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Found 19 papers, 4 papers with code
CHORUS: Foundation Models for Unified Data Discovery and Exploration
On all three tasks, we show that a foundation-model-based approach outperforms the task-specific models and so the state of the art.
Computing Rule-Based Explanations by Leveraging Counterfactuals
We prove a Duality Theorem, showing that rule-based and counterfactual-based explanations are dual to each other, then use this observation to develop an efficient algorithm for computing rule-based explanations, which uses the counterfactual-based explanation as an oracle.
GeCo: Quality Counterfactual Explanations in Real Time
Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions.
On the Tractability of SHAP Explanations
First, we consider fully-factorized data distributions, and show that the complexity of computing the SHAP explanation is the same as the complexity of computing the expected value of the model.
Causal Relational Learning
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
Causality-based Explanation of Classification Outcomes
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.
Mosaic: A Sample-Based Database System for Open World Query Processing
Recently, with the increase in the number of public data repositories, sample data has become easier to access.
Data Management for Causal Algorithmic Fairness
Fairness is increasingly recognized as a critical component of machine learning systems.
Capuchin: Causal Database Repair for Algorithmic Fairness
However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem.
LaraDB: A Minimalist Kernel for Linear and Relational Algebra Computation
Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA).
Databases
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data
In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL.
Lara: A Key-Value Algebra underlying Arrays and Relations
Data processing systems roughly group into families such as relational, array, graph, and key-value.
Databases Programming Languages
Symmetric Weighted First-Order Model Counting
For the combined complexity, we prove that, for every fragment FO$^{k}$, $k\geq 2$, the combined complexity of FOMC (or WFOMC) is #P-complete.
Approximate Lifted Inference with Probabilistic Databases
This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases.
Oblivious Bounds on the Probability of Boolean Functions
By performing several dissociations, one can transform a Boolean formula whose probability is difficult to compute, into one whose probability is easy to compute, and which is guaranteed to provide an upper or lower bound on the probability of the original formula by choosing appropriate probabilities for the dissociated variables.
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC), which counts the assignments that satisfy a given sentence in first-order logic (FOL); it has applications in Statistical Relational Learning (SRL) and Probabilistic Databases (PDB).
Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems
We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers.
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
The best current methods for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas can be seen, either directly or indirectly, as building 'decision-DNNF' (decision decomposable negation normal form) representations of the input Boolean formulas.
Lifted Inference Seen from the Other Side : The Tractable Features
Lifted inference algorithms for representations that combine first-order logic and probabilistic graphical models have been the focus of much recent research.
