Gemini Robotics 2: Safety
Gemini Robotics Team, 2026
Publications
“We can only see a short distance ahead, but we can see plenty there that needs to be done”
“Simplicity is the final achievement”
“To play a wrong note is insignificant; to play without passion is inexcusable”
“Coauthorship is a closer relationship than friendship”
“The search is more important than the destination”
“You are never too far behind to win and never too far ahead to lose”
“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”
2026
2025
Evaluating Gemini Robotics Policies in a Veo World Simulator
with Gemini Robotics Team
Can AI Perceive Physical Danger and Intervene?
Abhishek Jindal, Dmitry Kalashnikov, Oscar Chang, Divya Garikapati, Anirudha Majumdar, Pierre Sermanet, Vikas Sindhwani
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, September 2025
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
with Gemini Team, 2025
Gemini Robotics: Bringing AI into the Physical World
with Gemini Robotics Team, 2025
Generating Robot Constitutions & Benchmarks for Semantic Safety
Pierre Sermanet, Anirudha Majumdar, Alex Irpan, Dmitry Kalashnikov, Vikas Sindhwani
Conference on Robot Learning (CORL), 2025
Predictive Red Teaming: Breaking Policies Without Breaking Robots
Anirudha Majumdar, Mohit Sharma, Dmitry Kalashnikov, Sumeet Singh, Pierre Sermanet, Vikas Sindhwani
Conference on Robot Learning (CORL), 2025
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
Connor Schenck, Isaac Reid, Mithun George Jacob, Alex Bewley, Joshua Ainslie, David Rendleman, Deepali Jain, Mohit Sharma, Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, Krzysztof Choromanski
International Conference on Machine Learning (ICML), 2025
SciFi-Benchmark: How Would AI-Powered Robots Behave in Science Fiction Literature?
Pierre Sermanet, Anirudha Majumdar and Vikas Sindhwani
scifi-benchmark.github.io, 2025
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
2024 ICRA Best Paper Award on Robot Learning, Finalist!Project Website
2024
Safely Learning Dynamical Systems
Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
Foundations of Computational Mathematics, 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
Best RoboCup Paper Award! Best Safety Paper Nomination!
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
Neural Information Processing Systems (NeurIPS), 2024
Barkour: Benchmarking animal-level agility with quadruped robots
with Caluwaerts et al
International Conference on Intelligent Robots and Systems (IROS), 2024
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
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 Outstanding 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
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
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
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
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
H. Avron and V. Sindhwani
Technometrics (Statistical Analysis of Big Data), to appear, 2015
Near-separable Non-negative Matrix Factorization with L1 and Bregman Loss Functions
A. Kumar, V. Sindhwani
SIAM International Conference on Data Mining (SDM), 2015
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
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
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
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), February 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
An RKHS for Multi-view Learning and Manifold Co-Regularization
V. Sindhwani and D. Rosenberg
International Conference on Machine Learning (ICML), 2008
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
Deterministic Annealing for Semi-supervised Kernel Machines
V. Sindhwani, S.S. Keerthi, O. Chapelle
International Conference on Machine Learning (ICML), 2006
Large Scale Semi-supervised Linear SVMs
V. Sindhwani, S.S. Keerthi
29th Annual International ACM SIGIR, 2006
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
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
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, Department of Computer Science, University of Chicago, 2004
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, Engineering Physics, Indian Institute of Technology, Bombay, 2001
Information Theoretic Feature Crediting in Multiclass Support Vector Machines
V. Sindhwani, P. Bhattacharya, S. Rakshit
First SIAM International Conference on Data Mining (SDM), 2001