no code implementations • ICML 2020 • Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt
We perform an in-depth evaluation of human accuracy on the ImageNet dataset.
no code implementations • 17 Oct 2022 • Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Recent approaches to data-driven MPC have used the simplest form of imitation learning known as behavior cloning to learn controllers that mimic the performance of MPC by online sampling of the trajectories of the closed-loop MPC system.
no code implementations • 29 Dec 2021 • Ali Jadbabaie, Horia Mania, Devavrat Shah, Suvrit Sra
We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system.
no code implementations • 31 Dec 2020 • Horia Mania, Suvrit Sra
Recent studies of generalization in deep learning have observed a puzzling trend: accuracies of models on one data distribution are approximately linear functions of the accuracies on another distribution.
no code implementations • 14 Dec 2020 • Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan
We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is required to learn its preferences from experience.
no code implementations • 18 Jun 2020 • Horia Mania, Michael. I. Jordan, Benjamin Recht
While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i. i. d.
no code implementations • 12 Jun 2019 • Lydia T. Liu, Horia Mania, Michael. I. Jordan
Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned.
no code implementations • NeurIPS 2019 • Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht
Excessive reuse of test data has become commonplace in today's machine learning workflows.
no code implementations • NeurIPS 2019 • Horia Mania, Stephen Tu, Benjamin Recht
We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system and the cost incurred by using the optimal LQ controller enjoys a fast statistical rate, scaling as the square of the parameter error.
1 code implementation • NeurIPS 2018 • Horia Mania, Aurelia Guy, Benjamin Recht
Common evaluation methodology shows that our method matches state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks.
no code implementations • NeurIPS 2018 • Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs.
25 code implementations • 19 Mar 2018 • Horia Mania, Aurelia Guy, Benjamin Recht
A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.
no code implementations • 22 Feb 2018 • Max Simchowitz, Horia Mania, Stephen Tu, Michael. I. Jordan, Benjamin Recht
We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal performance for the identification of linear dynamical systems from a single observed trajectory.
no code implementations • 4 Oct 2017 • Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown.
no code implementations • 25 Mar 2016 • Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael. I. Jordan, Benjamin Recht
This paper studies the use of reproducing kernel Hilbert space methods for learning from permutation-valued features.
no code implementations • 24 Jul 2015 • Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael. I. Jordan
We demonstrate experimentally on a 16-core machine that the sparse and parallel version of SVRG is in some cases more than four orders of magnitude faster than the standard SVRG algorithm.