no code implementations • 28 Mar 2024 • Ravi Mangal, Nina Narodytska, Divya Gopinath, Boyue Caroline Hu, Anirban Roy, Susmit Jha, Corina Pasareanu
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures.
1 code implementation • 7 Oct 2023 • Haoze Wu, Clark Barrett, Nina Narodytska
The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract reasoning about program properties that is challenging for verification tools.
no code implementations • 27 Feb 2023 • Sagar Patel, Sangeetha Abdu Jyothi, Nina Narodytska
We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems.
no code implementations • 24 Feb 2023 • Sagar Patel, Junyang Zhang, Sangeetha Abdu Jyothi, Nina Narodytska
First, we identify the critical features that determine the behavior of the traces.
no code implementations • 12 Dec 2022 • Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva
One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness.
1 code implementation • 20 Jun 2022 • Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Nina Narodytska, Joao Marques-Silva
It also means the "why not" explanations may be suspect as the counterexamples they rely on may not be meaningful.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 19 May 2022 • Yacine Izza, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva
The paper proposes two logic encodings for computing smallest {\delta}-relevant sets for DTs.
1 code implementation • 7 Mar 2022 • Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh
Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics.
1 code implementation • 28 Feb 2022 • Parikshit Gopalan, Nina Narodytska, Omer Reingold, Vatsal Sharan, Udi Wieder
Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory.
no code implementations • 1 Jun 2021 • Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska
This paper describes novel algorithms for the computation of one formal explanation of a (black-box) monotonic classifier.
no code implementations • 1 Jun 2021 • Yacine Izza, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva
Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input.
no code implementations • 1 Jan 2021 • Alexey Ignatiev, Nina Narodytska, Nicholas Asher, Joao Marques-Silva
Explanations of Machine Learning (ML) models often address a ‘Why?’ question.
no code implementations • 1 Jan 2021 • Aditya Aniruddha Shrotri, Nina Narodytska, Alexey Ignatiev, Joao Marques-Silva, Kuldeep S. Meel, Moshe Vardi
Modern machine learning techniques have enjoyed widespread success, but are plagued by lack of transparency in their decision making, which has led to the emergence of the field of explainable AI.
no code implementations • 21 Dec 2020 • Alexey Ignatiev, Nina Narodytska, Nicholas Asher, Joao Marques-Silva
and 'Why Not?'
no code implementations • NeurIPS 2020 • Joao Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska
In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers.
no code implementations • ICLR 2020 • Nina Narodytska, Hongce Zhang, Aarti Gupta, Toby Walsh
Analyzing the behavior of neural networks is one of the most pressing challenges in deep learning.
no code implementations • 14 Mar 2020 • Christian Bessiere, Clement Carbonnel, Anton Dries, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Nina Narodytska, Claude-Guy Quimper, Kostas Stergiou, Dimosthenis C. Tsouros, Toby Walsh
Learning constraint networks is known to require a number of membership queries exponential in the number of variables.
1 code implementation • NeurIPS 2019 • Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
The importance of explanations (XP's) of machine learning (ML) model predictions and of adversarial examples (AE's) cannot be overstated, with both arguably being essential for the practical success of ML in different settings.
1 code implementation • 6 Sep 2019 • Lalith Suresh, Joao Loff, Faria Kalim, Nina Narodytska, Leonid Ryzhyk, Sahan Gamage, Brian Oki, Zeeshan Lokhandwala, Mukesh Hira, Mooly Sagiv
Modern cluster management systems like Kubernetes and Openstack grapple with hard combinatorial optimization problems: load balancing, placement, scheduling, and configuration.
Distributed, Parallel, and Cluster Computing
1 code implementation • 4 Jul 2019 • Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions.
1 code implementation • ICLR 2019 • Weili Nie, Nina Narodytska, Ankit Patel
Generative adversarial networks (GANs) have achieved great success at generating realistic images.
Ranked #3 on Text Generation on EMNLP2017 WMT
1 code implementation • 26 Nov 2018 • Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.
no code implementations • 25 May 2018 • Francesco Leofante, Nina Narodytska, Luca Pulina, Armando Tacchella
Neural networks are one of the most investigated and widely used techniques in Machine Learning.
no code implementations • 24 Feb 2018 • Svyatoslav Korneev, Nina Narodytska, Luca Pulina, Armando Tacchella, Nikolaj Bjorner, Mooly Sagiv
To perform image generation we need to define a mapping from a porous medium to its physical process parameters.
no code implementations • 21 Oct 2017 • Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks.
no code implementations • 19 Sep 2017 • Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, Toby Walsh
To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.
no code implementations • 19 Dec 2016 • Nina Narodytska, Shiva Prasad Kasiviswanathan
In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network.
no code implementations • 28 May 2014 • Nina Narodytska, Toby Walsh
These methods modify scoring rules (like the Borda count), elimination style rules (like single transferable vote) and rules based on the tournament graph (like Copeland) respectively.
no code implementations • 16 Jan 2014 • Lucas Bordeaux, George Katsirelos, Nina Narodytska, Moshe Y. Vardi
An important question is therefore whether strongly-polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints.
no code implementations • 21 Jun 2013 • Nina Narodytska, Toby Walsh
We can break symmetry by eliminating solutions within each symmetry class.
no code implementations • 23 Apr 2013 • Nicholas Mattei, Nina Narodytska, Toby Walsh
Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage voting rule.
no code implementations • 22 Apr 2013 • Nina Narodytska, Thierry Petit, Mohamed Siala, Toby Walsh
The FOCUS constraint expresses the notion that solutions are concentrated.
no code implementations • 3 Apr 2013 • Serge Gaspers, Thomas Kalinowski, Nina Narodytska, Toby Walsh
Schulze's rule is used in the elections of a large number of organizations including Wikimedia and Debian.