhare, Deniz Erdogmus, José Carlos Príncipe, Partha Niyogi

IEEE Trans. Neural Networks, 2004

2001

Information Theoretic Feature Crediting in Multiclass Support Vector Machines.

Vikas Sindhwani, P. Bhattacharya, Subrata Rakshit

Proceedings of the First SIAM International Conference on Data Mining, 2001