arXiv subjects

Mohammad Afzal

Publications and source records attributed to Mohammad Afzal.

3 recordsLinked to original sources

arXiv2026

Formal Reasoning About Confidence and Automated Verification of Neural Networks

In the last decade, a large body of work has emerged on robustness of neural networks, i.e., checking if the decision remains unchanged when the input is slightly perturbed. However, most of these approaches ignore the confidence of a neural network on its output. In this work, we aim to develop a generalized framework for formally reasoning about the confidence along with robustness in neural networks. We propose a simple yet expressive grammar that captures various confidence-based specifications. We develop a novel and unified technique to verify all instances of the grammar in a homogeneous way, viz., by adding a few additional layers to the neural network, which enables the use any state-of-the-art neural network verification tool. We perform an extensive experimental evaluation over a large suite of 8870 benchmarks, where the largest network has 138M parameters, and show that this outperforms ad-hoc encoding approaches by a significant margin.

cs.LO↗

arXiv2023

LTL-Based Non-Markovian Inverse Reinforcement Learning

The successes of reinforcement learning in recent years are underpinned by the characterization of suitable reward functions. However, in settings where such rewards are non-intuitive, difficult to define, or otherwise error-prone in their definition, it is useful to instead learn the reward signal from expert demonstrations. This is the crux of inverse reinforcement learning (IRL). While eliciting learning requirements in the form of scalar reward signals has been shown to effective, such representations lack explainability and lead to opaque learning. We aim to mitigate this situation by presenting a novel IRL method for eliciting declarative learning requirements in the form of a popular formal logic -- Linear Temporal Logic (LTL) -- from a set of traces given by the expert policy. A key novelty of the proposed approach is quantitative semantics of satisfaction of an LTL formula by a word that, following Occam's razor principle, incentivizes simpler explanations. Given a sample $S=(P,N)$ consisting of positive traces $P$ and negative traces $N$, the proposed algorithms automate the search for a formula $φ$ which provides the simplest explanation (in the $GF$ fragment of LTL) of the samples. We have implemented this approach as an open-source tool QuantLearn to perform logic-based non-Markovian IRL. Our results demonstrate the feasibility of the proposed approach in eliciting intuitive LTL-based reward signals from noisy data.

cs.FL↗

arXiv2022

Efficiently Finding Adversarial Examples with DNN Preprocessing

Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for machines to carry out. In doing so, they do a lot of decision making which, depending on the application, may be disastrous if gone wrong. This necessitates a formal argument that the underlying neural networks satisfy certain desirable properties. Robustness is one such key property for DNNs, particularly if they are being deployed in safety or business critical applications. Informally speaking, a DNN is not robust if very small changes to its input may affect the output in a considerable way (e.g. changes the classification for that input). The task of finding an adversarial example is to demonstrate this lack of robustness, whenever applicable. While this is doable with the help of constrained optimization techniques, scalability becomes a challenge due to large-sized networks. This paper proposes the use of information gathered by preprocessing the DNN to heavily simplify the optimization problem. Our experiments substantiate that this is effective, and does significantly better than the state-of-the-art.

cs.LG↗