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Fast rates in structured prediction.

Project: SEQUOIA

Updated at: 27-04-2024

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Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes

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On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

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Affine Invariant Covariance Estimation for Heavy-Tailed Distributions

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Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions

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Deep Equals Shallow for ReLU Networks in Kernel Regimes.

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On Lazy Training in Differentiable Programming

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Efficient first-order methods for convex minimization: a constructive approach

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On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

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Exponential convergence of testing error for stochastic gradient methods

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On Fast Leverage Score Sampling and Optimal Learning

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Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses

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An accelerated decentralized stochastic proximal algorithm for finite Sums

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Batch Normalization Orthogonalizes Representations in Deep Random Networks

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Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Esti...

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Batch Normalization Provably Avoids Rank Collapse for Randomly Initialised Deep Networks.

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A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation.

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Explicit Regularization of Stochastic Gradient Methods through Duality.

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Dual-Free Stochastic Decentralized Optimization with Variance Reduction.

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Non-parametric Models for Non-negative Functions.

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Periodic Reporting for period 3 - SEQUOIA (Robust algorithms for learning from modern data)

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Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations

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A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly conv...

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Finite-sample analysis of M-estimators using self-concordance

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On the oracle complexity of smooth strongly convex minimization

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On the Effectiveness of Richardson Extrapolation in Data Science.

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A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip

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Learning with Differentiable Perturbed Optimizers

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Relating Leverage Scores and Density using Regularized Christoffel Functions

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Resources

Selected filters:

Fast rates in structured prediction.

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes

On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Affine Invariant Covariance Estimation for Heavy-Tailed Distributions

Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions

Deep Equals Shallow for ReLU Networks in Kernel Regimes.

On Lazy Training in Differentiable Programming

Efficient first-order methods for convex minimization: a constructive approach

On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Exponential convergence of testing error for stochastic gradient methods

On Fast Leverage Score Sampling and Optimal Learning

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses

An accelerated decentralized stochastic proximal algorithm for finite Sums

Batch Normalization Orthogonalizes Representations in Deep Random Networks

Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Esti...

Batch Normalization Provably Avoids Rank Collapse for Randomly Initialised Deep Networks.

A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation.

Explicit Regularization of Stochastic Gradient Methods through Duality.

Dual-Free Stochastic Decentralized Optimization with Variance Reduction.

Non-parametric Models for Non-negative Functions.

Periodic Reporting for period 3 - SEQUOIA (Robust algorithms for learning from modern data)

Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations

A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly conv...

Finite-sample analysis of M-estimators using self-concordance

On the oracle complexity of smooth strongly convex minimization

On the Effectiveness of Richardson Extrapolation in Data Science.

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip

Learning with Differentiable Perturbed Optimizers

Relating Leverage Scores and Density using Regularized Christoffel Functions