210 research outputs found
Interpolation Properties and SAT-based Model Checking
Craig interpolation is a widespread method in verification, with important
applications such as Predicate Abstraction, CounterExample Guided Abstraction
Refinement and Lazy Abstraction With Interpolants. Most state-of-the-art model
checking techniques based on interpolation require collections of interpolants
to satisfy particular properties, to which we refer as "collectives"; they do
not hold in general for all interpolation systems and have to be established
for each particular system and verification environment. Nevertheless, no
systematic approach exists that correlates the individual interpolation systems
and compares the necessary collectives. This paper proposes a uniform
framework, which encompasses (and generalizes) the most common collectives
exploited in verification. We use it for a systematic study of the collectives
and of the constraints they pose on propositional interpolation systems used in
SAT-based model checking
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias
We investigate how shallow ReLU networks interpolate between known regions.
Our analysis shows that empirical risk minimizers converge to a minimum norm
interpolant as the number of data points and parameters tends to infinity when
a weight decay regularizer is penalized with a coefficient which vanishes at a
precise rate as the network width and the number of data points grow. With and
without explicit regularization, we numerically study the implicit bias of
common optimization algorithms towards known minimum norm interpolants
Scalable Synthesis and Verification: Towards Reliable Autonomy
We have seen the growing deployment of autonomous systems in our daily life, ranging from safety-critical self-driving cars to dialogue agents. While impactful and impressive, these systems do not often come with guarantees and are not rigorously evaluated for failure cases. This is in part due to the limited scalability of tools available for designing correct-by-construction systems, or verifying them posthoc. Another key limitation is the lack of availability of models for the complex environments with which autonomous systems often have to interact with. In the direction of overcoming these above mentioned bottlenecks to designing reliable autonomous systems, this thesis makes contributions along three fronts.
First, we develop an approach for parallelized synthesis from linear-time temporal logic Specifications corresponding to the generalized reactivity (1) fragment. We begin by identifying a special case corresponding to singleton liveness goals that allows for a decomposition of the synthesis problem, which facilitates parallelized synthesis. Based on the intuition from this special case, we propose a more generalized approach for parallelized synthesis that relies on identifying equicontrollable states.
Second, we consider learning-based approaches to enable verification at scale for complex systems, and for autonomous systems that interact with black-box environments. For the former, we propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems. For the latter, we present a data-driven approach based on chance-constrained optimization that allows for a system to be evaluated for specification conformance without an accurate model of the environment. We demonstrate this approach on several tasks, including a lane-change scenario with real-world driving data.
Lastly, we consider the problem of interpreting and verifying learning-based components such as neural networks. We introduce a new method based on Craig's interpolants for computing compact symbolic abstractions of pre-images for neural networks. Our approach relies on iteratively computing approximations that provably overapproximate and underapproximate the pre-images at all layers. Further, building on existing work for training neural networks for verifiability in the classification setting, we propose extensions that allow us to generalize the approach to more general architectures and temporal specifications.</p
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
We investigate conditions under which test statistics exist that can reliably
detect examples, which have been adversarially manipulated in a white-box
attack. These statistics can be easily computed and calibrated by randomly
corrupting inputs. They exploit certain anomalies that adversarial attacks
introduce, in particular if they follow the paradigm of choosing perturbations
optimally under p-norm constraints. Access to the log-odds is the only
requirement to defend models. We justify our approach empirically, but also
provide conditions under which detectability via the suggested test statistics
is guaranteed to be effective. In our experiments, we show that it is even
possible to correct test time predictions for adversarial attacks with high
accuracy
Inverse Abstraction of Neural Networks Using Symbolic Interpolation
Neural networks in real-world applications have to satisfy critical properties such as safety and reliability. The analysis of such properties typically requires extracting information through computing pre-images of the network transformations, but it is well-known that explicit computation of pre-images is intractable. We introduce new methods for computing compact symbolic abstractions of pre-images by computing their overapproximations and underapproximations through all layers. The abstraction of pre-images enables formal analysis and knowledge extraction without affecting standard learning algorithms. We use inverse abstractions to automatically extract simple control laws and compact representations for pre-images corresponding to unsafe outputs. We illustrate that the extracted abstractions are interpretable and can be used for analyzing complex properties
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