Vikas Sindhwani

• “We can only see a short distance ahead, but we can see plenty there that needs to be done” – Alan Turing, 1950
  

• “Simplicity is the final achievement” – Frederic Chopin
  

• “To play a wrong note is insignificant; to play without passion is inexcusable” – Beethoven
  

• “Coauthorship is a closer relationship than friendship” – Fan Chung
  

• “The search is more important than the destination” – A.R. Rahman
  

• “You are never too far behind to win and never too far ahead to lose” – seen at Arlen Specter Squash Center
  

• I have no doubt I'll be forgotten, and that's good. … I had the privilege of playing for you all, now let's move on" – George Winston
  

2024

• Achieving Human Level Competitive Robot Table Tennis
  David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser, Atil Iscen, Heni Ben Amor, Alex Bewley, Barney J. Reed, Krista Reymann, Leila Takayama, Yuval Tassa, Krzysztof Choromanski, Erwin Coumans, Deepali Jain, Navdeep Jaitly, Natasha Jaques, Satoshi Kataoka, Yuheng Kuang, Nevena Lazic, Reza Mahjourian, Sherry Moore, Kenneth Oslund, Anish Shankar, Vikas Sindhwani, Vincent Vanhoucke, Grace Vesom, Peng Xu, Pannag R. Sanketi
  Project Website, 2024

• Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
  Sumeet Singh, Stephen Tu, Vikas Sindhwani
  Transactions on Machine Learning Research (TMLR), 2024

• Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
  Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee, Wenhao Yu, Connor Schenck, David Rendleman, Dhruv Shah, Fei Xia, Jasmine Hsu, Jonathan Hoech, Pete Florence, Sean Kirmani, Sumeet Singh, Vikas Sindhwani, Carolina Parada, Chelsea Finn, Peng Xu, Sergey Levine, Jie Tan
  Conference on Robot Learning (CORL), 2024

• Modeling the Real World with High-Density Visual Particle Dynamics
  William F. Whitney, Jacob Varley, Deepali Jain, Krzysztof Choromanski, Sumeet Singh, Vikas Sindhwani
  Conference on Robot Learning (CORL), 2024

• Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
  Jake Varley, Sumeet Singh, Deepali Jain, Krzysztof Choromanski, Andy Zeng, Somnath B. R. Chowdhury, Avinana Dubey, Vikas Sindhwani
  International Conference on Intelligent Robots and Systems (IROS), 2024

• SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
  with I. Leal, K. Choromanski, D. Jain et. al.
  IEEE International Conference on Robotics and Automation (ICRA), 2024 Best Manipulation Paper Award!

• How To Prompt Your Robot: A PromptBook for Manipulation Skills with Code as Policies
  with M.G. Arenas et. al.
  IEEE International Conference on Robotics and Automation (ICRA), 2024

• Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
  Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi
  arXiv:2406.17740, 2024

• Barkour: Benchmarking animal-level agility with quadruped robots
  with Caluwaerts et al
  International Conference on Intelligent Robots and Systems (IROS), 2024
  Blog Post with Links

2023

• Safely Learning Dynamical Systems
  Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
  Submitted, 2023

• Agile Catching with Whole-Body MPC and Blackbox Policy Learning
  with Abeyruwan et al
  Learning for Dynamics and Control (L4DC), 2023

• Robotic Table Tennis: A Case Study into a High Speed Learning System
  with D'Ambrosio et. al
  Robotics Science and Systems (RSS) 2023

• Mnemosyne: Learning to Train Transformers with Transformers
  Deepali Jain, Krzysztof Marcin Choromanski, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan, Avinava Dube
  Neural Information Processing Systems (NeurIPS), 2023

• Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
  Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence
  International Conference on Representation Learning (ICLR), 2023

• Single-Level Differentiable Contact Simulation
  Simon Le Cleac'h, Mac Schwager, Zachary Manchester, Vikas Sindhwani, Pete Florence, Sumeet Singh
  IEEE International Conference on Intelligent Robots and Systems (IROS 2023)

2022

• Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
  Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Edward Lee, Anthony 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
  Conference on Robot Learning (CoRL), 2022

• 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
  submitted, 2022
  Video

• Agile Catching with Whole Body MPC and Blackbox Policy Learning
  Saminda Abeyruwan, Alex Bewley, Nicholas M. Boffi, Krzysztof Choromanski, David D'Ambrosio, Deepali Jain, Pannag Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques Slotine, and Stephen Tu
  Learning for Agile Robotics (CoRL), 2022
  

• Multiscale Sensor Fusion and Continuous Control with Neural CDEs
  Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani
  International Conference on Intelligent Robots and Systems (IROS), 2022

• Optimizing Trajectories with Closed-loop Dynamic SQP
  Sumeet Singh, Jean-Jacques Slotine, Vikas Sindhwani
  International Conference on Robotics and Automation (ICRA) 2022 Finalist for Oustanding Planning Paper Award!
  

• Trajectory Optimization with Optimization-based Dynamics
  Taylor A. Howell, Simon Le Cleac'h, Sumeet Singh, Pete Florence, Zachary Manchester, Vikas Sindhwani
  IEEE Robotics and Automation Letters 2022 (to appear)

• Hybrid Random Features
  Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
  International Conference on Learning Representations (ICLR) 2022 (to appear)

• 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
  5th Robot Learning Workshop: Trustworthy Robotics at NeurIPS 2022

2021

• Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles
  Bachir El Khadir, Jean Bernard Lasserre, Vikas Sindhwani
  International Conference on Robotics and Automation (ICRA), 2021 Video

• 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
  International Conference on Robotics and Automation (ICRA), 2021

• Safely Learning Dynamical Systems from Short Trajectories
  Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
  Learning for Dynamics and Control (L4DC), 2020

2020

• Rearranging the Visual World: Transporter Networks
  Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, Johnny Lee
  Conference on Robot Learning (CORL), 2020
  Best Paper Presentation Award Finalist!
  

• Learning Stability Certificates from Data
  Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
  Conference on Robot Learning (CORL), 2020

• Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
  Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-jacques Slotine, Marco Pavone
  International Journal of Robotics Research (to appear, IJRR), 2020

• Unsupervised Anomaly Detection for Self-flying Delivery Drones
  Vikas Sindhwani, Hakim Sidahmed, Krzysztof Choromanski, Brandon Jones
  International Conference on Robotics and Automation (ICRA), 2020

• Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint
  M. Palan, S. Barratt, A. McCauley, D. Singh, V. Sindhwani, S.Boyd
  Learning for Dynamics and Control (L4DC), 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
  IEEE International Conference on Intelligent Robots and Systems (IROS 2020), 2020

• Stochastic Flows and Geometric Optimization on the Orthogonal Group
  International Conference on Machine Learning (ICML), 2020

• Time Dependence in Non-Autonomous Neural ODEs
  Submitted, 2020

• An Ode to an ODE
  Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani
  Neural Information Processing Systems (NeurIPS), 2020

2019

• Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
  Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques Slotine, Marco Pavone
  Invited Paper, Submitted, 2018
  Video

• Teleoperator Imitation with Continuous-time Safety
  Bachir El Khadir, Jake Varley, Vikas Sindhwani
  Robotics: Science and Systems (RSS), 2019

• Data Efficient Reinforcement Learning for Legged Robots
  Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani
  Conference on Robot Learning (CoRL), 2019

• Provably Robust Blackbox Optimization for Reinforcement Learning
  K. Choromanski, A. Pacchiano, J. Parker-Holder, Y. Tang, D. Jain, Y. Yang, A. Iscen, J. Hsu, V. Sindhwani
  Conference on Robot Learning (CoRL), 2019

2018

• Learning Stabilizable Dynamical System via Control Contraction Metrics
  Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone
  Workshop on Algorithmic Foundations of Robotics (WAFR), 2018

• Learning Contracting Vector Fields for Stable Imitation Learning
  Vikas Sindhwani, Stephen Tu and Mohi Khansari
  

• Policies Modulating Trajectory Generators
  Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke
  Conference on Robot Learning (CoRL), 2018

• Structured Evolution with Compact Architectures for Scalable Policy Optimization
  Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
  IEEE International Conference on Machine Learning (ICML), 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
  IEEE International Symposium On Safety, Security, And Rescue Robotics (SSRR), 2018
  

• Optimizing Simulations with Noise-Tolerant Structured Exploration
  Krzysztof Choromanski, Erwin Coumans, Atil Iscen, Vikas Sindhwani, Jie Tan
  IEEE International Conference on Robotics and Automation (ICRA), 2018
  

• The Geometry of Random Features
  Krzysztof Choromanski, Mark Rowland, Tamas Sarlos, Vikas Sindhwani, Richard Turner, Adrian Weller
  International Conference on Artificial Intelligence and Statistics (AISTATS’18)
  

2017

• On Blackbox Backpropagation and Jacobian Sensing
  Krzysztof Choromanski and Vikas Sindhwani
  Neural Information Processing Systems (NIPS), 2017
  

• Sequential Operator Splitting for Constrained Nonlinear Optimal Control
  Vikas Sindhwani, Rebecca Roelofs, Mrinal Kalakrishnan
  American Control Conference (ACC), 2017
  

• Manifold Regularization for Kernelized LSTD
  Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani
  Conference on Robot Learning (CoRL), 2017
  

• Geometry of 3D Environments and Sum of Squares Polynomials
  Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani
  Robotics: Science and Systems (RSS), 2017 (to appear)
  

• Hierarchically Compositional Kernels for Scalable Nonparametric Learning
  J. Chen, H. Avron and V. Sindhwani
  Journal of Machine Learning Research (JMLR), 2017 (to appear)
  

2016

• Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
  Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael Mahoney
  Journal of Machine Learning Research, 2016
  

• Recycling Randomness with Structure for Sublinear time Kernel Expansions
  K. Choromanski and V. Sindhwani
  International Conference on Machine Learning (ICML), to appear, 2016
  

• Learning Compact Recurrent Neural Networks
  Z. Lu, V. Sindhwani, T. Sainath
  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016

2015

• Structured Transforms for Small-footprint Deep Learning
  V. Sindhwani, T. Sainath, S. Kumar
  Neural Information Processing Systems (NIPS), 2015 Spotlight Paper!
  
  supplementary video

• High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
  H. Avron and V. Sindhwani
  Technometrics (Statistical Analysis of Big Data), to appear, 2015
  arxiv

• Near-separable Non-negative Matrix Factorization with L1 and Bregman Loss Functions
  A. Kumar, V. Sindhwani
  SIAM International Conference on Data Mining (SDM), 2015 (Extension of ICML 2013 paper)
  

• libSkylark: A Framework for High-Performance Matrix Sketching for Statistical Computing
  G. Kollias, Y. Ineichen, H. Avron, V. Sindhwani, K. Clarkson, C. Bekas, A. Curion
  Supercomputing (SC) 2015 (Poster)
  

2014

• Random Laplace Feature Maps for Semigroup Kernels on Histograms
  J. Yang, V. Sindhwani, Q. Fan, H. Avron, M. Mahoney
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
  

• Kernel Methods match Deep Neural Networks on TIMIT
  Po-Sen Huang, Haim Avron, Tara Sainath, Vikas Sindhwani, Bhuvana Ramabhadran
  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014 (Best Student Paper Award!)
  

• Quasi-Monte Carlo Feature Maps for Shift Invariant Kernels
  J. Yang, V. Sindhwani, H. Avron, M. Mahoney
  International Conference in Machine Learning (ICML), 2014
  Supplementary Material

• Learning Machines Implemented on Non-Deterministic Hardware
  S. Gupta, V. Sindhwani, K. Gopalakrishnan, arXiv Preprint, 2014
  

2013

• Sketching Structured Matrices for Faster Nonlinear Regression
  H. Avron, V. Sindhwani, D. Woodruff
  Neural Information Processing Systems (NIPS), 2013

• Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
  V.Sindhwani, H.Q. Minh and A.C. Lozano
  Uncertainty in Artificial Intelligence (UAI), 2013 Best Paper Award! IBM Pat Goldberg Memorial Award!
  Arxiv version: arXiv:1210.4792v2
  Talk pdf

• Low-Rank Matrix Factorization for Deep Neural Network Training with High-dimensional Output Targets
  T.N. Sainath, B. Kingsbury, V. Sindhwani, E. Arisoy, B. Ramabhadran
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013
  

• Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
  A. Kumar, V. Sindhwani, P. Kambadur
  International Conference in Machine Learning (ICML), 2013
  Arxiv version: arXiv:1210.1190v1, October 2012

2012

• Large-scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning
  V. Sindhwani and A. Ghoting
  ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2012
  

• Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
  H. Avron, S. Kale, S. Kasiviswanathan, V. Sindhwani
  International Conference on Machine Learning (ICML), 2012

• Learning Evolving and Emerging Topics in Social Media: A Dynamic NMF Approach with Temporal Regularization
  A. Saha and V. Sindhwani
  Fifth ACM International Conference on Web Search and Data Mining (WSDM), 2012
  

2011

• Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
  V. Sindhwani and A.C. Lozano
  Neural Information Processing Systems (NIPS), 2011
  

• Vector-Valued Manifold Regularization
  H.Q. Minh and V. Sindhwani
  International Conference on Machine Learning (ICML), 2011
  

• SystemML: Declarative Machine Learning on MapReduce
  A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan
  IEEE International Conference on Data Engineering (ICDE), 2011

• Emerging Topic Detection Using Dictionary Learning
  S. Kasiviswanathan, P. Melville, A. Banerjee, and V. Sindhwani
  ACM Conference on Information and Knowledge Management (CIKM), 2011

• Concept Labeling: Building Text Classifiers with Minimal Supervision
  V. Chenthamarakshan, P. Melville, V. Sindhwani and R. D. Lawrence
  International Joint Conference on Artificial Intelligence (IJCAI), 2011

2010

• Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference
  A.C. Lozano and V. Sindhwani
  Neural Information Processing Systems (NIPS), 2010

• Rank Selection in Low-Rank Matrix Approximations
  B. Kanagal, V. Sindhwani
  Low-Rank Methods for Large-scale Machine Learning (NIPS Workshop), 2010

• One-class Matrix Completion with Low-Density Factorizations
  V. Sindhwani, S.S. Bucak, J. Hu, A. Mojsilovic
  IEEE International Conference on Data Mining (ICDM), 2010

• Bridging Domains with Words: Opinion Analysis with Matrix Trifactorizations
  T. Li, V. Sindhwani, C.Ding, Y.Zhang
  SIAM International Conference on Data Mining (SDM), 2010

• Recommender Systems
  P. Melville, V. Sindhwani
  Encyclopedia of Machine Learning, Springer, 2010.

• Social Media Analytics
  R. Lawrence, P. Melville, C. Perlich, V.Sindhwani, E.Meliksetian, P.Hsueh, Y. Liu
  Operations Research - Management Science Today (ORMS-Today), Feburary 2010

2009

• Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision
  V. Sindhwani, P. Melville, R. Lawrence
  International Conference on Machine Learning (ICML), 2009

• Sparse Least Squares Methods in the Parallel Machine Learning (PML) Framework
  R. Natarajan, V.Sindhwani, S. Tatikonda
  Large Scale Data Mining: Theory and Applications Workshop IEEE International Conference on Data Mining (ICDM), 2009

• A Kernel for Semi-supervised Learning with Multi-view Point Cloud Regularization
  D. Rosenberg, V. Sindhwani, P. Bartlett, P. Niyogi
  IEEE Signal Processing Magazine, 2009

• A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge
  T. Li, Y. Zhang and V. Sindhwani
  Association for Computational Linguistics (ACL), 2009

• Winning the KDD Cup Orange Challenge with Ensemble Selection
  A. Niculescu-Mizil, C. Perlich, G. Swirszcz, V. Sindhwani, Y. Liu, P. Melville, D. Wang, J. Xiao, J. Hu, M. Singh, W. X. Shang, Y.F. Zhu
  Journal of Machine Learning Research (JMLR proceeding series), 2009 (KDD Cup Winner!)
  

• Active Dual Supervision: Reducing the Cost of Annotating Examples and Features
  P. Melville and V. Sindhwani
  NAACL HLT 2009 Workshop on Active Learning for NLP, 2009

• Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria
  P. Hsueh, P. Melville, V. Sindhwani
  NAACL HLT 2009 Workshop on Active Learning for NLP, 2009

• Leveraging Social Networks for Corporate Staffing and Expert Recommendation
  V. Chenthamarakshan, K. Dey, J. Hu, A. Mojsilovic, W. Riddle, V. Sindhwani
  IBM Journal of Research and Development, 2009

2008

• Regularized Co-Clustering With Dual Supervision
  V. Sindhwani, J. Hu, A. Mojsilovic
  Neural Information Processing Systems (NIPS), 2008

• Optimization Techniques for Semi-supervised Support Vector Machines
  O. Chapelle, V. Sindhwani, S. S. Keerthi
  Journal of Machine Learning Research 9(Feb):203–233, 2008

• Document-Word Co-Regularization for Semi-supervised Sentiment Analysis
  V. Sindhwani, P. Melville
  IEEE International Conference on Data Mining (ICDM), 2008
  datasets

• An RKHS for Multi-view Learning and Manifold Co-Regularization
  V. Sindhwani and D. Rosenberg
  International Conference on Machine Learning (ICML), 2008
  video

2007

• On Semi-supervised Kernel Methods
  V. Sindhwani
  Ph.D. Thesis
  Department of Computer Science, University of Chicago, 2007

• Newton Methods for Fast Solution of Semi-supervised Linear SVMs
  V. Sindhwani, S.S. Keerthi
  Large Scale Kernel Machines MIT Press (Book Chapter), 2007

• Semi-supervised Gaussian Processes
  V. Sindhwani, W. Chu, S. S. Keerthi
  International Joint Conference on Artificial Intelligence (IJCAI), 2007

2006

• An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
  S. S. Keerthi, V. Sindhwani, O. Chapelle
  Neural Information Processing Systems (NIPS), 2006

• Relational Learning with Gaussian Processes
  W. Chu, V. Sindhwani, Z. Ghahramani, S. S. Keerthi
  Neural Information Processing Systems (NIPS), 2006

• Branch and Bound for Semi-supervised Support Vector Machines
  O. Chapelle, V. Sindhwani, S. S. Keerthi
  Neural Information Processing Systems (NIPS), 2006
  Matlab Code

• Deterministic Annealing for Semi-supervised Kernel Machines
  V. Sindhwani, S.S. Keerthi, O. Chapelle
  International Conference on Machine Learning (ICML), 2006
  talk

• Large Scale Semi-supervised Linear SVMs
  V. Sindhwani, S.S. Keerthi
  29th Annual International ACM SIGIR, 2006
  talk techreport

• Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
  M. Belkin, P. Niyogi, V. Sindhwani
  Journal of Machine Learning Research (JMLR), 2006
  talk

• The Geometric Basis of Semi-supervised Learning
  V. Sindhwani, M. Belkin, P. Niyogi
  Semi-supervised Learning MIT Press, 2006

• SVMlin: Fast Linear SVM Solvers for Supervised and Semi-supervised Learning
  V. Sindhwani, P. Niyogi, M. Belkin
  Workshop on Machine Learning Open Source Software, Neural Information Processing Systems (NIPS), 2006

2005

• Beyond the Point Cloud: from Transductive to Semi-supervised Learning
  V. Sindhwani, P. Niyogi, M. Belkin
  International Conference on Machine Learning (ICML), 2005
  code talk

• A Co-regularization Approach to Semi-supervised Learning with Multiple Views
  V. Sindhwani, P. Niyogi, M. Belkin
  Workshop on Learning with Multiple Views, International Conference on Machine Learning (ICML), 2005

• On Manifold Regularization
  M. Belkin, P. Niyogi, V. Sindhwani
  Artificial Intelligence and Statistics (AISTATS), 2005

• Linear Manifold Regularization for Large Scale Semi-supervised Learning
  V. Sindhwani, P. Niyogi, M. Belkin
  Workshop on Learning with Partially Classified Training Data, International Conference on Machine Learning (ICML), 2005

2004

• Kernel Machines for Semi-supervised Learning
  V. Sindhwani
  Masters Thesis, 2004
  Department of Computer Science, University of Chicago
  

• Feature Selection in MLPs and SVMs Based On Maximum Output Information
  V. Sindhwani, S. Rakshit, D. Deodhare, D. Erdogmus, J.Principe, P.Niyogi
  IEEE Trans. on Neural Networks,V.15,N.4, 2004

Earlier

• Information Theoretic Performance Evaluation and Feature Selection in Machine Learning
  V. Sindhwani
  Bachelors Thesis, 2001
  Engineering Physics, Indian Institute of Technology, Bombay

• Information Theoretic Feature Crediting in Multiclass Support Vector Machines
  V. Sindhwani, P. Bhattacharya, S. Rakshit
  First Siam International Conference on Data Mining (SDM), 2001