:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Yan, Blanchet, Jose, Dembczynski, Krzysztof, Nern, Laura Fee, Flores, Aaron
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2410.08994
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
by: Mazzetto, Alessio, et al.
Published: (2026)

A General Online Algorithm for Optimizing Complex Performance Metrics
by: Kotłowski, Wojciech, et al.
Published: (2024)

Generalized test utilities for long-tail performance in extreme multi-label classification
by: Schultheis, Erik, et al.
Published: (2023)

Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?
by: De Waele, Gaetan, et al.
Published: (2026)

Consistent algorithms for multi-label classification with macro-at-$k$ metrics
by: Schultheis, Erik, et al.
Published: (2024)

Optimal Sample Complexity for Average Reward Markov Decision Processes
by: Wang, Shengbo, et al.
Published: (2023)

Geometric Imbalance in Semi-Supervised Node Classification
by: Yan, Liang, et al.
Published: (2023)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
by: Lu, Miao, et al.
Published: (2024)

REDUCR: Robust Data Downsampling Using Class Priority Reweighting
by: Bankes, William, et al.
Published: (2023)

When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions
by: Tan, Lezhi, et al.
Published: (2026)

Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets
by: Nguyen, Thuan, et al.
Published: (2026)

Group Downsampling with Equivariant Anti-aliasing
by: Rahman, Md Ashiqur, et al.
Published: (2025)

Unifying Distributionally Robust Optimization via Optimal Transport Theory
by: Blanchet, Jose, et al.
Published: (2023)

Automatic Outlier Rectification via Optimal Transport
by: Blanchet, Jose, et al.
Published: (2024)

Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces
by: Wang, Shengbo, et al.
Published: (2024)

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
by: Yan, Liang, et al.
Published: (2023)

Classification Imbalance as Transfer Learning
by: Xia, Eric, et al.
Published: (2026)

ToDo: Token Downsampling for Efficient Generation of High-Resolution Images
by: Smith, Ethan, et al.
Published: (2024)

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
by: Marisca, Ivan, et al.
Published: (2024)

Generative Learning for Simulation of Vehicle Faults
by: Kuiper, Patrick, et al.
Published: (2024)

Oversampling and Downsampling with Core-Boundary Awareness: A Data Quality-Driven Approach
by: Belhaouari, Samir Brahim, et al.
Published: (2025)

ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters
by: Liu, Hao, et al.
Published: (2024)

The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
by: Stando, Adrian, et al.
Published: (2023)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction
by: Agrawal, Saurabh, et al.
Published: (2025)

Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
by: D'souza, Annie, et al.
Published: (2024)

Learning Confidence Bounds for Classification with Imbalanced Data
by: Clifford, Matt, et al.
Published: (2024)

Graffin: Stand for Tails in Imbalanced Node Classification
by: Qi, Xiaorui, et al.
Published: (2024)

Conformal Inference for Open-Set and Imbalanced Classification
by: Xie, Tianmin, et al.
Published: (2025)

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
by: Ma, Rongrong, et al.
Published: (2024)

VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
by: Hu, Yulan, et al.
Published: (2023)

All-around Neural Collapse for Imbalanced Classification
by: Zhang, Enhao, et al.
Published: (2024)

Catch Causal Signals from Edges for Label Imbalance in Graph Classification
by: Zhang, Fengrui, et al.
Published: (2025)

Large Language Models for Imbalanced Classification: Diversity makes the difference
by: Nguyen, Dang, et al.
Published: (2025)

Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)

BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Losses
by: Yuan, Xiao
Published: (2023)

Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
by: Wu, David X., et al.
Published: (2023)

Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
by: Li, Wenjie, et al.
Published: (2025)

IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification
by: Li, Zhixun, et al.
Published: (2025)

Limit Theorems for Stochastic Gradient Descent with Infinite Variance
by: Blanchet, Jose, et al.
Published: (2024)