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...

[2602.10449] A Unified Theory of Random Projection for Influence Functions

This paper presents a unified theory of random projection for influence functions, addressing challenges in scalable influence computatio...

[2602.10388] Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs

The paper introduces a novel metric, Feature Activation Coverage (FAC), to measure data diversity in large language models (LLMs) and pre...

[2602.10234] Transforming Policy-Car Swerving for Mitigating Stop-and-Go Traffic Waves: A Practice-Oriented Jam-Absorption Driving Strategy

This article presents a novel driving strategy to mitigate stop-and-go traffic waves using a jam-absorption technique inspired by police-...

[2602.10168] EVA: Towards a universal model of the immune system

The paper introduces EVA, a universal multimodal foundation model for immunology that integrates diverse biological data to enhance drug ...

[2602.07298] Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation

This paper presents a novel framework for generating high-quality synthetic data to establish scaling laws for large language models (LLM...

[2602.05687] Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

This study investigates how AI, specifically large language models, can enhance the understanding and use of patient-generated health dat...

[2602.01308] Dispelling the Curse of Singularities in Neural Network Optimizations

This article explores the optimization instability in deep neural networks caused by singularities in the parametric space, proposing a m...

[2602.00737] Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization

This article presents a novel framework called Pareto-Conditioned Diffusion (PCD) for offline multi-objective optimization, addressing ch...

[2602.01157] Deep Time-Series Models Meet Volatility: Multi-Horizon Electricity Price Forecasting in the Australian National Electricity Market

This paper explores the effectiveness of deep time-series models for forecasting electricity prices in the volatile Australian National E...

[2601.21452] SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation

The paper presents SAGE, a new optimizer for generative recommendation systems that addresses limitations in existing methods by improvin...

[2601.12357] SimpleMatch: A Simple and Strong Baseline for Semantic Correspondence

The paper presents SimpleMatch, a novel framework for semantic correspondence that enhances performance at lower resolutions while reduci...

[2601.15673] Enhancing guidance for missing data in diffusion-based sequential recommendation

This paper presents the Counterfactual Attention Regulation Diffusion model (CARD) to improve sequential recommendation systems by addres...

[2601.09605] Sim2real Image Translation Enables Viewpoint-Robust Policies from Fixed-Camera Datasets

The paper presents MANGO, a novel image translation method that enhances viewpoint robustness in robot manipulation policies using fixed-...

[2601.07969] Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification

This paper presents a standardized framework for tuberculosis detection from cough audio, addressing inconsistencies in previous studies ...

[2510.26722] Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off

This paper explores the challenges of heterogeneous federated learning in wireless networks, focusing on the bias-variance trade-off in n...

[2509.14832] Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization

The paper presents a Diffusion Scenario Tree (DST) framework for multivariate time series prediction and multistage stochastic optimizati...

[2508.17742] EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation of EEG Foundation Models

The paper presents EEG-FM-Bench, a standardized benchmark for evaluating EEG foundation models, addressing inconsistencies in current eva...

[2507.16696] FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

FISHER is a proposed foundation model aimed at improving the analysis of multi-modal industrial signals, addressing the challenges posed ...

[2507.12108] Multimodal Coordinated Online Behavior: Trade-offs and Strategies

This paper explores multimodal coordinated online behavior, analyzing trade-offs between different integration strategies and their effec...

[2507.02310] Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment

This paper presents a novel framework for continual learning that addresses concept drift through Adaptive Memory Realignment (AMR), enha...