Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models.This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate a capability for verifying large models. Finally, we show that some model configurations tend to be inherently more robust than others.

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Garbage collectors automatically free memory previously allocated by applications. Generally, they discard unreachable objects from memory, leaving reachable objects intact. However, object reachability does not necessarily imply usability, as an object may be obsolete and still reachable. Such objects are usually referred to as loitering objects. Loitering objects introduce a form of memory leak in a Java application. Predicting, tracing and eliminating loitering objects is a difficult problem. In this paper we address this problem. We present a self-healing approach for dealing with loitering objects. Specifically, the paper proposes an algorithm that can be integrated within the Java garbage collector. The algorithm prevents memory leaks resulting from loitering objects by "paging" suspected live objects to disk and reloading them if they are required. As a proof-of-concept, we have implemented and validated the algorithm for the Java Virtual Machine. This could be a first step towards genuine self-healing of memory management problems.