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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.

1 code implementation • 9 Dec 2021 • Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa

We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis.

1 code implementation • 30 Jun 2021 • Mihaela Curmei, Sarah Dean, Benjamin Recht

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery.

1 code implementation • 5 Mar 2021 • Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan

Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations.

no code implementations • 10 Feb 2021 • Moritz Hardt, Benjamin Recht

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions.

no code implementations • 28 Jan 2021 • Tengyuan Liang, Benjamin Recht

Under the assumption that the data is independently and identically distributed, the mistake bound implies that MNIC generalizes at a rate proportional to the norm of the interpolating solution and inversely proportional to the number of data points.

no code implementations • 21 Nov 2020 • Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron D. Ames

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains.

1 code implementation • 7 Nov 2020 • Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan

We observe that offline metrics are correlated with online performance over a range of environments.

1 code implementation • 30 Oct 2020 • Sarah Dean, Andrew J. Taylor, Ryan K. Cosner, Benjamin Recht, Aaron D. Ames

The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions.

no code implementations • 16 Oct 2020 • Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use.

1 code implementation • 27 Aug 2020 • Sarah Dean, Benjamin Recht

In order to certify performance and safety, feedback control requires precise characterization of sensor errors.

1 code implementation • NeurIPS 2020 • Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets.

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 • ICML 2020 • John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data.

1 code implementation • 12 Mar 2020 • Esther Rolf, Michael. I. Jordan, Benjamin Recht

Observational data are often accompanied by natural structural indices, such as time stamps or geographic locations, which are meaningful to prediction tasks but are often discarded.

1 code implementation • ICML 2020 • Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

We investigate the connections between neural networks and simple building blocks in kernel space.

2 code implementations • 20 Dec 2019 • Sarah Dean, Sarah Rich, Benjamin Recht

When the systems are deployed, these models determine the availability of content and information to different users.

no code implementations • NeurIPS 2019 • Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.

no code implementations • 25 Sep 2019 • Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

We conduct a large experimental comparison of various robustness metrics for image classification.

no code implementations • L4DC 2020 • Sarah Dean, Nikolai Matni, Benjamin Recht, Vickie Ye

Motivated by vision-based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, is extracted from complex and nonlinear data, such as a camera image.

1 code implementation • ICCV 2021 • Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

no code implementations • WS 2019 • Orianna Demasi, Marti A. Hearst, Benjamin Recht

A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation.

no code implementations • NeurIPS 2019 • Karl Krauth, Stephen Tu, Benjamin Recht

We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand the limits of reinforcement learning (RL) algorithms on continuous control tasks.

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 • ICML Workshop Deep_Phenomen 2019 • Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.

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 Workshop ImageNet_PPF 2021 • Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet.

1 code implementation • 2 Feb 2019 • Max Simchowitz, Ross Boczar, Benjamin Recht

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning.

no code implementations • 9 Dec 2018 • Stephen Tu, Benjamin Recht

The effectiveness of model-based versus model-free methods is a long-standing question in reinforcement learning (RL).

no code implementations • 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.

4 code implementations • ICLR 2018 • Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Modern learning models are characterized by large hyperparameter spaces and long training times.

no code implementations • 28 Sep 2018 • Stephen Tu, Ross Boczar, Benjamin Recht

The problem of estimating the $\mathcal{H}_\infty$-norm of an LTI system from noisy input/output measurements has attracted recent attention as an alternative to parameter identification for bounding unmodeled dynamics in robust control.

no code implementations • 27 Sep 2018 • Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire Tomlin

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions.

2 code implementations • 26 Sep 2018 • Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht

We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques.

no code implementations • 25 Jun 2018 • Benjamin Recht

This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications.

3 code implementations • 1 Jun 2018 • Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.

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.

no code implementations • 4 Apr 2018 • Max Simchowitz, Ahmed El Alaoui, Benjamin Recht

We show that for every $\mathtt{gap} \in (0, 1/2]$, there exists a distribution over matrices $\mathbf{M}$ for which 1) $\mathrm{gap}_r(\mathbf{M}) = \Omega(\mathtt{gap})$ (where $\mathrm{gap}_r(\mathbf{M})$ is the normalized gap between the $r$ and $r+1$-st largest-magnitude eigenvector of $\mathbf{M}$), and 2) any algorithm $\mathsf{Alg}$ which takes fewer than $\mathrm{const} \times \frac{r \log d}{\sqrt{\mathtt{gap}}}$ queries fails (with overwhelming probability) to identity a matrix $\widehat{\mathsf{V}} \in \mathbb{R}^{d \times r}$ with orthonormal columns for which $\langle \widehat{\mathsf{V}}, \mathbf{M} \widehat{\mathsf{V}}\rangle \ge (1 - \mathrm{const} \times \mathtt{gap})\sum_{i=1}^r \lambda_i(\mathbf{M})$.

1 code implementation • 25 Mar 2018 • Ross Boczar, Nikolai Matni, Benjamin Recht

As the systems we control become more complex, first-principle modeling becomes either impossible or intractable, motivating the use of machine learning techniques for the control of systems with continuous action spaces.

24 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 • ICLR 2018 • Lisha Li, Kevin Jamieson, Afshin Rostamizadeh, Katya Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs.

no code implementations • ICML 2018 • Stephen Tu, Benjamin Recht

Reinforcement learning (RL) has been successfully used to solve many continuous control tasks.

no code implementations • 20 Oct 2017 • Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael. I. Jordan, Benjamin Recht

We establish that first-order methods avoid saddle points for almost all initializations.

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 • 3 Aug 2017 • Eric Jonas, Monica G. Bobra, Vaishaal Shankar, J. Todd Hoeksema, Benjamin Recht

This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.

no code implementations • 15 Jul 2017 • Stephen Tu, Ross Boczar, Andrew Packard, Benjamin Recht

We derive bounds on the number of noisy input/output samples from a stable linear time-invariant system that are sufficient to guarantee that the corresponding finite impulse response approximation is close to the true system in the $\mathcal{H}_\infty$-norm.

2 code implementations • NeurIPS 2017 • Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht

Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks.

no code implementations • 14 Apr 2017 • Max Simchowitz, Ahmed El Alaoui, Benjamin Recht

We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA).

no code implementations • 16 Feb 2017 • Max Simchowitz, Kevin Jamieson, Benjamin Recht

Moreover, our lower bounds zero-in on the number of times each \emph{individual} arm needs to be pulled, uncovering new phenomena which are drowned out in the aggregate sample complexity.

no code implementations • NeurIPS 2016 • Kevin G. Jamieson, Daniel Haas, Benjamin Recht

This paper studies the trade-off between two different kinds of pure exploration: breadth versus depth.

8 code implementations • 10 Nov 2016 • Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.

no code implementations • 29 Oct 2016 • Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht

Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements.

no code implementations • 21 Sep 2016 • Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael Jordan

We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.

no code implementations • 16 Sep 2016 • Moritz Hardt, Tengyu Ma, Benjamin Recht

We prove that stochastic gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system from a sequence of noisy observations generated by the system.

1 code implementation • NeurIPS 2016 • Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael. I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht

We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting.

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 • 9 Mar 2016 • Max Simchowitz, Kevin Jamieson, Benjamin Recht

This paper studies the Best-of-K Bandit game: At each time the player chooses a subset S among all N-choose-K possible options and observes reward max(X(i) : i in S) where X is a random vector drawn from a joint distribution.

no code implementations • 17 Feb 2016 • Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht

We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points.

no code implementations • 16 Feb 2016 • Jason D. Lee, Max Simchowitz, Michael. I. Jordan, Benjamin Recht

We show that gradient descent converges to a local minimizer, almost surely with random initialization.

no code implementations • 3 Sep 2015 • Moritz Hardt, Benjamin Recht, Yoram Singer

In the non-convex case, we give a new interpretation of common practices in neural networks, and formally show that popular techniques for training large deep models are indeed stability-promoting.

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.

no code implementations • NeurIPS 2015 • Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael. I. Jordan

We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably.

no code implementations • 16 Jul 2015 • Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi

We sharply characterize the convergence rate associated with a wide variety of random measurement ensembles in terms of the number of measurements and structural complexity of the signal with respect to the chosen penalty function.

no code implementations • 11 Jun 2015 • Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi

In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices.

no code implementations • 6 Feb 2015 • Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew Packard, Michael. I. Jordan

We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex.

Optimization and Control Numerical Analysis

no code implementations • 15 Sep 2014 • David Lopez-Paz, Krikamol Muandet, Benjamin Recht

We are interested in learning causal relationships between pairs of random variables, purely from observational data.

no code implementations • 11 Apr 2014 • Afonso S. Bandeira, Dustin G. Mixon, Benjamin Recht

This paper addresses the fundamental question of when convex sets remain disjoint after random projection.

1 code implementation • 21 Nov 2012 • Ali Ahmed, Benjamin Recht, Justin Romberg

That is, we show that if $\boldsymbol{x}$ is drawn from a random subspace of dimension $N$, and $\boldsymbol{w}$ is a vector in a subspace of dimension $K$ whose basis vectors are "spread out" in the frequency domain, then nuclear norm minimization recovers $\boldsymbol{w}\boldsymbol{x}^*$ without error.

Information Theory Information Theory

1 code implementation • NeurIPS 2012 • Victor Bittorf, Benjamin Recht, Christopher Re, Joel A. Tropp

The constraints are chosen to ensure that the matrix C selects features; these features can then be used to find a low-rank NMF of X.

1 code implementation • 3 Apr 2012 • Badri Narayan Bhaskar, Gongguo Tang, Benjamin Recht

Motivated by recent work on atomic norms in inverse problems, we propose a new approach to line spectral estimation that provides theoretical guarantees for the mean-squared-error (MSE) performance in the presence of noise and without knowledge of the model order.

Information Theory Information Theory

2 code implementations • 19 Feb 2012 • Benjamin Recht, Christopher Re

We detail the consequences of this inequality for stochastic gradient descent and the randomized Kaczmarz algorithm for solving linear systems.

no code implementations • NeurIPS 2011 • Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks.

5 code implementations • 28 Jun 2011 • Feng Niu, Benjamin Recht, Christopher Re, Stephen J. Wright

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks.

1 code implementation • 21 Jun 2010 • Laura Balzano, Robert Nowak, Benjamin Recht

GROUSE performs exceptionally well in practice both in tracking subspaces and as an online algorithm for matrix completion.

no code implementations • NeurIPS 2008 • Ali Rahimi, Benjamin Recht

``I am training a randomly wired neural net to play tic-tac-toe,'' Sussman replied.

no code implementations • 29 May 2008 • Emmanuel J. Candes, Benjamin Recht

We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries.

Information Theory Information Theory

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