The Ladder: A Reliable Leaderboard for Machine Learning Competitions

TL;DR

This paper introduces 'the Ladder,' a new algorithm for maintaining a reliable leaderboard in machine learning competitions, offering strong theoretical guarantees, robustness against attacks, and practical deployment without tuning.

Contribution

The paper presents the Ladder algorithm, providing the first strong theoretical guarantees for adaptive leaderboard accuracy in machine learning competitions.

Findings

Supports strong theoretical guarantees in adaptive settings

Resists practical adversarial attacks

Achieves high utility on real Kaggle competition data

Abstract

The organizer of a machine learning competition faces the problem of maintaining an accurate leaderboard that faithfully represents the quality of the best submission of each competing team. What makes this estimation problem particularly challenging is its sequential and adaptive nature. As participants are allowed to repeatedly evaluate their submissions on the leaderboard, they may begin to overfit to the holdout data that supports the leaderboard. Few theoretical results give actionable advice on how to design a reliable leaderboard. Existing approaches therefore often resort to poorly understood heuristics such as limiting the bit precision of answers and the rate of re-submission. In this work, we introduce a notion of "leaderboard accuracy" tailored to the format of a competition. We introduce a natural algorithm called "the Ladder" and demonstrate that it simultaneously supports strong theoretical guarantees in a fully adaptive model of estimation, withstands practical adversarial attacks, and achieves high utility on real submission files from an actual competition hosted by Kaggle. Notably, we are able to sidestep a powerful recent hardness result for adaptive risk estimation that rules out algorithms such as ours under a seemingly very similar notion of accuracy. On a practical note, we provide a completely parameter-free variant of our algorithm that can be deployed in a real competition with no tuning required whatsoever.