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Found 20 papers, 9 papers with code
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2. 48\% in AUC and 22. 5\% in R@P$_{0. 9}$, outperforming state-of-the-art methods significantly.
CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics
Cohort studies are of significant importance in the field of healthcare analysis.
Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression
The adaptive nature of LITO counters the limitations of one-size-fits-all intervention methods, maximizing truthfulness by reflecting the model's internal knowledge only when it is confident.
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern.
Observatory: Characterizing Embeddings of Relational Tables
Based on these properties, we define an extensible framework to evaluate language and table embedding models.
Pylon: Semantic Table Union Search in Data Lakes
The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data.
Detection of Groups with Biased Representation in Ranking
We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation.
Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams
Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties.
CompactIE: Compact Facts in Open Information Extraction
Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1. 5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.
Ranked #3 on
Open Information Extraction
on BenchIE
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately.
ARM-Net: Adaptive Relation Modeling Network for Structured Data
The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
Duoquest: A Dual-Specification System for Expressive SQL Queries
We present results from user studies in which Duoquest demonstrates a 62. 5% absolute increase in query construction accuracy over a state-of-the-art NLI and comparable accuracy to a PBE system on a more limited workload supported by the PBE system.
Databases
Responsible Scoring Mechanisms Through Function Sampling
We provide unbiased samplers for the entire function space, as well as a $\theta$-vicinity around a given function.
Database Meets Deep Learning: Challenges and Opportunities
Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
Exploiting Structure in Representation of Named Entities using Active Learning
We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels.
PANDA: Facilitating Usable AI Development
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.
Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale
Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data".
