no code implementations • 14 Aug 2024 • Jan Luca Scheerer, Anton Lykov, Moe Kayali, Ilias Fountalis, Dan Olteanu, Nikolaos Vasiloglou, Dan Suciu

The input question is mapped to IR using LLMs, which is then repaired into a valid relational database query with the aid of a semantic search on vector embeddings.

1 code implementation • 16 Jun 2023 • Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, Dan Suciu

On all three tasks, we show that a foundation-model-based approach outperforms the task-specific models and so the state of the art.

no code implementations • 31 Oct 2022 • Zixuan Geng, Maximilian Schleich, Dan Suciu

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.

1 code implementation • 5 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.

no code implementations • 18 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.

no code implementations • 7 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.

no code implementations • 15 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.

no code implementations • 17 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.

no code implementations • 20 Aug 2019 • Babak Salimi, Bill Howe, Dan Suciu

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

no code implementations • 21 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.

1 code implementation • 21 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).

Databases

no code implementations • 12 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.

1 code implementation • 12 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

no code implementations • 3 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.

no code implementations • 2 Dec 2014 • Wolfgang Gatterbauer, Dan Suciu

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

no code implementations • 21 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.

no code implementations • 13 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).

no code implementations • 23 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.

no code implementations • 26 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.

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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