# Fast rates in structured prediction

@inproceedings{Cabannes2021FastRI, title={Fast rates in structured prediction}, author={Vivien A. Cabannes and Alessandro Rudi and Francis R. Bach}, booktitle={Annual Conference Computational Learning Theory}, year={2021} }

Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, between estimate and solution, by the surrogate error endows discrete problems with convergence rates already shown for continuous instances. Yet, current approaches do not leverage the fact that discrete problems are essentially predicting a discrete output when continuous problems are predicting a continuous value. In…

## 11 Citations

### Towards Sharper Generalization Bounds for Structured Prediction

- Computer ScienceNeurIPS
- 2021

This paper investigates the generalization performance of structured prediction learning and obtains state-of-the-art generalization bounds from three different perspectives: Lipschitz continuity, smoothness, and space capacity condition.

### Disambiguation of Weak Supervision leading to Exponential Convergence rates

- Computer ScienceICML
- 2021

This paper focuses on partial labelling, an instance of weak supervision where, from a given input, the authors are given a set of potential targets, and proposes an empirical disambiguation algorithm to recover full supervision from weak supervision.

### Disambiguation of weak supervision with exponential convergence rates

- Computer ScienceArXiv
- 2021

This paper focuses on partial labelling, an instance of weak supervision where, from a given input, the authors are given a set of potential targets and proposes an empirical disambiguation algorithm to recover full supervision from weak supervision.

### Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression

- Materials Science, Computer ScienceAsian Journal of Civil Engineering
- 2022

In this study, supervised learning and neural networks were applied to predict the compressive strength of concrete mixes with GGBFS and fly ash. Three models: Multilayer perceptron network (MLP),…

### Active Labeling: Streaming Stochastic Gradients

- Computer ScienceArXiv
- 2022

After formalizing the “active labeling” problem, which focuses on active learning with partial supervision, this paper provides a streaming technique that provably minimizes the ratio of generalization error over the number of samples.

### A Case of Exponential Convergence Rates for SVM

- Computer ScienceArXiv
- 2022

A simple mechanism to obtain fast convergence rates and its usage for SVM is presented and it is shown that SVM can exhibit exponential convergence rates even without assuming the hard Tsybakov margin condition.

### Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes

- Mathematics, Computer Science
- 2022

It is demonstrated how these entropy bounds can be used to show the uniform law of large numbers and asymptotic equicontinuity of the function classes, and also apply it to statistical learning theory in which the output space is a Hilbert space.

### Multiclass learning with margin: exponential rates with no bias-variance trade-off

- Computer ScienceICML
- 2022

For a wide variety of methods it is proved that the classiﬁcation error under a hard-margin condition decreases exponentially fast without any bias-variance trade-off.

### Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification

- Computer ScienceArXiv
- 2022

This study offers a unified robust framework that includes a broad variety of linear prediction problems on a Hilbert space, coupled with a generic class of loss functions, and shows that this rate can be improved to achieve so-called “fast rates” under additional assumptions.

### Machine classification for probe-based quantum thermometry

- Computer SciencePhysical Review A
- 2022

This work considers the problem of probe-based quantum thermometry, and shows that machine classification can provide reliable estimates over a broad range of scenarios, based on the k-nearest-neighbor algorithm, and argues that classification may become an experimentally relevant tool for thermometry in the quantum regime.

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