Data Science

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Scientists uncover new method to generate protein datasets for training AI

6 Months Using AI for Actual Work: What's Incredible, What's Overhyped, and What's Quietly Dangerous

Six months ago I committed to using AI tools for everything I possibly could in my work. Every day, every task, every workflow. Here's th...

[2411.04551] Measure-to-measure interpolation using Transformers

This article explores the use of Transformers as measure-to-measure maps, detailing their ability to process arbitrary input and target m...

[2409.16407] Towards Representation Learning for Weighting Problems in Design-Based Causal Inference

This article explores the use of representation learning to improve weighting methods in design-based causal inference, addressing challe...

[2407.03888] Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy

This paper explores continuous-time q-Learning in jump-diffusion models, utilizing Tsallis entropy to derive optimal policies and develop...

[2404.17592] Low-Rank Online Dynamic Assortment with Dual Contextual Information

This paper presents a low-rank dynamic assortment model that improves real-time personalized recommendations in e-commerce by utilizing d...

[2312.17111] Online Tensor Inference

The paper presents a novel framework for online tensor inference, addressing the challenges of real-time data processing in applications ...

[2108.02431] AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network

The paper presents AutoLL, a novel method for automatic linear layout of graphs using deep neural networks, enhancing the reordering of a...

[2602.11505] Calibrating an Imperfect Auxiliary Predictor for Unobserved No-Purchase Choice

This paper presents methods for calibrating biased auxiliary predictors to improve estimates of unobserved no-purchase choices in market ...

[2602.09127] Epistemic Throughput: Fundamental Limits of Attention-Constrained Inference

This paper explores the concept of 'epistemic throughput' in attention-constrained inference, analyzing how generative AI systems can man...

[2602.07744] Riemannian MeanFlow

The paper introduces Riemannian MeanFlow (RMF), a novel framework for generative modeling on Riemannian manifolds, significantly reducing...

[2602.05358] Bayesian Neighborhood Adaptation for Graph Neural Networks

This paper presents a Bayesian framework for adapting neighborhood scopes in Graph Neural Networks (GNNs), enhancing their performance in...

[2602.00099] Gauss-Newton Natural Gradient Descent for Shape Learning

This paper presents the Gauss-Newton method for optimization in shape learning, demonstrating faster convergence and improved accuracy ov...

[2601.20154] Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning

This paper explores the concept of spectral representation in self-supervised learning (SSL), aiming to unify various SSL methods and enh...

[2601.05383] Imitation Learning for Combinatorial Optimisation under Uncertainty

This paper explores imitation learning for combinatorial optimization under uncertainty, introducing a taxonomy of expert types and a new...

[2512.14908] ATLAS: Adaptive Topology-based Learning at Scale for Homophilic and Heterophilic Graphs

The paper introduces ATLAS, a novel framework for graph neural networks that enhances performance on both homophilic and heterophilic gra...

[2512.09654] Membership and Dataset Inference Attacks on Large Audio Generative Models

This paper explores membership and dataset inference attacks on large audio generative models, assessing their implications for copyright...

[2511.21537] Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns

This paper presents a framework for context-specific causal graph discovery that addresses non-stationarity and spatio-temporal patterns,...

[2512.06630] Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study

This study introduces a Quantum Temporal Convolutional Neural Network (QTCNN) for predicting equity returns, demonstrating its superiorit...

[2510.26510] LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection

This article explores the use of large language models (LLMs) as in-context meta-learners for model and hyperparameter selection in machi...

[2510.25926] Active Learning with Task-Driven Representations for Messy Pools

This article presents a novel approach to active learning by introducing task-driven representations that adapt during the learning proce...

[2510.19093] Weight Decay may matter more than muP for Learning Rate Transfer in Practice

This article investigates the role of weight decay versus the Maximal Update Parameterization (muP) in learning rate transfer for neural ...