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10/31/2022
Computing Rule-Based Explanations by Leveraging Counterfactuals
Sophisticated machine models are increasingly used for high-stakes decis...
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10/12/2022
Optimizing Tensor Programs on Flexible Storage
Tensor programs often need to process large tensors (vectors, matrices, ...
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01/05/2021
GeCo: Quality Counterfactual Explanations in Real Time
Machine learning is increasingly applied in high-stakes decision making ...
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09/18/2020
On the Tractability of SHAP Explanations
SHAP explanations are a popular feature-attribution mechanism for explai...
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08/19/2020
LMFAO: An Engine for Batches of Group-By Aggregates
LMFAO is an in-memory optimization and execution engine for large batche...
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03/15/2020
Causality-based Explanation of Classification Outcomes
We propose a simple definition of an explanation for the outcome of a cl...
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01/10/2020
Multi-layer Optimizations for End-to-End Data Analytics
We consider the problem of training machine learning models over multi-r...
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11/15/2019
Learning Models over Relational Data: A Brief Tutorial
This tutorial overviews the state of the art in learning models over rel...
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10/11/2019
Rk-means: Fast Clustering for Relational Data
Conventional machine learning algorithms cannot be applied until a data ...
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06/20/2019
A Layered Aggregate Engine for Analytics Workloads
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optim...
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12/22/2018
On Functional Aggregate Queries with Additive Inequalities
Motivated by fundamental applications in databases and relational machin...
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03/20/2018