no code implementations • 9 Sep 2014 • Sashank Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra
We develop randomized (block) coordinate descent (CD) methods for linearly constrained convex optimization.
no code implementations • NeurIPS 2015 • Ahmed Hefny, Carlton Downey, Geoffrey Gordon
To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L1 regularization.
no code implementations • NeurIPS 2015 • Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alex Smola
We demonstrate the empirical performance of our method through a concrete realization of asynchronous SVRG.
no code implementations • 19 Mar 2016 • Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola
We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them.
1 code implementation • 12 Feb 2017 • Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
We propose a framework for modeling and estimating the state of controlled dynamical systems, where an agent can affect the system through actions and receives partial observations.
no code implementations • 14 Feb 2017 • Carlton Downey, Ahmed Hefny, Geoffrey Gordon
Unfortunately it is not obvious how to apply apply an EM style algorithm in the context of PSRs as the Log Likelihood is not well defined for all PSRs.
no code implementations • NeurIPS 2017 • Carlton Downey, Ahmed Hefny, Boyue Li, Byron Boots, Geoffrey Gordon
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems.
2 code implementations • ICML 2018 • Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon
Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward.
2 code implementations • 29 Nov 2021 • Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Khaled S. Refaat, Nigamaa Nayakanti, Andre Cornman, Kan Chen, Bertrand Douillard, Chi Pang Lam, Dragomir Anguelov, Benjamin Sapp
Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving.
Ranked #16 on Motion Forecasting on Argoverse CVPR 2020