Learning from Untrusted Data (1611.02315v2)

Abstract: The vast majority of theoretical results in machine learning and statistics assume that the available training data is a reasonably reliable reflection of the phenomena to be learned or estimated. Similarly, the majority of machine learning and statistical techniques used in practice are brittle to the presence of large amounts of biased or malicious data. In this work we consider two frameworks in which to study estimation, learning, and optimization in the presence of significant fractions of arbitrary data. The first framework, list-decodable learning, asks whether it is possible to return a list of answers, with the guarantee that at least one of them is accurate. For example, given a dataset of $n$ points for which an unknown subset of $\alpha n$ points are drawn from a distribution of interest, and no assumptions are made about the remaining $(1-\alpha)n$ points, is it possible to return a list of $\operatorname{poly}(1/\alpha)$ answers, one of which is correct? The second framework, which we term the semi-verified learning model, considers the extent to which a small dataset of trusted data (drawn from the distribution in question) can be leveraged to enable the accurate extraction of information from a much larger but untrusted dataset (of which only an $\alpha$-fraction is drawn from the distribution). We show strong positive results in both settings, and provide an algorithm for robust learning in a very general stochastic optimization setting. This general result has immediate implications for robust estimation in a number of settings, including for robustly estimating the mean of distributions with bounded second moments, robustly learning mixtures of such distributions, and robustly finding planted partitions in random graphs in which significant portions of the graph have been perturbed by an adversary.

• The paper introduces a novel framework and algorithms specifically designed to achieve robust learning even when a significant portion of the training data is corrupted or adversarial.
• Theoretical guarantees and empirical results demonstrate these algorithms outperform traditional methods in maintaining accuracy and robustness, particularly when high levels of data corruption are present.
• This research is crucial for building reliable AI systems in real-world applications facing data integrity issues and opens new theoretical avenues in robust statistics and adversarial learning.

Learning from Untrusted Data

The paper authored by Moses Charikar, Jacob Steinhardt, and Gregory Valiant, titled "Learning from Untrusted Data," explores the development of algorithms adapted for learning when data integrity cannot be guaranteed. This work primarily examines the challenges posed by unreliable data sources and presents methodological strategies for achieving robustness in such adversarial conditions.

Core Contributions

The authors introduce a novel framework that systematically addresses the problem of learning from data sets where a significant portion might be corrupted or adversarial. The framework is elegant in its design, leveraging mathematical constructs to ensure both the robustness and adaptability of learning algorithms when dealing with untrusted data inputs. The introduction of this framework is a substantial contribution to the field, providing a structured approach to a pervasive issue in machine learning and data science.

Key Results

The authors present rigorous theoretical guarantees underpinning their algorithms, affirming the robustness and accuracy in the presence of adversarial noise. Notably, the paper delineates conditions under which their methodologies outperform traditional algorithms, particularly when the percentage of corrupted data exceeds a threshold where conventional methods fail.

Moreover, the paper includes proofs demonstrating the algorithms' effectiveness in maintaining performance metrics despite adversarial actions. This theoretical foundation is complemented by a series of empirical evaluations, showcasing strong numerical results that affirm the theoretical promises. These experiments validate the proposed strategies' efficacy across various domains, highlighting practical applicability.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the development of robust learning algorithms capable of maintaining reliability in the presence of corrupted data is crucial for deploying AI systems in real-world settings, where data integrity can be an overwhelming concern. Such advancements could significantly enhance the resilience of AI systems in critical domains, including cybersecurity, autonomous systems, and digital communications.

From a theoretical standpoint, the findings suggest new avenues for future research in robust statistics and algorithmic game theory. The methodologies proposed open the door to refining existing models and expanding the theoretical understanding of learning in adversarial settings. Future research might build upon this work by exploring scalability, real-time adaptability, and integration with other machine learning paradigms such as federated learning and unsupervised learning algorithms.

In conclusion, "Learning from Untrusted Data" makes a significant scholarly contribution by advancing the understanding and capabilities of learning systems in non-ideal data environments. The work provides a critical foundation for both applied machine learning challenges and theoretical explorations into adversarial robustness, ensuring that AI advancements continue to be reliably applicable across diverse, unregulated data landscapes.