Daniel Deutch
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View article: Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms
Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms Open
In this paper, we introduce a novel approach to computing the contribution of input tuples to the result of the query, quantified by the Banzhaf and Shapley values. In contrast to prior algorithmic work that focuses on Select-Project-Join-…
CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifiers Open
This work presents CaFA, a system for Cost-aware Feasible Attacks for assessing the robustness of neural tabular classifiers against adversarial examples realizable in the problem space, while minimizing adversaries' effort. To this end, C…
View article: Banzhaf Values for Facts in Query Answering
Banzhaf Values for Facts in Query Answering Open
Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measure of fact contribution, yet its efficient computation for …
View article: Banzhaf Values for Facts in Query Answering
Banzhaf Values for Facts in Query Answering Open
Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measure of fact contribution, yet its efficient computation for …
Answering Questions by Meta-Reasoning over Multiple Chains of Thought Open
Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through …
Answering Questions by Meta-Reasoning over Multiple Chains of Thought Open
Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through …
View article: FEDEX: An Explainability Framework for Data Exploration Steps
FEDEX: An Explainability Framework for Data Exploration Steps Open
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this paper a novel solution that assists data scientists in this l…
Weakly Supervised Text-to-SQL Parsing through Question Decomposition Open
Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries.I…
Computing the Shapley Value of Facts in Query Answering Open
The Shapley value is a game-theoretic notion for wealth distribution that is nowadays extensively used to explain complex data-intensive computation, for instance, in network analysis or machine learning. Recent theoretical works show that…
Weakly Supervised Text-to-SQL Parsing through Question Decomposition Open
Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. …
On Optimizing the Trade-off between Privacy and Utility in Data Provenance Open
Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed to analyze the data, i.e., their queries, may be proprieta…
On Optimizing the Trade-off between Privacy and Utility in Data\n Provenance Open
Organizations that collect and analyze data may wish or be mandated by\nregulation to justify and explain their analysis results. At the same time, the\nlogic that they have followed to analyze the data, i.e., their queries, may be\npropri…
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries Open
The algebraic approach for provenance tracking, originating in the semiring model of Green et. al, has proven useful as an abstract way of handling metadata. Commutative Semirings were shown to be the "correct" algebraic structure for Unio…
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning Open
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Recent work has proposed to leverage ideas from data provenance t…
On Multiple Semantics for Declarative Database Repairs Open
We study the problem of database repairs through a rule-based framework that\nwe refer to as Delta Rules. Delta Rules are highly expressive and allow\nspecifying complex, cross-relations repair logic associated with Denial\nConstraints, Ca…
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries Open
International audience
B<span>reak</span> It Down: A Question Understanding Benchmark Open
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR …
Break It Down: A Question Understanding Benchmark Open
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR …
View article: Explaining Queries Over Web Tables to Non-experts
Explaining Queries Over Web Tables to Non-experts Open
Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL quest…
View article: Just in Time: Personal Temporal Insights for Altering Model Decisions
Just in Time: Personal Temporal Insights for Altering Model Decisions Open
The interpretability of complex Machine Learning models is coming to be a\ncritical social concern, as they are increasingly used in human-related\ndecision-making processes such as resume filtering or loan applications.\nIndividuals recei…
Hypothetical Reasoning via Provenance Abstraction Open
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Previous work has shown that fine-grained data provenance can hel…
Possible and Certain Answers for Queries over Order-Incomplete Data Open
To combine and query ordered data from multiple sources, one needs to handle uncertainty about the possible orderings. Examples of such "order-incomplete" data include integrated event sequences such as log entries, lists of properties (e.…
View article: Data Citation
Data Citation Open
Data citation is an interesting computational challenge, whose solution draws on several well-studied problems in database theory: query answering using views, and provenance. We describe the problem, suggest an approach to its solution, a…
Query By Provenance Open
To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of examples have been proposed. While highly useful, a shortcoming of the approach is that if users can only provide…
View article: PROX: Approximated Summarization of Data Provenance
PROX: Approximated Summarization of Data Provenance Open
Many modern applications involve collecting large amounts of data from multiple sources, and then aggregating and manipulating it in intricate ways. The complexity of such applications, combined with the size of the collected data, makes i…