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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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12/12/2022
Variance-Reduced Conservative Policy Iteration
We study the sample complexity of reducing reinforcement learning to a s...
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11/21/2022
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret
We consider the fundamental problem of online control of a linear dynami...
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10/11/2022
Multi-User Reinforcement Learning with Low Rank Rewards
In this work, we consider the problem of collaborative multi-user reinfo...
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07/29/2022
Adaptive Gradient Methods at the Edge of Stability
Very little is known about the training dynamics of adaptive gradient me...
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02/06/2022
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
We revisit the classical online portfolio selection problem. It is widel...
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11/19/2021
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the I...
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10/16/2021
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Q-learning is a popular Reinforcement Learning (RL) algorithm which is w...
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10/11/2021
The Skellam Mechanism for Differentially Private Federated Learning
We introduce the multi-dimensional Skellam mechanism, a discrete differe...
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10/06/2021
Efficient Methods for Online Multiclass Logistic Regression
Multiclass logistic regression is a fundamental task in machine learning...
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03/01/2021
Acceleration via Fractal Learning Rate Schedules
When balancing the practical tradeoffs of iterative methods for large-sc...
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02/26/2021
A Regret Minimization Approach to Iterative Learning Control
We consider the setting of iterative learning control, or model-based po...
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02/19/2021
Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking
We present an open-source library of natively differentiable physics and...
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02/12/2021
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator...
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10/26/2020
Stochastic Optimization with Laggard Data Pipelines
State-of-the-art optimization is steadily shifting towards massively par...
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02/26/2020
Disentangling Adaptive Gradient Methods from Learning Rates
We investigate several confounding factors in the evaluation of optimiza...
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02/04/2020
A Deep Conditioning Treatment of Neural Networks
We study the role of depth in training randomly initialized overparamete...
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09/11/2019
Logarithmic Regret for Online Control
We study optimal regret bounds for control in linear dynamical systems u...
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06/20/2019
Boosting for Dynamical Systems
We propose a framework of boosting for learning and control in environme...
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03/01/2019
Design and Development of Underwater Vehicle: ANAHITA
Anahita is an autonomous underwater vehicle which is currently being dev...
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02/23/2019
Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial distu...
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02/12/2019
Extreme Tensoring for Low-Memory Preconditioning
State-of-the-art models are now trained with billions of parameters, rea...
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06/08/2018
The Case for Full-Matrix Adaptive Regularization
Adaptive regularization methods come in diagonal and full-matrix variant...
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05/27/2018
cpSGD: Communication-efficient and differentially-private distributed SGD
Distributed stochastic gradient descent is an important subroutine in di...
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05/21/2018
Effective Dimension of Exp-concave Optimization
We investigate the role of the effective (a.k.a. statistical) dimension ...
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11/22/2017
Leverage Score Sampling for Faster Accelerated Regression and ERM
Given a matrix A∈R^n× d and a vector b ∈R^d, we show how to compute an ϵ...
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10/27/2017
Lower Bounds for Higher-Order Convex Optimization
State-of-the-art methods in convex and non-convex optimization employ hi...
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01/27/2017
The Price of Differential Privacy For Online Learning
We design differentially private algorithms for the problem of online li...
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11/03/2016
Finding Approximate Local Minima Faster than Gradient Descent
We design a non-convex second-order optimization algorithm that is guara...
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02/12/2016
Second-Order Stochastic Optimization for Machine Learning in Linear Time
First-order stochastic methods are the state-of-the-art in large-scale m...
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07/08/2015
