Benjamin Recht

research

∙ 01/24/2023

K-Planes: Explicit Radiance Fields in Space, Time, and Appearance

We introduce k-planes, a white-box model for radiance fields in arbitrar...

2 Sara Fridovich-Keil, et al. ∙

research

∙ 08/01/2022

Towards Psychologically-Grounded Dynamic Preference Models

Designing recommendation systems that serve content aligned with time va...

0 Mihaela Curmei, et al. ∙

research

∙ 12/09/2021

Plenoxels: Radiance Fields without Neural Networks

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

3 Alex Yu, et al. ∙

research

∙ 12/02/2021

A note on sampling biases in the Bangladesh mask trial

A recent cluster trial in Bangladesh randomized 600 villages into 300 tr...

0 Maria Chikina, et al. ∙

research

∙ 06/30/2021

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

In this work, we consider how preference models in interactive recommend...

0 Mihaela Curmei, et al. ∙

research

∙ 03/05/2021

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

Collecting more diverse and representative training data is often touted...

30 Esther Rolf, et al. ∙

research

∙ 02/10/2021

Patterns, predictions, and actions: A story about machine learning

This graduate textbook on machine learning tells a story of how patterns...

0 Moritz Hardt, et al. ∙

research

∙ 01/28/2021

Interpolating Classifiers Make Few Mistakes

This paper provides elementary analyses of the regret and generalization...

0 Tengyuan Liang, et al. ∙

research

∙ 11/21/2020

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty

Modern nonlinear control theory seeks to endow systems with properties s...

0 Andrew J. Taylor, et al. ∙

research

∙ 11/07/2020

Do Offline Metrics Predict Online Performance in Recommender Systems?

Recommender systems operate in an inherently dynamical setting. Past rec...

0 Karl Krauth, et al. ∙

research

∙ 10/30/2020

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

Modern nonlinear control theory seeks to develop feedback controllers th...

0 Sarah Dean, et al. ∙

research

∙ 10/16/2020

A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery

Combining satellite imagery with machine learning (SIML) has the potenti...

16 Esther Rolf, et al. ∙

research

∙ 08/27/2020

Certainty Equivalent Perception-Based Control

In order to certify performance and safety, feedback control requires pr...

0 Sarah Dean, et al. ∙

research

∙ 07/10/2020

Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty

We study the problem of finding equilibrium strategies in multi-agent ga...

0 Wenshuo Guo, et al. ∙

research

∙ 07/01/2020

Measuring Robustness to Natural Distribution Shifts in Image Classification

We study how robust current ImageNet models are to distribution shifts a...

3 Rohan Taori, et al. ∙

research

∙ 06/18/2020

Active Learning for Nonlinear System Identification with Guarantees

While the identification of nonlinear dynamical systems is a fundamental...

41 Horia Mania, et al. ∙

research

∙ 04/29/2020

The Effect of Natural Distribution Shift on Question Answering Models

We build four new test sets for the Stanford Question Answering Dataset ...

9 John Miller, et al. ∙

research

∙ 03/12/2020

Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information

Observational data are often accompanied by natural structural indices, ...

0 Esther Rolf, et al. ∙

research

∙ 03/04/2020

Neural Kernels Without Tangents

We investigate the connections between neural networks and simple buildi...

10 Vaishaal Shankar, et al. ∙

research

∙ 12/20/2019

Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information

Recommender systems often rely on models which are trained to maximize a...

15 Sarah Dean, et al. ∙

research

∙ 07/08/2019

Robust Guarantees for Perception-Based Control

Motivated by vision based control of autonomous vehicles, we consider th...

5 Sarah Dean, et al. ∙

research

∙ 06/05/2019

A systematic framework for natural perturbations from videos

We introduce a systematic framework for quantifying the robustness of cl...

10 Vaishaal Shankar, et al. ∙

research

∙ 05/30/2019

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

We study the sample complexity of approximate policy iteration (PI) for ...

0 Karl Krauth, et al. ∙

research

∙ 05/29/2019

Model Similarity Mitigates Test Set Overuse

Excessive reuse of test data has become commonplace in today's machine l...

8 Horia Mania, et al. ∙

research

∙ 02/21/2019

Certainty Equivalent Control of LQR is Efficient

We study the performance of the certainty equivalent controller on the L...

8 Horia Mania, et al. ∙

research

∙ 02/02/2019

Learning Linear Dynamical Systems with Semi-Parametric Least Squares

We analyze a simple prefiltered variation of the least squares estimator...

20 Max Simchowitz, et al. ∙

research

∙ 12/09/2018

The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint

The effectiveness of model-based versus model-free methods is a long-sta...

6 Stephen Tu, et al. ∙

research

∙ 10/23/2018

numpywren: serverless linear algebra

Linear algebra operations are widely used in scientific computing and ma...

0 Vaishaal Shankar, et al. ∙

research

∙ 10/13/2018

Massively Parallel Hyperparameter Tuning

Modern learning models are characterized by large hyperparameter spaces....

0 Liam Li, et al. ∙

research

∙ 09/28/2018

Minimax Lower Bounds for H_∞-Norm Estimation

The problem of estimating the H_∞-norm of an LTI system from noisy input...

0 Stephen Tu, et al. ∙

research

∙ 09/27/2018

A Successive-Elimination Approach to Adaptive Robotic Sensing

We study the adaptive sensing problem for the multiple source seeking pr...

2 Esther Rolf, et al. ∙

research

∙ 09/26/2018

Safely Learning to Control the Constrained Linear Quadratic Regulator

We study the constrained linear quadratic regulator with unknown dynamic...

20 Sarah Dean, et al. ∙

research

∙ 06/25/2018

A Tour of Reinforcement Learning: The View from Continuous Control

This manuscript surveys reinforcement learning from the perspective of o...

14 Benjamin Recht, et al. ∙

research

∙ 06/01/2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Machine learning is currently dominated by largely experimental work foc...

0 Benjamin Recht, et al. ∙

research

∙ 05/23/2018

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator

We consider adaptive control of the Linear Quadratic Regulator (LQR), wh...

2 Sarah Dean, et al. ∙

research

∙ 04/04/2018

Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law

We prove a query complexity lower bound for approximating the top r dime...

0 Max Simchowitz, et al. ∙

research

∙ 03/25/2018

Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System

As the systems we control become more complex, first-principle modeling ...

0 Ross Boczar, et al. ∙

research

∙ 03/19/2018

Simple random search provides a competitive approach to reinforcement learning

A common belief in model-free reinforcement learning is that methods bas...

0 Horia Mania, et al. ∙

research

∙ 02/22/2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

We prove that the ordinary least-squares (OLS) estimator attains nearly ...

0 Max Simchowitz, et al. ∙

research

∙ 12/22/2017

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator

Reinforcement learning (RL) has been successfully used to solve many con...

0 Stephen Tu, et al. ∙

research

∙ 11/15/2017

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

Personalizing interventions and treatments is a necessity for optimal me...

0 Orianna DeMasi, et al. ∙

research

∙ 10/25/2017

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

In this project, we build a modular, scalable system that can collect, s...

0 Ian Bolliger, et al. ∙

research

∙ 10/20/2017

First-order Methods Almost Always Avoid Saddle Points

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

0 Jason D. Lee, et al. ∙

research

∙ 10/04/2017

On the Sample Complexity of the Linear Quadratic Regulator

This paper addresses the optimal control problem known as the Linear Qua...

0 Sarah Dean, et al. ∙

research

∙ 08/03/2017

Flare Prediction Using Photospheric and Coronal Image Data

The precise physical process that triggers solar flares is not currently...

0 Eric Jonas, et al. ∙

research

∙ 07/19/2017

Meaningless comparisons lead to false optimism in medical machine learning

A new trend in medicine is the use of algorithms to analyze big datasets...

0 Orianna DeMasi, et al. ∙

research

∙ 05/23/2017

The Marginal Value of Adaptive Gradient Methods in Machine Learning

Adaptive optimization methods, which perform local optimization with a m...

0 Ashia C. Wilson, et al. ∙

research

∙ 04/14/2017

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

We prove a query complexity lower bound on rank-one principal component ...

0 Max Simchowitz, et al. ∙

research

∙ 02/16/2017

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime

We propose a novel technique for analyzing adaptive sampling called the ...

0 Max Simchowitz, et al. ∙

research

∙ 09/21/2016

Saturating Splines and Feature Selection

We extend the adaptive regression spline model by incorporating saturati...

0 Nicholas Boyd, et al. ∙

Benjamin Recht

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