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Journal Articles
- 2023
[j12]Simon Le Cleac'h
, Mac Schwager, Zachary Manchester, Vikas Sindhwani, Pete Florence, Sumeet Singh:
Single-Level Differentiable Contact Simulation. IEEE Robotics Autom. Lett. 8(7): 4012-4019 (2023)- 2022
- [j11]Taylor A. Howell, Simon Le Cleac'h, Sumeet Singh, Peter R. Florence, Zachary Manchester, Vikas Sindhwani:
Trajectory Optimization with Optimization-Based Dynamics. IEEE Robotics Autom. Lett. 7(3): 6750-6757 (2022) - 2021
- [j10]Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone:
Learning stabilizable nonlinear dynamics with contraction-based regularization. Int. J. Robotics Res. 40(10-11) (2021) - 2017
- [j9]Jie Chen, Haim Avron, Vikas Sindhwani:
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. J. Mach. Learn. Res. 18: 66:1-66:42 (2017) - 2016
- [j8]Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. 17: 120:1-120:38 (2016) - [j7]Haim Avron, Vikas Sindhwani:
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization. Technometrics 58(3): 341-349 (2016) - 2011
- [j6]Vikas Sindhwani, Amol Ghoting, Edison Ting, Richard D. Lawrence:
Extracting insights from social media with large-scale matrix approximations. IBM J. Res. Dev. 55(5): 9 (2011) - 2009
- [j5]Vijil Chenthamarakshan, Kuntal Dey, Jianying Hu, Aleksandra Mojsilovic, W. Riddle, Vikas Sindhwani:
Leveraging social networks for corporate staffing and expert recommendation. IBM J. Res. Dev. 53(6): 11 (2009) - [j4]David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, Partha Niyogi:
Multiview point cloud kernels for semisupervised learning [Lecture Notes]. IEEE Signal Process. Mag. 26(5): 145-150 (2009) - 2008
- [j3]Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
Optimization Techniques for Semi-Supervised Support Vector Machines. J. Mach. Learn. Res. 9: 203-233 (2008) - 2006
- [j2]Mikhail Belkin, Partha Niyogi, Vikas Sindhwani:
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. J. Mach. Learn. Res. 7: 2399-2434 (2006) - 2004
- [j1]Vikas Sindhwani, Subrata Rakshit, Dipti Deodhare, Deniz Erdogmus, José Carlos Príncipe, Partha Niyogi:
Feature selection in MLPs and SVMs based on maximum output information. IEEE Trans. Neural Networks 15(4): 937-948 (2004)
Conference and Workshop Papers
- 2024
- [c76]Montserrat Gonzalez Arenas, Ted Xiao, Sumeet Singh, Vidhi Jain, Allen Z. Ren, Quan Vuong, Jake Varley, Alexander Herzog, Isabel Leal, Sean Kirmani, Mario Prats, Dorsa Sadigh, Vikas Sindhwani, Kanishka Rao, Jacky Liang, Andy Zeng:
How to Prompt Your Robot: A PromptBook for Manipulation Skills with Code as Policies. ICRA 2024: 4340-4348 - [c75]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:
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention. ICRA 2024: 6920-6927 - 2023
- [c74]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:
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language. ICLR 2023 - [c73]Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, Gaurav Aggarwal:
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations. ICRA 2023: 5645-5652 - [c72]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:
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization. ICRA 2023: 7184-7190 - [c71]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:
Agile Catching with Whole-Body MPC and Blackbox Policy Learning. L4DC 2023: 851-863 - [c70]Deepali Jain, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan:
Mnemosyne: Learning to Train Transformers with Transformers. NeurIPS 2023 - [c69]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:
Robotic Table Tennis: A Case Study into a High Speed Learning System. Robotics: Science and Systems 2023 - [c68]Andy Zeng, Brian Ichter, Fei Xia, Ted Xiao, Vikas Sindhwani:
Demonstrating Large Language Models on Robots. Robotics: Science and Systems 2023 - 2022
- [c67]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:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRL 2022: 1708-1721 - [c66]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:
Hybrid Random Features. ICLR 2022 - [c65]Sumeet Singh, Jean-Jacques E. Slotine, Vikas Sindhwani:
Optimizing Trajectories with Closed-Loop Dynamic SQP. ICRA 2022: 5249-5254 - [c64]Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani:
Multiscale Sensor Fusion and Continuous Control with Neural CDEs. IROS 2022: 10897-10904 - 2021
- [c63]Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng:
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks. ICRA 2021: 4568-4575 - [c62]Bachir El Khadir, Jean-Bernard Lasserre, Vikas Sindhwani:
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles. ICRA 2021: 7802-7808 - [c61]Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems from Short Trajectories. L4DC 2021: 498-509 - 2020
- [c60]Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, Johnny Lee:
Transporter Networks: Rearranging the Visual World for Robotic Manipulation. CoRL 2020: 726-747 - [c59]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRL 2020: 1341-1350 - [c58]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:
Stochastic Flows and Geometric Optimization on the Orthogonal Group. ICML 2020: 1918-1928 - [c57]Vikas Sindhwani, Hakim Sidahmed, Krzysztof Choromanski, Brandon Jones:
Unsupervised Anomaly Detection for Self-flying Delivery Drones. ICRA 2020: 186-192 - [c56]Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly:
Robotic Table Tennis with Model-Free Reinforcement Learning. IROS 2020: 5556-5563 - [c55]Malayandi Palan, Shane T. Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen P. Boyd:
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint. L4DC 2020: 374-383 - [c54]Krzysztof Marcin Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani:
Ode to an ODE. NeurIPS 2020 - 2019
- [c53]Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani:
Data Efficient Reinforcement Learning for Legged Robots. CoRL 2019: 1-10 - [c52]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani:
Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL 2019: 683-696 - [c51]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani:
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS 2019: 10299-10309 - [c50]Bachir El Khadir, Jacob Varley, Vikas Sindhwani:
Teleoperator Imitation with Continuous-Time Safety. Robotics: Science and Systems 2019 - 2018
- [c49]Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
The Geometry of Random Features. AISTATS 2018: 1-9 - [c48]Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke:
Policies Modulating Trajectory Generators. CoRL 2018: 916-926 - [c47]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML 2018: 969-977 - [c46]Krzysztof Choromanski, Atil Iscen, Vikas Sindhwani, Jie Tan, Erwin Coumans:
Optimizing Simulations with Noise-Tolerant Structured Exploration. ICRA 2018: 2970-2977 - [c45]Krzysztof Choromanski, Vikas Sindhwani, Brandon Jones, Damien Jourdan, Maciej Chociej, Byron Boots:
Learning-based Air Data System for Safe and Efficient Control of Fixed-wing Aerial Vehicles. SSRR 2018: 1-8 - [c44]Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone:
Learning Stabilizable Dynamical Systems via Control Contraction Metrics. WAFR 2018: 179-195 - 2017
- [c43]Vikas Sindhwani, Rebecca Roelofs, Mrinal Kalakrishnan:
Sequential operator splitting for constrained nonlinear optimal control. ACC 2017: 4864-4871 - [c42]Krzysztof Marcin Choromanski, Vikas Sindhwani:
On Blackbox Backpropagation and Jacobian Sensing. NIPS 2017: 6521-6529 - [c41]Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani:
Geometry of 3D Environments and Sum of Squares Polynomials. Robotics: Science and Systems 2017 - 2016
- [c40]Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath:
Learning compact recurrent neural networks. ICASSP 2016: 5960-5964 - [c39]Krzysztof Choromanski, Vikas Sindhwani:
Recycling Randomness with Structure for Sublinear time Kernel Expansions. ICML 2016: 2502-2510 - 2015
- [c38]Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. NIPS 2015: 3088-3096 - [c37]Abhishek Kumar, Vikas Sindhwani:
Near-separable Non-negative Matrix Factorization with ℓ1 and Bregman Loss Functions. SDM 2015: 343-351 - 2014
- [c36]Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney:
Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR 2014: 971-978 - [c35]Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran:
Kernel methods match Deep Neural Networks on TIMIT. ICASSP 2014: 205-209 - [c34]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML 2014: 485-493 - [c33]Ramakrishna B. Bairi, Ganesh Ramakrishnan, Vikas Sindhwani:
Personalized classifiers: evolving a classifier from a large reference knowledge graph. IDEAS 2014: 132-141 - 2013
- [c32]Tara N. Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, Bhuvana Ramabhadran:
Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets. ICASSP 2013: 6655-6659 - [c31]Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur:
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization. ICML (1) 2013: 231-239 - [c30]Haim Avron, Vikas Sindhwani, David P. Woodruff:
Sketching Structured Matrices for Faster Nonlinear Regression. NIPS 2013: 2994-3002 - [c29]Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano:
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. UAI 2013 - 2012
- [c28]Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani:
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML 2012 - [c27]Vikas Sindhwani, Amol Ghoting:
Large-scale distributed non-negative sparse coding and sparse dictionary learning. KDD 2012: 489-497 - [c26]Ankan Saha, Vikas Sindhwani:
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization. WSDM 2012: 693-702 - 2011
- [c25]Shiva Prasad Kasiviswanathan, Prem Melville, Arindam Banerjee, Vikas Sindhwani:
Emerging topic detection using dictionary learning. CIKM 2011: 745-754 - [c24]Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan:
SystemML: Declarative machine learning on MapReduce. ICDE 2011: 231-242 - [c23]Ha Quang Minh, Vikas Sindhwani:
Vector-valued Manifold Regularization. ICML 2011: 57-64 - [c22]Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani, Richard D. Lawrence:
Concept Labeling: Building Text Classifiers with Minimal Supervision. IJCAI 2011: 1225-1230 - [c21]Vikas Sindhwani, Aurélie C. Lozano:
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. NIPS 2011: 2519-2527 - 2010
- [c20]Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu, Aleksandra Mojsilovic:
One-Class Matrix Completion with Low-Density Factorizations. ICDM 2010: 1055-1060 - [c19]Aurélie C. Lozano, Vikas Sindhwani:
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. NIPS 2010: 1486-1494 - [c18]Tao Li, Vikas Sindhwani, Chris H. Q. Ding, Yi Zhang:
Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations. SDM 2010: 293-302 - 2009
- [c17]Tao Li, Yi Zhang, Vikas Sindhwani:
A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge. ACL/IJCNLP 2009: 244-252 - [c16]Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikonda:
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework. ICDM Workshops 2009: 314-319 - [c15]Vikas Sindhwani, Prem Melville, Richard D. Lawrence:
Uncertainty sampling and transductive experimental design for active dual supervision. ICML 2009: 953-960 - [c14]Tao Li, Vikas Sindhwani, Chris H. Q. Ding, Yi Zhang:
Knowledge transformation for cross-domain sentiment classification. SIGIR 2009: 716-717 - [c13]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:
Winning the KDD Cup Orange Challenge with Ensemble Selection. KDD Cup 2009: 23-34 - 2008
- [c12]Vikas Sindhwani, Prem Melville:
Document-Word Co-regularization for Semi-supervised Sentiment Analysis. ICDM 2008: 1025-1030 - [c11]Vikas Sindhwani, David S. Rosenberg:
An RKHS for multi-view learning and manifold co-regularization. ICML 2008: 976-983 - [c10]Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic:
Regularized Co-Clustering with Dual Supervision. NIPS 2008: 1505-1512 - 2007
- [c9]Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi:
Semi-Supervised Gaussian Process Classifiers. IJCAI 2007: 1059-1064 - 2006
- [c8]Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle:
Deterministic annealing for semi-supervised kernel machines. ICML 2006: 841-848 - [c7]Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
Branch and Bound for Semi-Supervised Support Vector Machines. NIPS 2006: 217-224 - [c6]Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi:
Relational Learning with Gaussian Processes. NIPS 2006: 289-296 - [c5]S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle:
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. NIPS 2006: 673-680 - [c4]Vikas Sindhwani, S. Sathiya Keerthi:
Large scale semi-supervised linear SVMs. SIGIR 2006: 477-484 - 2005
- [c3]Misha Belkin, Partha Niyogi, Vikas Sindhwani:
On Manifold Regularization. AISTATS 2005: 17-24 - [c2]Vikas Sindhwani, Partha Niyogi, Mikhail Belkin:
Beyond the point cloud: from transductive to semi-supervised learning. ICML 2005: 824-831 - 2001
- [c1]Vikas Sindhwani, P. Bhattacharya, Subrata Rakshit:
Information Theoretic Feature Crediting in Multiclass Support Vector Machines. SDM 2001: 1-18
Parts in Books or Collections
- 2006
- [p1]Vikas Sindhwani, Misha Belkin, Partha Niyogi:
The Geometric Basis of Semi-Supervised Learning. Semi-Supervised Learning 2006: 217-235
Reference Works
- 2017
- [r2]Prem Melville, Vikas Sindhwani:
Recommender Systems. Encyclopedia of Machine Learning and Data Mining 2017: 1056-1066 - 2010
- [r1]Prem Melville, Vikas Sindhwani:
Recommender Systems. Encyclopedia of Machine Learning 2010: 829-838
Informal and Other Publications
- 2024
- [i52]Jake Varley, Sumeet Singh, Deepali Jain, Krzysztof Choromanski, Andy Zeng, Somnath Basu Roy Chowdhury, Avinava Dubey, Vikas Sindhwani:
Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity. CoRR abs/2404.03570 (2024) - [i51]Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi:
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning. CoRR abs/2406.17740 (2024) - [i50]William F. Whitney, Jacob Varley, Deepali Jain, Krzysztof Choromanski, Sumeet Singh, Vikas Sindhwani:
Modeling the Real World with High-Density Visual Particle Dynamics. CoRR abs/2406.19800 (2024) - [i49]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:
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs. CoRR abs/2407.07775 (2024) - [i48]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:
Achieving Human Level Competitive Robot Table Tennis. CoRR abs/2408.03906 (2024) - 2023
- [i47]Deepali Jain, Krzysztof Marcin Choromanski, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan, Avinava Dubey:
Mnemosyne: Learning to Train Transformers with Transformers. CoRR abs/2302.01128 (2023) - [i46]Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems. CoRR abs/2305.12284 (2023) - [i45]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, José 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:
Barkour: Benchmarking Animal-level Agility with Quadruped Robots. CoRR abs/2305.14654 (2023) - [i44]Saminda Abeyruwan, Alex Bewley, Nicholas M. Boffi, Krzysztof Choromanski, David B. D'Ambrosio, Deepali Jain, Pannag Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques E. Slotine, Stephen Tu:
Agile Catching with Whole-Body MPC and Blackbox Policy Learning. CoRR abs/2306.08205 (2023) - [i43]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:
Robotic Table Tennis: A Case Study into a High Speed Learning System. CoRR abs/2309.03315 (2023) - [i42]Sumeet Singh, Stephen Tu, Vikas Sindhwani:
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models. CoRR abs/2309.05803 (2023) - [i41]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:
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention. CoRR abs/2312.01990 (2023) - 2022
- [i40]Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani:
Multiscale Sensor Fusion and Continuous Control with Neural CDEs. CoRR abs/2203.08715 (2022) - [i39]Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence:
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language. CoRR abs/2204.00598 (2022) - [i38]Yunfan Zhao, Qingkai Pan, Krzysztof Choromanski, Deepali Jain, Vikas Sindhwani:
Implicit Two-Tower Policies. CoRR abs/2208.01191 (2022) - [i37]Xuesu Xiao, Tingnan Zhang, Krzysztof 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:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRR abs/2209.10780 (2022) - [i36]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:
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization. CoRR abs/2210.10865 (2022) - [i35]Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, Gaurav Aggarwal:
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations. CoRR abs/2211.16309 (2022) - [i34]Simon Le Cleac'h, Mac Schwager, Zachary Manchester, Vikas Sindhwani, Pete Florence, Sumeet Singh:
Single-Level Differentiable Contact Simulation. CoRR abs/2212.06764 (2022) - 2021
- [i33]Taylor A. Howell, Simon Le Cleac'h, Sumeet Singh, Pete Florence, Zachary Manchester, Vikas Sindhwani:
Trajectory Optimization with Optimization-Based Dynamics. CoRR abs/2109.04928 (2021) - [i32]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:
Hybrid Random Features. CoRR abs/2110.04367 (2021) - 2020
- [i31]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:
Stochastic Flows and Geometric Optimization on the Orthogonal Group. CoRR abs/2003.13563 (2020) - [i30]Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly:
Robotic Table Tennis with Model-Free Reinforcement Learning. CoRR abs/2003.14398 (2020) - [i29]Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques E. Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani:
Time Dependence in Non-Autonomous Neural ODEs. CoRR abs/2005.01906 (2020) - [i28]Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani:
An Ode to an ODE. CoRR abs/2006.11421 (2020) - [i27]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRR abs/2008.05952 (2020) - [i26]Bachir El Khadir, Jean-Bernard Lasserre, Vikas Sindhwani:
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles. CoRR abs/2010.08167 (2020) - [i25]Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, Johnny Lee:
Transporter Networks: Rearranging the Visual World for Robotic Manipulation. CoRR abs/2010.14406 (2020) - [i24]Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems from Short Trajectories. CoRR abs/2011.12257 (2020) - [i23]Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng:
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks. CoRR abs/2012.03385 (2020) - 2019
- [i22]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani:
When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. CoRR abs/1903.02993 (2019) - [i21]Bachir El Khadir, Jake Varley, Vikas Sindhwani:
Teleoperator Imitation with Continuous-time Safety. CoRR abs/1905.09499 (2019) - [i20]Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani:
Data Efficient Reinforcement Learning for Legged Robots. CoRR abs/1907.03613 (2019) - [i19]Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone:
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization. CoRR abs/1907.13122 (2019) - [i18]Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke:
Policies Modulating Trajectory Generators. CoRR abs/1910.02812 (2019) - 2018
- [i17]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR abs/1804.02395 (2018) - [i16]Vikas Sindhwani, Stephen Tu, Mohi Khansari:
Learning Contracting Vector Fields For Stable Imitation Learning. CoRR abs/1804.04878 (2018) - [i15]Krzysztof Choromanski, Atil Iscen, Vikas Sindhwani, Jie Tan, Erwin Coumans:
Optimizing Simulations with Noise-Tolerant Structured Exploration. CoRR abs/1805.07831 (2018) - [i14]Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone:
Learning Stabilizable Dynamical Systems via Control Contraction Metrics. CoRR abs/1808.00113 (2018) - 2017
- [i13]Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani:
Manifold Regularization for Kernelized LSTD. CoRR abs/1710.05387 (2017) - 2016
- [i12]Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath:
Learning Compact Recurrent Neural Networks. CoRR abs/1604.02594 (2016) - [i11]Krzysztof Choromanski, Vikas Sindhwani:
Recycling Randomness with Structure for Sublinear time Kernel Expansions. CoRR abs/1605.09049 (2016) - [i10]Jie Chen, Haim Avron, Vikas Sindhwani:
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. CoRR abs/1608.00860 (2016) - [i9]Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani:
Geometry of 3D Environments and Sum of Squares Polynomials. CoRR abs/1611.07369 (2016) - 2015
- [i8]Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. CoRR abs/1510.01722 (2015) - 2014
- [i7]Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano:
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. CoRR abs/1408.2066 (2014) - [i6]Vikas Sindhwani, Haim Avron:
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization. CoRR abs/1409.0940 (2014) - [i5]Suyog Gupta, Vikas Sindhwani, Kailash Gopalakrishnan:
Learning Machines Implemented on Non-Deterministic Hardware. CoRR abs/1409.2620 (2014) - [i4]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR abs/1412.8293 (2014) - 2013
- [i3]Abhishek Kumar, Vikas Sindhwani:
Near-separable Non-negative Matrix Factorization with ℓ1- and Bregman Loss Functions. CoRR abs/1312.7167 (2013) - 2012
- [i2]Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur:
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization. CoRR abs/1210.1190 (2012) - [i1]Vikas Sindhwani, Aurélie C. Lozano, Ha Quang Minh:
Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference. CoRR abs/1210.4792 (2012)
Coauthor Index
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