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 • NeurIPS 2018 • Mark Rowland, Krzysztof M. Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller
Monte Carlo sampling in high-dimensional, low-sample settings is important in many machine learning tasks.
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 • NeurIPS 2013 • Krzysztof M. Choromanski, Tony Jebara, Kui Tang
The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy.