Search Results for author: Dan Suciu

Found 17 papers, 3 papers with code

GeCo: Quality Counterfactual Explanations in Real Time

1 code implementation5 Jan 2021 Maximilian Schleich, Zixuan Geng, Yihong Zhang, Dan Suciu

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.

Decision Making

On the Tractability of SHAP Explanations

no code implementations18 Sep 2020 Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu

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

no code implementations7 Apr 2020 Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.

Causal Inference Decision Making +1

Causality-based Explanation of Classification Outcomes

no code implementations15 Mar 2020 Leopoldo Bertossi, Jordan Li, Maximilian Schleich, Dan Suciu, Zografoula Vagena

We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.

Classification General Classification

Mosaic: A Sample-Based Database System for Open World Query Processing

no code implementations17 Dec 2019 Laurel Orr, Samuel Ainsworth, Walter Cai, Kevin Jamieson, Magda Balazinska, Dan Suciu

Recently, with the increase in the number of public data repositories, sample data has become easier to access.

Data Management for Causal Algorithmic Fairness

no code implementations20 Aug 2019 Babak Salimi, Bill Howe, Dan Suciu

Fairness is increasingly recognized as a critical component of machine learning systems.


Capuchin: Causal Database Repair for Algorithmic Fairness

no code implementations21 Feb 2019 Babak Salimi, Luke Rodriguez, Bill Howe, Dan Suciu

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

1 code implementation21 Mar 2017 Dylan Hutchison, Bill Howe, Dan Suciu

Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA).


ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data

no code implementations12 Sep 2016 Babak Salimi, Dan Suciu

In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL.

Causal Inference

Lara: A Key-Value Algebra underlying Arrays and Relations

1 code implementation12 Apr 2016 Dylan Hutchison, Bill Howe, Dan Suciu

Data processing systems roughly group into families such as relational, array, graph, and key-value.

Databases Programming Languages

Symmetric Weighted First-Order Model Counting

no code implementations3 Dec 2014 Paul Beame, Guy Van Den Broeck, Eric Gribkoff, Dan Suciu

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

no code implementations2 Dec 2014 Wolfgang Gatterbauer, Dan Suciu

This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases.

Oblivious Bounds on the Probability of Boolean Functions

no code implementations21 Sep 2014 Wolfgang Gatterbauer, Dan Suciu

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

no code implementations13 May 2014 Eric Gribkoff, Guy Van Den Broeck, Dan Suciu

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).

Relational Reasoning

Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems

no code implementations23 Oct 2013 Wolfgang Gatterbauer, Dan Suciu

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

no code implementations26 Sep 2013 Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu

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

no code implementations NeurIPS 2010 Abhay Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu

Lifted inference algorithms for representations that combine first-order logic and probabilistic graphical models have been the focus of much recent research.

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