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Found 15 papers, 2 papers with code
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia.
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier.
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset.
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness.
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Recent work has sought to train fair models without sensitive attributes on training data.
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
We further show that MinDiff optimization is very sensitive to choice of batch size in the under-parameterized regime.
Parameterized Explanations for Investor / Company Matching
We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task.
GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning
Similarly, social networks within universities and organizations may enable certain groups to more easily access people with valuable information or influence.
Inferring Network Structure From Data
Networks are complex models for underlying data in many application domains.
Privacy Shadow: Measuring Node Predictability and Privacy Over Time
The structure of network data enables simple predictive models to leverage local correlations between nodes to high accuracy on tasks such as attribute and link prediction.
Network Model Selection Using Task-Focused Minimum Description Length
Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i. e. node-level) predictive task of interest.
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities.
Evaluating Social Networks Using Task-Focused Network Inference
We present a general framework for evaluating the suitability of given networks for a set of predictive tasks of interest, compared against alternative, networks inferred from data.
Network Structure Inference, A Survey: Motivations, Methods, and Applications
Do two animals who physically co-locate have a social bond?
Coordination Event Detection and Initiator Identification in Time Series Data
Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species.
