Vikas Sindhwani

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Google ∙ ibm

Researcher in the Google Brain team.

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

Rearranging and manipulating deformable objects such as cables, fabrics,...
12/06/2020 ∙ by Daniel Seita, et al. ∙ 7 ∙ share

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Safely Learning Dynamical Systems from Short Trajectories

A fundamental challenge in learning to control an unknown dynamical syst...
11/24/2020 ∙ by Amir Ali Ahmadi, et al. ∙ 0 ∙ share

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Transporter Networks: Rearranging the Visual World for Robotic Manipulation

Robotic manipulation can be formulated as inducing a sequence of spatial...
10/27/2020 ∙ by Andy Zeng, et al. ∙ 0 ∙ share

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

We propose a novel method for planning shortest length piecewise-linear ...
10/16/2020 ∙ by Bachir El Khadir, et al. ∙ 0 ∙ share

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Learning Stability Certificates from Data

Many existing tools in nonlinear control theory for establishing stabili...
08/13/2020 ∙ by Nicholas M. Boffi, et al. ∙ 2 ∙ share

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An Ode to an ODE

We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...
06/19/2020 ∙ by Krzysztof Choromanski, et al. ∙ 18 ∙ share

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Time Dependence in Non-Autonomous Neural ODEs

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...
05/05/2020 ∙ by Jared Quincy Davis, et al. ∙ 7 ∙ share

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Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capabl...
03/31/2020 ∙ by Wenbo Gao, et al. ∙ 18 ∙ share

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Stochastic Flows and Geometric Optimization on the Orthogonal Group

We present a new class of stochastic, geometrically-driven optimization ...
03/30/2020 ∙ by Krzysztof Choromanski, et al. ∙ 2 ∙ share

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Policies Modulating Trajectory Generators

We propose an architecture for learning complex controllable behaviors b...
10/07/2019 ∙ by Atil Iscen, et al. ∙ 12 ∙ share

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Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization

We propose a novel framework for learning stabilizable nonlinear dynamic...
07/29/2019 ∙ by Sumeet Singh, et al. ∙ 0 ∙ share

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Data Efficient Reinforcement Learning for Legged Robots

We present a model-based framework for robot locomotion that achieves wa...
07/08/2019 ∙ by Yuxiang Yang, et al. ∙ 6 ∙ share

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Teleoperator Imitation with Continuous-time Safety

Learning to effectively imitate human teleoperators, with generalization...
05/23/2019 ∙ by Bachir El Khadir, et al. ∙ 0 ∙ share

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Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dim...
03/07/2019 ∙ by Krzysztof Choromanski, et al. ∙ 12 ∙ share

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

Interest in derivative-free optimization (DFO) and "evolutionary strateg...
03/07/2019 ∙ by Krzysztof Choromanski, et al. ∙ 12 ∙ share

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Learning Stabilizable Dynamical Systems via Control Contraction Metrics

We propose a novel framework for learning stabilizable nonlinear dynamic...
07/31/2018 ∙ by Sumeet Singh, et al. ∙ 0 ∙ share

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Optimizing Simulations with Noise-Tolerant Structured Exploration

We propose a simple drop-in noise-tolerant replacement for the standard ...
05/20/2018 ∙ by Krzysztof Choromanski, et al. ∙ 0 ∙ share

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Learning Contracting Vector Fields For Stable Imitation Learning

We propose a new non-parametric framework for learning incrementally sta...
04/13/2018 ∙ by Vikas Sindhwani, et al. ∙ 0 ∙ share

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Structured Evolution with Compact Architectures for Scalable Policy Optimization

We present a new method of blackbox optimization via gradient approximat...
04/06/2018 ∙ by Krzysztof Choromanski, et al. ∙ 0 ∙ share

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Manifold Regularization for Kernelized LSTD

Policy evaluation or value function or Q-function approximation is a key...
10/15/2017 ∙ by Xinyan Yan, et al. ∙ 0 ∙ share

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Geometry of 3D Environments and Sum of Squares Polynomials

Motivated by applications in robotics and computer vision, we study prob...
11/22/2016 ∙ by Amir Ali Ahmadi, et al. ∙ 0 ∙ share

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Hierarchically Compositional Kernels for Scalable Nonparametric Learning

We propose a novel class of kernels to alleviate the high computational ...
08/02/2016 ∙ by Jie Chen, et al. ∙ 0 ∙ share

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Recycling Randomness with Structure for Sublinear time Kernel Expansions

We propose a scheme for recycling Gaussian random vectors into structure...
05/29/2016 ∙ by Krzysztof Choromanski, et al. ∙ 0 ∙ share

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Learning Compact Recurrent Neural Networks

Recurrent neural networks (RNNs), including long short-term memory (LSTM...
04/09/2016 ∙ by Zhiyun Lu, et al. ∙ 0 ∙ share

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Structured Transforms for Small-Footprint Deep Learning

We consider the task of building compact deep learning pipelines suitabl...
10/06/2015 ∙ by Vikas Sindhwani, et al. ∙ 0 ∙ share

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Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

We consider the problem of improving the efficiency of randomized Fourie...
12/29/2014 ∙ by Haim Avron, et al. ∙ 0 ∙ share

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

We propose a general matrix-valued multiple kernel learning framework fo...
08/09/2014 ∙ by Vikas Sindhwani, et al. ∙ 0 ∙ share

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Near-separable Non-negative Matrix Factorization with ℓ_1- and Bregman Loss Functions

Recently, a family of tractable NMF algorithms have been proposed under ...
12/27/2013 ∙ by Abhishek Kumar, et al. ∙ 0 ∙ share

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

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)...
10/03/2012 ∙ by Abhishek Kumar, et al. ∙ 0 ∙ share

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Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization

We describe novel subgradient methods for a broad class of matrix optimi...
06/27/2012 ∙ by Haim Avron, et al. ∙ 0 ∙ share

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Vikas Sindhwani

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