559 research outputs found
Abduction-Based Explanations for Machine Learning Models
The growing range of applications of Machine Learning (ML) in a multitude of
settings motivates the ability of computing small explanations for predictions
made. Small explanations are generally accepted as easier for human decision
makers to understand. Most earlier work on computing explanations is based on
heuristic approaches, providing no guarantees of quality, in terms of how close
such solutions are from cardinality- or subset-minimal explanations. This paper
develops a constraint-agnostic solution for computing explanations for any ML
model. The proposed solution exploits abductive reasoning, and imposes the
requirement that the ML model can be represented as sets of constraints using
some target constraint reasoning system for which the decision problem can be
answered with some oracle. The experimental results, obtained on well-known
datasets, validate the scalability of the proposed approach as well as the
quality of the computed solutions
A Unified View of Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A
comparative study
Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions
We present a technique for neural network verification using mixed-integer
programming (MIP) formulations. We derive a \emph{strong formulation} for each
neuron in a network using piecewise linear activation functions. Additionally,
as in general, these formulations may require an exponential number of
inequalities, we also derive a separation procedure that runs in super-linear
time in the input dimension. We first introduce and develop our technique on
the class of \emph{staircase} functions, which generalizes the ReLU, binarized,
and quantized activation functions. We then use results for staircase
activation functions to obtain a separation method for general piecewise linear
activation functions. Empirically, using our strong formulation and separation
technique, we can reduce the computational time in exact verification settings
based on MIP and improve the false negative rate for inexact verifiers relying
on the relaxation of the MIP formulation
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