Research

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High Probability Risk Control Under Covariate Shift

Distribution-free uncertainty quantification is an emerging field, which encompasses risk control techniques in finite sample settings with minimal distributional assumptions, […]

Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot Tasks

AI models are increasingly used for data analysis and visualization, yet benchmarks rarely address scatterplot-specific tasks, limiting insight into performance. […]

Evaluating Transfer Learning Methods on Real-World Data Streams

When the available data for a target domain is limited, transfer learning (TL) methods leverage related data-rich source domains to […]

DigitalTraces: Unveiling Fraud Through Interactive User Behaviour Exploration

Fraud detection teams in financial institutions face the challenge of identifying suspicious activity within user behaviour. However, existing tools often […]

A Universe of Data

#aitechnology #aitech #analytics #responsibleai On February 14, 1990, Carl Sagan inspired the Voyager mission to capture an image of Earth […]

Mind the Truncation Gap: Challenges of Learning on Dynamic Graphs with Recurrent Architectures

Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). […]

DiConStruct: Causal Concept-based Explanations through Black-Box Distillation

Abstract: Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. […]

Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs

Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often […]

Responsible AI. Fair-OBNC: A Fairness Method For Label Noise

Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. […]

Interleaved Sequence RNNs for Fraud Detection (KDD’2020 Promotional Video)

Interleaved Sequence RNNs for Fraud Detection Authors: Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana S. C. Almeida, João Tiago […]

Finding Nemo: Fishing in Banking Networks Using Network Motifs

Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discover new […]

Railgun: Managing Large Streaming Windows Under MAD Requirements

Some mission-critical systems, e.g., fraud detection, require accurate, real-time metrics over long-time sliding windows on applications that demand high throughput […]

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