LAVA: Data Valuation without Pre-Specified Learning Algorithms

This repository is the official implementation of the "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR 2023).

We propose LAVA: a novel model-agnostic framework to data valuation using a non-conventional, class-wise Wasserstein discrepancy. We further introduce an efficient way to measure datapoint contribution at no cost from the optimization solution.

Limitations of traditional data valuation methods

Traditional data valuation methods assume knowledge of the underlying learning algorithm.

• Learning algorithm is unknown prior to valuation
• Stochastic training process => Unstable values
• Model training => Computational burden

Data Valuation via Optimal Transport

We propose data valuation via optimal transport to replace the current data valuation frameworks which rely on the underlying learning algorithm.

Strong analytical properties of OT:

• well-defined distance metric
• computationally tractable
• computable from finite samples

LAVA: Individual Datapoint Valuation

To compute individual datapoint valuation, we propose the notion calibrated gradient, which measures sensitivity of the data point to the dataset distance by shifting the probability mass of the datapoint in the dual OT formulation.

$$Value(z_i) = \frac{\partial\text{OT}(\mu_t,\mu_v)}{\partial\mu_t(z_i)} = f_{i}^* -\sum_{j\in{1, ... N} \setminus i} \frac{f^*_j}{N-1}$$

• Exactly the gradient of the dual formulation
• Obtained for free when solving original OT problem

Applications

LAVA can be applied to numerous data quality applications:

• Mislabeled Data
• Noisy Features
• Dataset Redundancy
• Dataset Bias
• Irrelevant Data
• and more.

Requirements

Install a virtual environment (conda).

conda env create -f environment.yaml python=3.8

Getting Started

Load data package.

import lava

Create a corrupted dataset and the index list of corrupted data points or create your own.

loaders, shuffle_ind = lava.load_data_corrupted(corrupt_type='shuffle', dataname='CIFAR10', resize=resize, training_size=training_size, test_size=valid_size, currupt_por=portion)

Load a feature embedder.

feature_extractor = lava.load_pretrained_feature_extractor('cifar10_embedder_preact_resnet18.pth', device)

Compute the Dual Solution of the Optimal Transport problem.

dual_sol, trained_with_flag = lava.compute_dual(feature_extractor, loaders['train'], loaders['test'], training_size, shuffle_ind, resize=resize)

Compute the Data Values with LAVA + visualization.

calibrated_gradient = lava.compute_values_and_visualize(dual_sol, trained_with_flag, training_size, portion)

Examples

For better understanding of applying LAVA to data valuation, we have provided examples on CIFAR-10 and STL-10.

Checkpoints

The pretrained embedders are included in the folder 'checkpoint'.

Optimal Transport Solver

This repo relies on the OTDD implementation to compute the class-wise Wasserstein distance.
We are immensely grateful to the authors of that project.