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...

[2602.20111] Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds

This paper explores reliable abstention in online learning under adversarial injections, presenting new lower and upper bounds for error ...

[2602.18915] AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting

The article presents AAVGen, a generative AI framework designed for the precise engineering of adeno-associated viral capsids, enhancing ...

[2602.20070] Training-Free Generative Modeling via Kernelized Stochastic Interpolants

This paper presents a novel kernel method for generative modeling that eliminates the need for training neural networks, utilizing linear...

[2602.20062] A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

This paper presents a theoretical framework explaining how pretraining influences inductive bias during fine-tuning in machine learning, ...

[2602.18891] Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation

This pilot study explores the orchestration of LLM agents in scientific research, focusing on the generation and evaluation of multiple-c...

[2602.20019] Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

This article presents a novel framework for dynamic graph anomaly detection that effectively utilizes limited labeled anomalies while mai...

[2602.20003] A Secure and Private Distributed Bayesian Federated Learning Design

This paper presents a novel framework for Distributed Federated Learning (DFL) that enhances privacy, convergence speed, and robustness a...

[2602.19987] Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction

The paper presents CURE, a novel framework for counterfactual survival prediction that integrates multimodal data to enhance individualiz...

[2602.19964] On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference

This paper establishes theoretical connections between Random Network Distillation (RND), Deep Ensembles, and Bayesian Inference, enhanci...

[2602.19945] DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models

The paper introduces DP-FedAdamW, a novel optimizer designed for differentially private federated learning, addressing key challenges in ...

[2602.18824] UniRank: A Multi-Agent Calibration Pipeline for Estimating University Rankings from Anonymized Bibliometric Signals

UniRank introduces a multi-agent pipeline that estimates university rankings using anonymized bibliometric data, achieving notable accura...

[2602.19915] Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction

This article presents a deep learning framework for predicting microstructure evolution in materials science, enhancing accuracy while re...

[2602.19912] De novo molecular structure elucidation from mass spectra via flow matching

This article presents MSFlow, a novel generative model for de novo molecular structure elucidation from mass spectra, significantly impro...

[2602.19893] Generalized Random Direction Newton Algorithms for Stochastic Optimization

This paper introduces generalized Hessian estimators for stochastic optimization using random direction stochastic approximation, demonst...

[2602.19845] I Dropped a Neural Net

The paper 'I Dropped a Neural Net' explores a unique challenge in machine learning, where a neural network's layers are shuffled and need...

[2602.18763] TAG: Thinking with Action Unit Grounding for Facial Expression Recognition

The paper introduces TAG, a vision-language framework for Facial Expression Recognition (FER) that enhances reasoning by grounding predic...

[2602.18759] Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity

This paper presents ICPNS, a novel framework for negative sampling in recommendation systems that utilizes user community structures to e...

[2602.19790] Drift Localization using Conformal Predictions

This article presents a novel approach to drift localization using conformal predictions, addressing challenges in monitoring concept dri...

[2602.19789] Stop Preaching and Start Practising Data Frugality for Responsible Development of AI

This paper advocates for the machine learning community to adopt data frugality in AI development, emphasizing its environmental benefits...

[2602.19788] Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings

This paper presents a novel Bayesian meta-learning approach that utilizes expert feedback and causal embeddings to enhance task-shift ada...