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Machine Learning

[D] ICML 2026 Average Score

Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

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...

AI News - General · 4 min ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·

All Content

[2505.06646] Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
Machine Learning

[2505.06646] Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification

This article discusses the reproduction and enhancement of CheXNet, a deep learning model for classifying chest X-ray diseases, using the...

arXiv - Machine Learning · 3 min ·
[2503.04071] Tightening Optimality gap with confidence through conformal prediction
Machine Learning

[2503.04071] Tightening Optimality gap with confidence through conformal prediction

This article presents a novel conformal prediction framework aimed at tightening optimality gaps in constrained optimization, enhancing s...

arXiv - Machine Learning · 4 min ·
[2412.01283] Big data approach to Kazhdan-Lusztig polynomials
Nlp

[2412.01283] Big data approach to Kazhdan-Lusztig polynomials

This article explores the application of big data techniques to analyze Kazhdan-Lusztig polynomials, focusing on their structure within s...

arXiv - Machine Learning · 3 min ·
[2410.16106] Statistical Inference for Temporal Difference Learning with Linear Function Approximation
Machine Learning

[2410.16106] Statistical Inference for Temporal Difference Learning with Linear Function Approximation

This paper explores the statistical properties of Temporal Difference learning with Polyak-Ruppert averaging, enhancing parameter estimat...

arXiv - Machine Learning · 4 min ·
[2407.12506] Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
Machine Learning

[2407.12506] Classification and reconstruction for single-pixel imaging with classical and quantum neural networks

This article explores the use of classical and quantum neural networks for single-pixel imaging, demonstrating effective classification a...

arXiv - Machine Learning · 4 min ·
[2602.04192] LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data
Nlp

[2602.04192] LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data

The paper presents LORE, a framework for learning intrinsic dimensionality and ordinal embeddings from noisy triplet comparisons, enhanci...

arXiv - Machine Learning · 4 min ·
[2601.11283] Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Machine Learning

[2601.11283] Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

This article presents a novel approach to ADHD diagnosis by integrating urinary metabolomics with interpretable machine learning, identif...

arXiv - Machine Learning · 3 min ·
[2510.22293] Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
Machine Learning

[2510.22293] Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study

This study presents a machine learning model for predicting Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) using clinic...

arXiv - Machine Learning · 4 min ·
[2509.19975] From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Machine Learning

[2509.19975] From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting

This article introduces a new paradigm for probabilistic forecasting, proposing 'Probabilistic Scenarios' as an alternative to traditiona...

arXiv - Machine Learning · 3 min ·
[2509.21895] Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Machine Learning

[2509.21895] Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs

This paper explores an algebraic framework to explain why high-rank neural networks generalize effectively, deriving new Rademacher compl...

arXiv - Machine Learning · 3 min ·
[2508.21785] Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Machine Learning

[2508.21785] Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling

This paper presents a novel framework for heart rate modeling that addresses data heterogeneity by learning unified representations from ...

arXiv - Machine Learning · 4 min ·
[2508.16815] Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Machine Learning

[2508.16815] Uncertainty Propagation Networks for Neural Ordinary Differential Equations

The paper presents Uncertainty Propagation Networks (UPN), a novel approach to neural ordinary differential equations that integrates unc...

arXiv - Machine Learning · 3 min ·
[2508.01115] A hierarchy tree data structure for behavior-based user segment representation
Computer Vision

[2508.01115] A hierarchy tree data structure for behavior-based user segment representation

This paper introduces a novel hierarchy tree data structure for behavior-based user segmentation, enhancing recommendation systems by add...

arXiv - Machine Learning · 4 min ·
[2507.04448] Transfer Learning in Infinite Width Feature Learning Networks
Machine Learning

[2507.04448] Transfer Learning in Infinite Width Feature Learning Networks

This article presents a theoretical framework for understanding transfer learning in infinitely wide neural networks, focusing on how pre...

arXiv - Machine Learning · 4 min ·
[2506.18481] FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Machine Learning

[2506.18481] FREQuency ATTribution: benchmarking frequency-based occlusion for time series data

The paper presents FREQuency ATTribution, a framework for interpreting time-series data using frequency-based occlusion, enhancing the ro...

arXiv - Machine Learning · 4 min ·
[2506.10914] Foundation Models for Causal Inference via Prior-Data Fitted Networks
Llms

[2506.10914] Foundation Models for Causal Inference via Prior-Data Fitted Networks

This paper introduces CausalFM, a framework for training prior-data fitted networks (PFNs) for causal inference, enhancing Bayesian infer...

arXiv - Machine Learning · 4 min ·
[2503.00229] Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Machine Learning

[2503.00229] Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster

The paper discusses how the Armijo line-search method can enhance the convergence speed of stochastic gradient descent (SGD) for both con...

arXiv - Machine Learning · 4 min ·
[2502.03652] Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Machine Learning

[2502.03652] Improving the Convergence of Private Shuffled Gradient Methods with Public Data

This article presents a novel approach to improving the convergence of private shuffled gradient methods in machine learning by integrati...

arXiv - Machine Learning · 4 min ·
[2503.12016] A Survey on Federated Fine-tuning of Large Language Models
Llms

[2503.12016] A Survey on Federated Fine-tuning of Large Language Models

This survey explores the integration of Federated Learning with Large Language Models (LLMs), addressing challenges and methodologies for...

arXiv - Machine Learning · 4 min ·
[2501.10538] Universality of Benign Overfitting in Binary Linear Classification
Machine Learning

[2501.10538] Universality of Benign Overfitting in Binary Linear Classification

This article explores the phenomenon of benign overfitting in binary linear classification, revealing new insights into its occurrence in...

arXiv - Machine Learning · 4 min ·
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