Sindhwani, Vikas

55 publications

ICLRW 2025 Achieving Human Level Competitive Robot Table Tennis David B D'Ambrosio, Saminda Wishwajith Abeyruwan, Laura Graesser, Atil Iscen, Heni Ben Amor, Alex Bewley, Barney Reed, Krista Reymann, Leila Takayama, Yuval Tassa, Krzysztof Marcin 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

CoRL 2025 Generating Robot Constitutions & Benchmarks for Semantic Safety Pierre Sermanet, Anirudha Majumdar, Alex Irpan, Dmitry Kalashnikov, Vikas Sindhwani

ICML 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, Kumar Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey A. Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, Krzysztof Marcin Choromanski

ICLRW 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, Kumar Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey A. Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, Krzysztof Marcin Choromanski

CoRL 2025 Predictive Red Teaming: Breaking Policies Without Breaking Robots Anirudha Majumdar, Mohit Sharma, Dmitry Kalashnikov, Sumeet Singh, Pierre Sermanet, Vikas Sindhwani

ICLRW 2024 Implicit Two-Tower Policies Yunfan Zhao, Alvin Pan, Krzysztof Marcin Choromanski, Deepali Jain, Vikas Sindhwani

CoRL 2024 Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs Zhuo Xu, Hao-Tien Lewis Chiang, 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

CoRL 2024 Modeling the Real World with High-Density Visual Particle Dynamics William F Whitney, Jake Varley, Deepali Jain, Krzysztof Marcin Choromanski, Sumeet Singh, Vikas Sindhwani

TMLR 2024 Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models Sumeet Singh, Stephen Tu, Vikas Sindhwani

NeurIPS 2024 Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi

L4DC 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 Slotine, Stephen Tu

NeurIPS 2023 Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain, Krzysztof M Choromanski, Kumar Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan

ICLR 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

ICLR 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

CoRL 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 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

L4DC 2021 Safely Learning Dynamical Systems from Short Trajectories Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

L4DC 2020 Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint Malayandi Palan, Shane Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen Boyd

CoRL 2020 Learning Stability Certificates from Data Nicholas Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques Slotine, Vikas Sindhwani

NeurIPS 2020 Ode to an ODE Krzysztof M Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

ICML 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, Tamas Sarlos, Adrian Weller, Vikas Sindhwani

ICLRW 2020 Time Dependence in Non-Autonomous Neural ODEs Jared Quincy Davis, Krzysztof Choromanski, Vikas Sindhwani, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia

CoRL 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

CoRL 2019 Data Efficient Reinforcement Learning for Legged Robots Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani

NeurIPS 2019 From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

CoRL 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

CoRL 2018 Policies Modulating Trajectory Generators Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

ICML 2018 Structured Evolution with Compact Architectures for Scalable Policy Optimization Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller

AISTATS 2018 The Geometry of Random Features Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller

JMLR 2017 Hierarchically Compositional Kernels for Scalable Nonparametric Learning Jie Chen, Haim Avron, Vikas Sindhwani

NeurIPS 2017 On Blackbox Backpropagation and Jacobian Sensing Krzysztof M Choromanski, Vikas Sindhwani

JMLR 2016 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney

ICML 2016 Recycling Randomness with Structure for Sublinear Time Kernel Expansions Krzysztof Choromanski, Vikas Sindhwani

NeurIPS 2015 Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar

ICML 2014 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael Mahoney

CVPR 2014 Random Laplace Feature Maps for Semigroup Kernels on Histograms Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

ICML 2013 Fast Conical Hull Algorithms for Near-Separable Non-Negative Matrix Factorization Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur

UAI 2013 Scalable Matrix-Valued Kernel Learning for High-Dimensional Nonlinear Multivariate Regression and Granger Causality Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano

NeurIPS 2013 Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff

ICML 2012 Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani

IJCAI 2011 Concept Labeling: Building Text Classifiers with Minimal Supervision Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani, Richard D. Lawrence

NeurIPS 2011 Non-Parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano

ICML 2011 Vector-Valued Manifold Regularization Ha Quang Minh, Vikas Sindhwani

NeurIPS 2010 Block Variable Selection in Multivariate Regression and High-Dimensional Causal Inference Vikas Sindhwani, Aurelie C. Lozano

ICML 2009 Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision Vikas Sindhwani, Prem Melville, Richard D. Lawrence

ICML 2008 An RKHS for Multi-View Learning and Manifold Co-Regularization Vikas Sindhwani, David S. Rosenberg

JMLR 2008 Optimization Techniques for Semi-Supervised Support Vector Machines Olivier Chapelle, Vikas Sindhwani, Sathiya S. Keerthi

NeurIPS 2008 Regularized Co-Clustering with Dual Supervision Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic

IJCAI 2007 Semi-Supervised Gaussian Process Classifiers Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi

NeurIPS 2006 An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models S. S. Keerthi, Vikas Sindhwani, Olivier Chapelle

NeurIPS 2006 Branch and Bound for Semi-Supervised Support Vector Machines Olivier Chapelle, Vikas Sindhwani, S. S. Keerthi

ICML 2006 Deterministic Annealing for Semi-Supervised Kernel Machines Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle

JMLR 2006 Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples Mikhail Belkin, Partha Niyogi, Vikas Sindhwani

NeurIPS 2006 Relational Learning with Gaussian Processes Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. S. Keerthi

ICML 2005 Beyond the Point Cloud: From Transductive to Semi-Supervised Learning Vikas Sindhwani, Partha Niyogi, Mikhail Belkin

AISTATS 2005 On Manifold Regularization Misha Belkin, Partha Niyogi, Vikas Sindhwani