README.md

Regularization properties of adversarially-trained linear regression

Companion code to the paper:

Regularization properties of adversarially-trained linear regression
Antônio H Ribeiro, Dave Zachariah, Francis Bach, Thomas B. Sch\"on
NeurIPS 2023 (spotlight)

Paper: arXiv:2310.10807

Other info: open review - video - poster - slides - NeurIPS poster page - summary tweet

Paper Abstract:

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the minimum-norm interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial training---as in square-root Lasso---the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples.

Description

Given pair of input-output samples $(x_i, y_i), i = 1, \dots, n$, adversarial training in linear regression is formulated as a min-max optimization problem:

$$\min_\beta \frac{1}{n} \sum_{i=1}^n \max_{||\Delta x_i|| \le \delta} (y_i - \beta^\top(x_i+ \Delta x_i))^2$$

The paper analyse the properties of this problem and this repository contain code for reproducing the experiements in the paper.

Jupyter notebooks

We provide jupiter notebooks with minimal examples that can be used to quickly reproduce some of the paper main results:

We try to keep these notebooks as simple as possible, removing some of the plot configurations or running the experiment only once instead of multiple times.

Figure	Jupyter Notebook	Colab
Fig. 1
Fig. 2
Fig. 3
Fig. 4

Requirements

We use cvxpy to solve adversarial training optimization problem. We also use standard scientific python packages numpy, scipy, scikit-learn, pandas, seaborn and matplotlib. See requirements.txt.

Implementing adversarial training

The script advtrain.py implement adversarial training using python. It uses the reformulation provided in Proposition 1 in the paper.

import cvxpy as cp
import numpy as np


def compute_q(p):
    if p != np.Inf and p > 1:
        q = p / (p - 1)
    elif p == 1:
        q = np.Inf
    else:
        q = 1
    return q


class AdversarialTraining:
    def __init__(self, X, y, p):
        m, n = X.shape
        q = compute_q(p)
        # Formulate problem
        param = cp.Variable(n)
        param_norm = cp.pnorm(param, p=q)
        adv_radius = cp.Parameter(name='adv_radius', nonneg=True)
        abs_error = cp.abs(X @ param - y)
        adv_loss = 1 / m * cp.sum((abs_error + adv_radius * param_norm) ** 2)
        prob = cp.Problem(cp.Minimize(adv_loss))
        self.prob = prob
        self.adv_radius = adv_radius
        self.param = param
        self.warm_start = False

    def __call__(self, adv_radius, **kwargs):
        try:
            self.adv_radius.value = adv_radius
            self.prob.solve(warm_start=self.warm_start, **kwargs)
            v = self.param.value
        except:
            v = np.zeros(self.param.shape)
        return v

Experiments

The folders minnorm, varying_regularization, random_projections, varying_regularization, comparing_methods, magic, misc contain the scripts for different experiment and for generating the figures in the paper

These allow to reproduce the figures in the paper exactly. The jupyter notebooks above might be easier to understand, since they contain simplified code.

• minnorm/: Evaluate minimum norm interpolator. Plot Fig. 2 and S4, S5, S6.
• regulariazation_paths/: Regularization paths. Plot Fig. 1, 3 and S1, S2, S3.
• varying_regularization/: Varying regularization strength. Plot Fig. 4 and S7, S8, S9.
• misc/: other scripts. Plot Fig. 5.
• random_projections/: Random projections. Plot Fig. S.10.
• magic/: Experiment with Magic dataset. Plot Fig. 6.
• Comparing methods (magic): Compare methods with and without cross-validation. Plot Fig. S.11

Inside some of this folder, we include:

• plots/: containing the pdf plots generated from the experiment.
• results/: containing results in csv format.

Running all experiments

You can use run.sh to generate all figures in the paper.

# Description: Script to run all experiments in the paper

### Download data ###
wget http://mtweb.cs.ucl.ac.uk/mus/www/MAGICdiverse/MAGIC_diverse_FILES/BASIC_GWAS.tar.gz
tar -xvf BASIC_GWAS.tar.gz

### Evaluate minimum norm interpolator ###
# ---- Plot Fig. 2 and S4, S5, S6 ---- #
( cd minnorm && sh run.sh )

###  regularization paths ###
# ---- Plot Fig. 1, 3 and S1, S2, S3 ---- #
( cd regularization_paths && sh run.sh )

### varying regularization strength  ###
# ---- Plot Fig. 4 and S7, S8, S9 ---- #
( cd varying_regularization && sh run.sh )

### Random projections ###
# ---- Plot Fig. S.10---- #
( cd random_projections && sh run.sh )

### Other ###
# ---- Plot Fig. 5 ---- #
( cd misc && sh run.sh )

### MAGIC ###
# ---- Plot Fig. 6 ---- #
( cd magic && sh run.sh )

### Comparing methods (magic) ###
# ---- Plot Fig. S.11---- #
( cd comparing_methods && sh run.sh )