When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
In collaboration with Boston University
authors Gavin R Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar
This paper was accepted at the Theory and Practice of Differential Privacy workshop at the ICML 2021 conference.
Modern machine learning models are complex and frequently encode surprising amounts of information about individual inputs. In extreme cases, complex models appear to memorize entire input examples, including seemingly irrelevant information (social security numbers from text, for example). In this paper, we aim to understand whether this sort of memorization is necessary for accurate learning. We describe natural prediction problems in which every sufficiently accurate training algorithm must encode, in the prediction model, essentially all the information about a large subset of its training examples. This remains true even when the examples are high-dimensional and have entropy much higher than the sample size, and even when most of that information is ultimately irrelevant to the task at hand. Further, our results do not depend on the training algorithm or the class of models used for learning.
Our problems are simple and fairly natural variants of the next-symbol prediction and the cluster labeling tasks. These tasks can be seen as abstractions of image- and text-related prediction problems. To establish our results, we reduce from a family of one-way communication problems for which we prove new information complexity lower bounds.
Apple sponsored the Neural Information Processing Systems (NeurIPS) conference, which was held virtually from December 6 to 12. NeurIPS is a global conference focused on fostering the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects.