## Motivation

**TL;DR: The major motivation behind this method is: they use Jackknife to estimate the distribution of error, then it use a threshold to find the upper- and lower-bound of the error in the distribution, which is used for quantifying the uncertainty.**

Quantifying the uncertainty over the predictions of existing deep learning models remains a challenging problem.

Deep learning models are increasingly popular in various application domains. A key question often asked of such model is “Can we trust this particular model prediction?” This is highly relevant applications wherein predictions are used to inform critical decision-making.

Existing methods for uncertainty estimation are based on Bayesian neural network. They do not guarantee (1) *cover* the true prediction targets with high probability (2) *discriminate* between high- and low-confidence prediction.

**Frequentist coverage**: denotes whether the estimated confidence interval cover the data points.**Discrimination**: denotes whether the model is able to discriminate

high-confidence predictions (regions with dense training data) and low-confidence ones (regions with scarce training data).

Existing methods for uncertainty estimation are based predominantly on Bayesian neural networks.

- Bayesian neural networks require significant modifications to the training procedure.
- Approximate the posterior distributions could jeopardize both the coverage and discrimination performance of the resulting credible intervals.

## Contributions

Propose the

*discriminative jackknife*(DJ) to estimate the uncertainty over samples inspired by the jackknife leave-one-out (LOO) re-sampling procedureTo avoid exhaustively re-training the model for each sample, they adopt the

*high-order influence function*to approximate the impact of each sample.DJ is post-hoc to the model training. It is capable of improving coverage and discrimination without any modifications to the underlying predictive model.

## Preliminaries

### Learning setup

Considering a standard supervised learning setup, we try to minimize the prediction loss on the training data $\mathcal{D}*n = {(x_i, y_i)}*{i=1}^n$.

### Uncertainty Quantification

**We aim to estimate the uncertainty in the model’s prediction though the pointwise confidence interval $\mathcal{C}(x;\hat{\theta})$**.

The degree of uncertainty in the model’s prediction is quantified by the *interval width*

#### Frequentist coverage

This is satisfied if the confidence interval $\mathcal{C}(x;\hat{\theta})$ covers the true target $y$ with a prespecified coverage probability of $(1-\alpha), \alpha \in (0,1)$.

#### discrimination

The confidence interval is wider for test points with less accurate predictions.

## Discriminative Jackknife

### Classical Jackknife

The jackknife quantifies predictive uncertainty in terms of the average prediction error, which is estimated via leave-one-out (LOO) construction found by systematically leaving out each sample in $\mathcal{D}_n$, and evaluating the error of the restrained model on the left-out samples.

For a target coverage of $(1-\alpha)$, the native jackknife is

$\mathcal{\hat Q}_\alpha^+(\mathcal{R})$: $(1-\alpha)(n+1)$-th smallest element of $\mathcal{R}$.

The interval width is constant, which renders discrimination impossible.

### DJ Confidence Intervals

The $\mathcal{G}_{\alpha,\gamma}$ is a quantile function applied on the elements of the sets of *marginal prediction errors $\mathcal{R}$* and *local prediction variability $\mathcal{V}$*.

The prediction error is constant, i.e., does not depend on $x$, hence it only contributes to coverage but does not contribute to discrimination.

The local variability term depends on $x$, hence it fully determines the discrimination performance.

The confidence interval is bounded by

### Efficient Implementation via Influence Functions

Approximate the $\hat\theta_i$ using the high-order influence function.

Influence functions enable efficient computation of the effect of a training data point $(x_i,y_i)$ on $\hat\theta$. This is achieved by evaluating the change in $\hat\theta$, if $(x_i,y_i)$ was up-weighted by a small factor $\epsilon$.

Removing a training point is equivalent to upweighting it by $\frac{-1}{n}$.