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no code implementations • 2 Aug 2022 • Yunfan Zhao, Qingkai Pan, Krzysztof Choromanski, Deepali Jain, Vikas Sindhwani

We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states.

1 code implementation • 1 Apr 2022 • Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence

Large pretrained (e. g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on.

Ranked #8 on Video Retrieval on MSR-VTT-1kA (video-to-text R@1 metric)

no code implementations • 16 Mar 2022 • Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani

Though robot learning is often formulated in terms of discrete-time Markov decision processes (MDPs), physical robots require near-continuous multiscale feedback control.

1 code implementation • ICLR 2022 • 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

We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.

no code implementations • 6 Dec 2020 • Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng

Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag".

no code implementations • NeurIPS 2020 • Krzysztof M. Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

no code implementations • 24 Nov 2020 • Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

For our first two results, we consider the setting of safely learning linear dynamics.

no code implementations • 13 Aug 2020 • Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani

Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property.

no code implementations • NeurIPS 2020 • Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

no code implementations • ICLR Workshop DeepDiffEq 2019 • Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.

no code implementations • 31 Mar 2020 • Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly

We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz.

no code implementations • ICML 2020 • 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

We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$.

2 code implementations • 7 Oct 2019 • Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.

1 code implementation • 29 Jul 2019 • Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.

no code implementations • 8 Jul 2019 • Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani

We present a model-based framework for robot locomotion that achieves walking based on only 4. 5 minutes (45, 000 control steps) of data collected on a quadruped robot.

no code implementations • 7 Mar 2019 • Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani

Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics.

no code implementations • 31 Jul 2018 • Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.

no code implementations • 13 Apr 2018 • Vikas Sindhwani, Stephen Tu, Mohi Khansari

We propose a new non-parametric framework for learning incrementally stable dynamical systems x' = f(x) from a set of sampled trajectories.

no code implementations • ICML 2018 • Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller

We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees.

no code implementations • NeurIPS 2017 • Krzysztof M. Choromanski, Vikas Sindhwani

From a small number of calls to a given “blackbox" on random input perturbations, we show how to efficiently recover its unknown Jacobian, or estimate the left action of its Jacobian on a given vector.

no code implementations • 15 Oct 2017 • Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani

Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL).

no code implementations • 22 Nov 2016 • Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani

Motivated by applications in robotics and computer vision, we study problems related to spatial reasoning of a 3D environment using sublevel sets of polynomials.

no code implementations • 2 Aug 2016 • Jie Chen, Haim Avron, Vikas Sindhwani

We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods.

no code implementations • 29 May 2016 • Krzysztof Choromanski, Vikas Sindhwani

We propose a scheme for recycling Gaussian random vectors into structured matrices to approximate various kernel functions in sublinear time via random embeddings.

no code implementations • 9 Apr 2016 • Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath

Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks.

no code implementations • NeurIPS 2015 • Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar

We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices.

no code implementations • 29 Dec 2014 • Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael Mahoney

These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e. g., Gaussian kernel).

no code implementations • 9 Sep 2014 • Suyog Gupta, Vikas Sindhwani, Kailash Gopalakrishnan

This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementations.

no code implementations • 3 Sep 2014 • Vikas Sindhwani, Haim Avron

In order to fully utilize "big data", it is often required to use "big models".

no code implementations • 9 Aug 2014 • Vikas Sindhwani, Ha Quang Minh, Aurelie Lozano

We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems.

no code implementations • CVPR 2014 • Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

With the goal of accelerating the training and testing complexity of nonlinear kernel methods, several recent papers have proposed explicit embeddings of the input data into low-dimensional feature spaces, where fast linear methods can instead be used to generate approximate solutions.

no code implementations • 27 Dec 2013 • Abhishek Kumar, Vikas Sindhwani

Recently, a family of tractable NMF algorithms have been proposed under the assumption that the data matrix satisfies a separability condition Donoho & Stodden (2003); Arora et al. (2012).

no code implementations • NeurIPS 2013 • Haim Avron, Vikas Sindhwani, David Woodruff

Motivated by the desire to extend fast randomized techniques to nonlinear $l_p$ regression, we consider a class of structured regression problems.

no code implementations • NeurIPS 2011 • Vikas Sindhwani, Aurelie C. Lozano

We consider regularized risk minimization in a large dictionary of Reproducing kernel Hilbert Spaces (RKHSs) over which the target function has a sparse representation.

no code implementations • NeurIPS 2010 • Vikas Sindhwani, Aurelie C. Lozano

We consider multivariate regression problems involving high-dimensional predictor and response spaces.

no code implementations • NeurIPS 2008 • Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic

By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surpris- ingly impressive performance improvements over traditional one-sided (row) clustering techniques.

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