Data Science

[P] ML project (XGBoost + Databricks + MLflow) — how to talk about “production issues” in interviews?

Hey all, I recently built an end-to-end fraud detection project using a large banking dataset: Trained an XGBoost model Used Databricks f...

Harvard opens more free online courses in AI, data science, programming: Check full list and direct links

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...

[2602.18548] 1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World

The paper introduces 1D-Bench, a benchmark for evaluating iterative UI code generation with visual feedback, aimed at improving design-to...

[2602.18540] Rodent-Bench

Rodent-Bench introduces a benchmark for evaluating Multimodal Large Language Models (MLLMs) in annotating rodent behavior videos, reveali...

[2602.19510] Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon

This paper explores the convergence benefits of fewer data weight updates in machine learning, demonstrating that optimal update strategi...

[2602.18535] Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS

This paper presents a novel framework for voice classification of Parkinson's and ALS using fairness-aware partial-label domain adaptatio...

[2602.19489] Federated Learning Playground

The article presents the Federated Learning Playground, an interactive platform designed to teach core concepts of Federated Learning thr...

[2602.19455] SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

The paper introduces SenTSR-Bench, a framework that enhances time-series reasoning by integrating insights from specialized time-series l...

[2602.19483] Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification

This article explores the application of conformal prediction methods in healthcare, specifically focusing on EEG seizure classification....

[2602.19419] RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs

The paper presents RAmmStein, a Deep Reinforcement Learning approach for optimal liquidity management in decentralized exchanges, focusin...

[2602.19444] PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories

The article presents PIS, a Physics-Informed System that enhances the state partitioning of $Aβ_{42}$ protein trajectories, crucial for u...

[2602.19414] Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning

This paper presents a federated framework for causal representation learning in state-space systems, enabling decentralized counterfactua...

[2602.19406] LEVDA: Latent Ensemble Variational Data Assimilation via Differentiable Dynamics

The paper presents LEVDA, a novel ensemble-space variational smoother for geophysical forecasting that improves data assimilation by oper...

[2602.19404] One Size Fits None: Modeling NYC Taxi Trips

This paper analyzes 280 million NYC taxi trips to compare tipping behaviors between traditional taxis and app-based services, revealing d...

[2602.18504] A Computer Vision Framework for Multi-Class Detection and Tracking in Soccer Broadcast Footage

This paper presents a computer vision framework for detecting and tracking players and the ball in soccer broadcast footage using a singl...

[2602.18497] PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking

PIPE-RDF presents a novel pipeline for generating schema-specific NL-SPARQL benchmarks, enhancing RDF knowledge graph querying for enterp...

[2602.19393] In Defense of Cosine Similarity: Normalization Eliminates the Gauge Freedom

This paper defends cosine similarity in machine learning, arguing that normalization eliminates issues related to gauge freedom, thus ens...

[2602.18495] RDBLearn: Simple In-Context Prediction Over Relational Databases

RDBLearn introduces a novel approach for in-context learning (ICL) in relational databases, enabling efficient prediction tasks without e...

[2602.19355] Active perception and disentangled representations allow continual, episodic zero and few-shot learning

This paper presents a Complementary Learning System (CLS) that enables continual, episodic zero and few-shot learning by utilizing active...

[2602.18492] Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries

The paper explores the effectiveness of unanimous committees of Large Language Models (LLMs) in evaluating SQL queries, revealing insight...

[2602.18483] Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation

The article examines red teaming as a socio-technical practice in evaluating large language models (LLMs), highlighting the importance of...

[2602.18481] AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

The paper introduces AlphaForgeBench, a framework for evaluating trading strategies using Large Language Models (LLMs), addressing issues...