Benjamin Recht

According to our database1, Benjamin Recht authored at least 143 papers between 2002 and 2018.

Collaborative distances:
  • Dijkstra number2 of three.
  • Erdős number3 of two.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Homepage:

On csauthors.net:

Bibliography

2018
Sharp Time-Data Tradeoffs for Linear Inverse Problems.
IEEE Trans. Information Theory, 2018

Gradient Descent Learns Linear Dynamical Systems.
Journal of Machine Learning Research, 2018

numpywren: serverless linear algebra.
CoRR, 2018

Massively Parallel Hyperparameter Tuning.
CoRR, 2018

Minimax Lower Bounds for ℋ-Norm Estimation.
CoRR, 2018

A Successive-Elimination Approach to Adaptive Robotic Sensing.
CoRR, 2018

Safely Learning to Control the Constrained Linear Quadratic Regulator.
CoRR, 2018

A Tour of Reinforcement Learning: The View from Continuous Control.
CoRR, 2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?
CoRR, 2018

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator.
CoRR, 2018

Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law.
CoRR, 2018

Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System.
CoRR, 2018

Simple random search provides a competitive approach to reinforcement learning.
CoRR, 2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification.
CoRR, 2018

Tight query complexity lower bounds for PCA via finite sample deformed wigner law.
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification.
Proceedings of the Conference On Learning Theory, 2018

On the Approximation of Toeplitz Operators for Nonparametric H-norm Estimation.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization.
SIAM Journal on Optimization, 2017

The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems.
SIAM Journal on Optimization, 2017

Saturating Splines and Feature Selection.
Journal of Machine Learning Research, 2017

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator.
CoRR, 2017

An example of how false conclusions could be made with personalized health tracking and suggestions for avoiding similar situations.
CoRR, 2017

Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis.
CoRR, 2017

First-order Methods Almost Always Avoid Saddle Points.
CoRR, 2017

On the Sample Complexity of the Linear Quadratic Regulator.
CoRR, 2017

On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation.
CoRR, 2017

Flare Prediction Using Photospheric and Coronal Image Data.
CoRR, 2017

The Marginal Value of Adaptive Gradient Methods in Machine Learning.
CoRR, 2017

Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification.
CoRR, 2017

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime.
CoRR, 2017

On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation.
CoRR, 2017

Occupy the Cloud: Distributed Computing for the 99%.
CoRR, 2017

Meaningless comparisons lead to false optimism in medical machine learning.
CoRR, 2017

Exponential Stability Analysis via Integral Quadratic Constraints.
CoRR, 2017

Drizzle: Fast and Adaptable Stream Processing at Scale.
Proceedings of the 26th Symposium on Operating Systems Principles, 2017

The Marginal Value of Adaptive Gradient Methods in Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Breaking Locality Accelerates Block Gauss-Seidel.
Proceedings of the 34th International Conference on Machine Learning, 2017

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.
Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing.
Proceedings of the Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 2017

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime.
Proceedings of the 30th Conference on Learning Theory, 2017

Occupy the cloud: distributed computing for the 99%.
Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, 2017

2016
Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints.
SIAM Journal on Optimization, 2016

Understanding deep learning requires rethinking generalization.
CoRR, 2016

A Lyapunov Analysis of Momentum Methods in Optimization.
CoRR, 2016

Large Scale Kernel Learning using Block Coordinate Descent.
CoRR, 2016

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.
CoRR, 2016

Best-of-K Bandits.
CoRR, 2016

CYCLADES: Conflict-free Asynchronous Machine Learning.
CoRR, 2016

Universality of Mallows' and degeneracy of Kendall's kernels for rankings.
CoRR, 2016

Gradient Descent Converges to Minimizers.
CoRR, 2016

On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem.
CoRR, 2016

Gradient Descent Learns Linear Dynamical Systems.
CoRR, 2016

Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity.
Proceedings of the 2016 IEEE Wireless Health, 2016

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics.
Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, 2016

The Power of Adaptivity in Identifying Statistical Alternatives.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Train faster, generalize better: Stability of stochastic gradient descent.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Best-of-K-bandits.
Proceedings of the 29th Conference on Learning Theory, 2016

Gradient Descent Only Converges to Minimizers.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Near Minimax Line Spectral Estimation.
IEEE Trans. Information Theory, 2015

The Randomized Causation Coefficient.
Journal of Machine Learning Research, 2015

Superresolution without Separation.
CoRR, 2015

Parallel Correlation Clustering on Big Graphs.
CoRR, 2015

Sharp Time-Data Tradeoffs for Linear Inverse Problems.
CoRR, 2015

Isometric sketching of any set via the Restricted Isometry Property.
CoRR, 2015

Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets.
CoRR, 2015

A General Analysis of the Convergence of ADMM.
CoRR, 2015

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization.
CoRR, 2015

Train faster, generalize better: Stability of stochastic gradient descent.
CoRR, 2015

Exponential Convergence Bounds using Integral Quadratic Constraints.
CoRR, 2015

Parallel Correlation Clustering on Big Graphs.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A General Analysis of the Convergence of ADMM.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Exponential convergence bounds using integral quadratic constraints.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Superresolution without separation.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

The alternating descent conditional gradient method for sparse inverse problems.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

2014
Blind Deconvolution Using Convex Programming.
IEEE Trans. Information Theory, 2014

Fast Methods for Denoising Matrix Completion Formulations, with Applications to Robust Seismic Data Interpolation.
SIAM J. Scientific Computing, 2014

Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints.
CoRR, 2014

Compressive classification and the rare eclipse problem.
CoRR, 2014

Robust line spectral estimation.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013
Atomic Norm Denoising With Applications to Line Spectral Estimation.
IEEE Trans. Signal Processing, 2013

Compressed Sensing Off the Grid.
IEEE Trans. Information Theory, 2013

Decomposition Methods for Large Scale LP Decoding.
IEEE Trans. Information Theory, 2013

Parallel stochastic gradient algorithms for large-scale matrix completion.
Math. Program. Comput., 2013

Simple bounds for recovering low-complexity models.
Math. Program., 2013

Near Minimax Line Spectral Estimation
CoRR, 2013

An SVD-free Pareto curve approach to rank minimization
CoRR, 2013

Near minimax line spectral estimation.
Proceedings of the 47th Annual Conference on Information Sciences and Systems, 2013

Sparse recovery over continuous dictionaries-just discretize.
Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012
Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences.
Proceedings of the COLT 2012, 2012

Universal Measurement Bounds for Structured Sparse Signal Recovery.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

The Convex Geometry of Linear Inverse Problems.
Foundations of Computational Mathematics, 2012

Blind Deconvolution using Convex Programming
CoRR, 2012

Query Complexity of Derivative-Free Optimization
CoRR, 2012

Compressed Sensing off the Grid
CoRR, 2012

Factoring nonnegative matrices with linear programs
CoRR, 2012

Linear System Identification via Atomic Norm Regularization
CoRR, 2012

Atomic norm denoising with applications to line spectral estimation
CoRR, 2012

Decomposition Methods for Large Scale LP Decoding
CoRR, 2012

Towards a Unified Architecture for in-RDBMS Analytics
CoRR, 2012

Exact matrix completion via convex optimization.
Commun. ACM, 2012

Security Analysis of Smartphone Point-of-Sale Systems.
Proceedings of the 6th USENIX Workshop on Offensive Technologies, 2012

K-subspaces with missing data.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Towards a unified architecture for in-RDBMS analytics.
Proceedings of the ACM SIGMOD International Conference on Management of Data, 2012

Factoring nonnegative matrices with linear programs.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Query Complexity of Derivative-Free Optimization.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Suppressing pseudocodewords by penalizing the objective of LP decoding.
Proceedings of the 2012 IEEE Information Theory Workshop, 2012

Linear system identification via atomic norm regularization.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Compressive sensing off the grid.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012

The l1 penalized decoder and its reweighted LP.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011
Null space conditions and thresholds for rank minimization.
Math. Program., 2011

A Simpler Approach to Matrix Completion.
Journal of Machine Learning Research, 2011

Probability of unique integer solution to a system of linear equations.
European Journal of Operational Research, 2011

HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
CoRR, 2011

Simple Bounds for Low-complexity Model Reconstruction
CoRR, 2011

Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals.
CoRR, 2011

Dimensionality reduction: Beyond the Johnson-Lindenstrauss bound.
Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, 2011

Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic.
Proceedings of the IJCAI 2011, 2011

Atomic norm denoising with applications to line spectral estimation.
Proceedings of the 49th Annual Allerton Conference on Communication, 2011

Decomposition methods for large scale LP decoding.
Proceedings of the 49th Annual Allerton Conference on Communication, 2011

2010
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization.
SIAM Review, 2010

Online Identification and Tracking of Subspaces from Highly Incomplete Information
CoRR, 2010

High-Dimensional Matched Subspace Detection When Data are Missing
CoRR, 2010

Practical Large-Scale Optimization for Max-norm Regularization.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Transduction with Matrix Completion: Three Birds with One Stone.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Sample complexity for 1-bit compressed sensing and sparse classification.
Proceedings of the IEEE International Symposium on Information Theory, 2010

High-dimensional Matched Subspace Detection when data are missing.
Proceedings of the IEEE International Symposium on Information Theory, 2010

2009
Exact Matrix Completion via Convex Optimization.
Foundations of Computational Mathematics, 2009

A Simpler Approach to Matrix Completion
CoRR, 2009

Learning kernels from indefinite similarities.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Exact Matrix Completion via Convex Optimization
CoRR, 2008

Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

Determining interconnections in biochemical networks using linear programming.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

2007
Learning to Transform Time Series with a Few Examples.
IEEE Trans. Pattern Anal. Mach. Intell., 2007

Random Features for Large-Scale Kernel Machines.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Unsupervised Regression with Applications to Nonlinear System Identification.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Interaction techniques for musical performance with tabletop tangible interfaces.
Proceedings of the International Conference on Advances in Computer Entertainment Technology, 2006

2005
Learning Appearance Manifolds from Video.
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

2004
Distributed control of systems over discrete Groups.
IEEE Trans. Automat. Contr., 2004

2002
Audiopad: A Tag-based Interface for Musical Performance.
Proceedings of the New Interfaces for Musical Expression, 2002


  Loading...