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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

[2602.21160] Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Machine Learning

[2602.21160] Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions

The paper presents a novel method for decomposing epistemic uncertainty in machine learning models into per-class contributions, enhancin...

arXiv - Machine Learning · 4 min ·
[2602.21142] LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
Llms

[2602.21142] LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis

The LUMEN model enhances radiological diagnosis by leveraging longitudinal imaging data and multi-modal training, improving prognostic ca...

arXiv - Machine Learning · 4 min ·
[2602.21138] Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank
Machine Learning

[2602.21138] Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank

This paper investigates the efficiency of the FISTA method for computing $ ext{l}_1$-regularized PageRank, focusing on the trade-offs bet...

arXiv - Machine Learning · 3 min ·
[2602.21039] Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Ai Infrastructure

[2602.21039] Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise

This paper investigates the complexities of multi-distribution learning, revealing that achieving fast learning rates is inherently more ...

arXiv - Machine Learning · 4 min ·
[2602.21130] An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements
Machine Learning

[2602.21130] An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements

This paper introduces enhancements to the projection pursuit tree classifier, focusing on visual methods to assess algorithmic improvemen...

arXiv - Machine Learning · 3 min ·
[2602.21036] Empirically Calibrated Conditional Independence Tests
Machine Learning

[2602.21036] Empirically Calibrated Conditional Independence Tests

The paper presents Empirically Calibrated Conditional Independence Tests (ECCIT), a method designed to enhance the reliability of conditi...

arXiv - Machine Learning · 3 min ·
[2602.20857] Functional Continuous Decomposition
Ai Startups

[2602.20857] Functional Continuous Decomposition

The paper introduces Functional Continuous Decomposition (FCD), a novel framework for analyzing non-stationary time-series data using par...

arXiv - Machine Learning · 3 min ·
[2602.20833] DRESS: A Continuous Framework for Structural Graph Refinement
Machine Learning

[2602.20833] DRESS: A Continuous Framework for Structural Graph Refinement

The paper presents DRESS, a scalable framework for structural graph refinement that outperforms traditional methods in distinguishing com...

arXiv - Machine Learning · 3 min ·
[2602.20805] Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Machine Learning

[2602.20805] Assessing the Impact of Speaker Identity in Speech Spoofing Detection

This paper investigates the influence of speaker identity on speech spoofing detection systems, proposing a framework that integrates spe...

arXiv - Machine Learning · 3 min ·
[2602.20712] F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization
Machine Learning

[2602.20712] F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization

This article presents a novel forecasting method for the F10.7 solar index using wavelet decomposition, demonstrating improved prediction...

arXiv - Machine Learning · 4 min ·
[2602.20652] DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Machine Learning

[2602.20652] DANCE: Doubly Adaptive Neighborhood Conformal Estimation

The paper presents DANCE, a novel algorithm for conformal prediction in machine learning that enhances uncertainty quantification by util...

arXiv - Machine Learning · 3 min ·
[2602.20646] On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
Ai Infrastructure

[2602.20646] On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes

This paper analyzes the convergence of Stochastic Gradient Descent (SGD) under perturbations in both forward and backward passes, providi...

arXiv - Machine Learning · 3 min ·
[2602.20585] Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
Machine Learning

[2602.20585] Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness

This paper explores the conditions under which learning is achievable in online and private settings, focusing on generalized smoothness ...

arXiv - Machine Learning · 4 min ·
[2602.20611] Amortized Bayesian inference for actigraph time sheet data from mobile devices
Machine Learning

[2602.20611] Amortized Bayesian inference for actigraph time sheet data from mobile devices

This article presents a novel approach to Bayesian inference for analyzing actigraph time sheet data from mobile devices, focusing on hea...

arXiv - Machine Learning · 4 min ·
[2602.20555] Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,λ}$ Targets
Llms

[2602.20555] Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,λ}$ Targets

This paper demonstrates that standard Transformers can achieve the minimax optimal rate in nonparametric regression for Hölder functions,...

arXiv - Machine Learning · 3 min ·
[2602.20475] PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Machine Learning

[2602.20475] PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC

The article presents PhyGHT, a Physics-Guided HyperGraph Transformer designed to enhance signal purification at the High-Luminosity Large...

arXiv - Machine Learning · 4 min ·
[2602.20383] Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Machine Learning

[2602.20383] Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects

The paper discusses the detection and mitigation of group bias in heterogeneous treatment effects (HTEs) using a unified statistical fram...

arXiv - Machine Learning · 4 min ·
[2602.20394] Selecting Optimal Variable Order in Autoregressive Ising Models
Machine Learning

[2602.20394] Selecting Optimal Variable Order in Autoregressive Ising Models

This article presents a method for selecting optimal variable orderings in autoregressive Ising models, enhancing sampling efficiency and...

arXiv - Machine Learning · 3 min ·
[2602.20289] The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Machine Learning

[2602.20289] The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA

This article presents a systematic validation of deep learning techniques for quantifying GABA in magnetic resonance spectroscopy (MRS), ...

arXiv - Machine Learning · 4 min ·
[2602.20209] Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control
Machine Learning

[2602.20209] Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control

The paper presents DiffuNovo, a novel regressor-guided diffusion model for de novo peptide sequencing that incorporates explicit mass con...

arXiv - Machine Learning · 4 min ·
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