# Exponential Savings in Agnostic Active Learning Through Abstention

@article{Puchkin2022ExponentialSI, title={Exponential Savings in Agnostic Active Learning Through Abstention}, author={Nikita Puchkin and Nikita Zhivotovskiy}, journal={IEEE Transactions on Information Theory}, year={2022}, volume={68}, pages={4651-4665} }

We show that in pool-based active classification without assumptions on the underlying distribution, if the learner is given the power to abstain from some predictions by paying the price marginally smaller than the average loss 1/2 of a random guess, exponential savings in the number of label requests are possible whenever they are possible in the corresponding realizable problem. We extend this result to provide a necessary and sufficient condition for exponential savings in pool-based active…

## 5 Citations

### Efficient Active Learning with Abstention

- Computer ScienceArXiv
- 2022

The first computationally computationally active learning algorithm with abstention is developed, guaranteed to only abstain on hard examples (where the true label distribution is close to a fair coin), a novel property the authors term “proper abstention” that also leads to a host of other desirable characteristics.

### Classification with abstention but without disparities

- Computer ScienceUAI
- 2021

A general purpose classiﬁcation algorithm, which is able to abstain from prediction, while avoiding disparate impact, is built and it is shown that fairness and abstention constraints can be achieved independently from the initial classi ﬁer as long as sufﬂciently many unlabeled data is available.

### Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition

- Computer Science, MathematicsArXiv
- 2022

This work builds upon the recent approach to localization via oﬀset Rademacher complexities, for which a general high-probability theory has yet to be established and yields results at least as sharp as those obtainable via the classical theory.

### Active learning algorithm through the lens of rejection arguments

- Computer Science
- 2022

These experiments provide empirical evidence that the use of rejection arguments in the active learning algorithm is beneﬁcial and allows good performance in various statistical situations.

### A Regret-Variance Trade-Off in Online Learning

- Computer Science
- 2022

We consider prediction with expert advice for strongly convex and bounded losses, and investigate trade-offs between regret and “variance” (i.e., squared difference of learner’s predictions and best…

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