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Found 36 papers, 16 papers with code
Engineering an Exact Pseudo-Boolean Model Counter
Our work opens up several avenues for future work in the context of model counting for PB formulas, such as the development of preprocessing techniques and exploration of approaches other than knowledge compilation.
Locally-Minimal Probabilistic Explanations
Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML).
Auditable Algorithms for Approximate Model Counting
Since $n$ is often large, we ask if the count of variables in the certificate can be reduced -- a crucial question for potential implementation.
Total Variation Distance Estimation Is as Easy as Probabilistic Inference
In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models.
Scalable Probabilistic Routes
In this work, our contributions are two-fold: first, we introduce a relaxed encoding that uses a linear number of variables with respect to the number of vertices in a road network graph to significantly reduce the size of resultant decision diagrams.
Explaining SAT Solving Using Causal Reasoning
Subsequently, given a query such as whether a clause with low literals blocks distance (LBD) has a higher clause utility, CausalSAT calculates the causal effect of LBD on clause utility and provides an answer to the question.
Rounding Meets Approximate Model Counting
The problem of model counting, also known as #SAT, is to compute the number of models or satisfying assignments of a given Boolean formula $F$.
Synthesis with Explicit Dependencies
In QBF, an existentially quantified variable is allowed to depend on all universally quantified variables in its scope.
Fast Converging Anytime Model Counting
Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification.
How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis
In this paper, we aim to quantify the influence of different features in a dataset on the bias of a classifier.
Efficient Learning of Interpretable Classification Rules
The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable.
CCDD: A Tractable Representation for Model Counting and Uniform Sampling
Knowledge compilation concerns with the compilation of representation languages to target languages supporting a wide range of tractable operations arising from diverse areas of computer science.
Projected Model Counting: Beyond Independent Support
We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of the projection set, and yet counting models projected on UBS guarantees an upper bound of the true projected model count.
Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling
Given a Boolean formula $\varphi$ over the set of variables $X$ and a projection set $\mathcal{P} \subseteq X$, a subset of variables $\mathcal{I}$ is independent support of $\mathcal{P}$ if two solutions agree on $\mathcal{I}$, then they also agree on $\mathcal{P}$.
Algorithmic Fairness Verification with Graphical Models
In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of algorithms is of paramount importance.
Engineering an Efficient Boolean Functional Synthesis Engine
In this paper, we propose four algorithmic improvements for a data-driven framework for functional synthesis: using a dependency-driven multi-classifier to learn candidate function, extracting uniquely defined functions by interpolation, variables retention, and using lexicographic MaxSAT to repair candidates.
Program Synthesis as Dependency Quantified Formula Modulo Theory
Over the past decade, syntax-guided synthesis (SyGuS) has emerged as a dominant approach for program synthesis where in addition to the specification $\varphi$, the end-user also specifies a grammar $L$ to aid the underlying synthesis engine.
Predicting Forest Fire Using Remote Sensing Data And Machine Learning
The results support our claim that machine-learning based approaches can lead to reliable and cost-effective forest fire prediction systems.
Constraint-Driven Explanations of Black-Box ML Models
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.
Taming Discrete Integration via the Boon of Dimensionality
The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting.
Justicia: A Stochastic SAT Approach to Formally Verify Fairness
We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds.
Phase Transition Behavior in Knowledge Compilation
The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers.
Manthan: A Data Driven Approach for Boolean Function Synthesis
On an extensive and rigorous evaluation over 609 benchmarks, we demonstrate that Manthan significantly improves upon the current state of the art, solving 356 benchmarks in comparison to 280, which is the most solved by a state of the art technique; thereby, we demonstrate an increase of 76 benchmarks over the current state of the art.
Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice
In this paper, we address this challenge from theoretical and practical perspectives.
Scalable Quantitative Verification For Deep Neural Networks
But these existing techniques provide either scalability to large networks or formal guarantees, not both.
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions.
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice.
Phase Transition Behavior of Cardinality and XOR Constraints
The runtime performance of modern SAT solvers is deeply connected to the phase transition behavior of CNF formulas.
Embedding Symbolic Knowledge into Deep Networks
Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.
Quantitative Verification of Neural Networks And its Security Applications
Neural networks are increasingly employed in safety-critical domains.
MLIC: A MaxSAT-Based framework for learning interpretable classification rules
The wide adoption of machine learning approaches in the industry, government, medicine and science has renewed the interest in interpretable machine learning: many decisions are too important to be delegated to black-box techniques such as deep neural networks or kernel SVMs.
Constrained Counting and Sampling: Bridging the Gap between Theory and Practice
In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints.
On Hashing-Based Approaches to Approximate DNF-Counting
When the constraints are expressed as DNF formulas, Monte Carlo-based techniques have been shown to provide a fully polynomial randomized approximation scheme (FPRAS).
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification.
Approximate Probabilistic Inference via Word-Level Counting
Techniques based on bit-level (or Boolean) hash functions require these problems to be propositionalized, making it impossible to leverage the remarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can reason directly over words (or bit-vectors).
Distribution-Aware Sampling and Weighted Model Counting for SAT
We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems.
