We study regret minimization in online episodic linear Markov Decision
P...
We study the generalization properties of unregularized gradient methods...
We consider online learning problems in the realizable setting, where th...
We study Stochastic Gradient Descent with AdaGrad stepsizes: a popular
a...
We study reinforcement learning with linear function approximation and
a...
Online prediction from experts is a fundamental problem in machine learn...
An abundance of recent impossibility results establish that regret
minim...
We consider the problem of designing uniformly stable first-order
optimi...
We study best-of-both-worlds algorithms for bandits with switching cost,...
We consider the problem of controlling an unknown linear dynamical syste...
We consider the problem of controlling an unknown linear dynamical syste...
An influential line of recent work has focused on the generalization
pro...
We study to what extent may stochastic gradient descent (SGD) be underst...
We study the online learning with feedback graphs framework introduced b...
We study the generalization performance of full-batch optimization
algor...
We study online convex optimization in the random order model, recently
...
We consider stochastic optimization with delayed gradients where, at eac...
We study the stochastic Multi-Armed Bandit (MAB) problem with random del...
Stochastic convex optimization over an ℓ_1-bounded domain is ubiquitous
...
We consider the task of learning to control a linear dynamical system un...
Deep neural networks are widespread due to their powerful performance. Y...
We study a variant of online convex optimization where the player is
per...
We study the algorithmic stability of Nesterov's accelerated gradient me...
We give a new separation result between the generalization performance o...
We study a novel variant of online finite-horizon Markov Decision Proces...
We introduce the problem of regret minimization in Adversarial Dueling
B...
State-of-the-art optimization is steadily shifting towards massively par...
This work presents a new distributed Byzantine tolerant federated learni...
We consider the problem of controlling a known linear dynamical system u...
We study differentially private (DP) algorithms for stochastic convex
op...
The notion of implicit bias, or implicit regularization, has been sugges...
We investigate several confounding factors in the evaluation of optimiza...
We revisit the fundamental problem of prediction with expert advice, in ...
Optimization in machine learning, both theoretical and applied, is prese...
We consider the problem of learning in Linear Quadratic Control systems ...
We introduce a temperature into the exponential function and replace the...
We consider convex SGD updates with a block-cyclic structure, i.e. where...
We study the stochastic multi-armed bandits problem in the presence of
a...
We present the first computationally-efficient algorithm with
O(√(T)) r...
Adaptive gradient-based optimizers such as AdaGrad and Adam are among th...
We study the problem of controlling linear time-invariant systems with k...
Preconditioned gradient methods are among the most general and powerful ...
We describe a framework for deriving and analyzing online optimization
a...
We study an online learning framework introduced by Mannor and Shamir (2...
We consider the most common variants of linear regression, including Rid...