:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Di Gennaro, Federico, Chakraborty, Saptarshi, Zhivotovskiy, Nikita
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2604.02920
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function
by: Pham, Tuan Minh, et al.
Published: (2026)

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
by: Chen, Fan, et al.
Published: (2026)

Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
by: Montasser, Omar, et al.
Published: (2025)

Ratio Covers of Convex Sets and Optimal Mixture Density Estimation
by: Compton, Spencer, et al.
Published: (2026)

Refined Risk Bounds for Unbounded Losses via Transductive Priors
by: Qian, Jian, et al.
Published: (2024)

Lower Bounds for Greedy Teaching Set Constructions
by: Compton, Spencer, et al.
Published: (2025)

Consistency and Inconsistency in $K$-Means Clustering
by: Blanchard, Moïse, et al.
Published: (2025)

Revisiting Agnostic PAC Learning
by: Hanneke, Steve, et al.
Published: (2024)

Derandomizing Multi-Distribution Learning
by: Larsen, Kasper Green, et al.
Published: (2024)

Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
by: Nguyen, Huy, et al.
Published: (2024)

Dimension-free Private Mean Estimation for Anisotropic Distributions
by: Dagan, Yuval, et al.
Published: (2024)

Majority-of-Three: The Simplest Optimal Learner?
by: Aden-Ali, Ishaq, et al.
Published: (2024)

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
by: Chakraborty, Sujan, et al.
Published: (2026)

Compact Memory for Continual Logistic Regression
by: Jung, Yohan, et al.
Published: (2025)

A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
by: Zhang, Chenyang, et al.
Published: (2026)

Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring
by: Di Gennaro, Federico, et al.
Published: (2025)

F-measure Maximizing Logistic Regression
by: Okabe, Masaaki, et al.
Published: (2019)

Theory, Analysis, and Best Practices for Sigmoid Self-Attention
by: Ramapuram, Jason, et al.
Published: (2024)

Near-Polynomially Competitive Active Logistic Regression
by: Zhou, Yihan, et al.
Published: (2025)

An Experiment on Feature Selection using Logistic Regression
by: Islam, Raisa, et al.
Published: (2024)

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Grokking in Linear Models for Logistic Regression
by: Das, Nataraj, et al.
Published: (2026)

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
by: Chakraborty, Sunrit, et al.
Published: (2024)

Controlled Model Debiasing through Minimal and Interpretable Updates
by: Di Gennaro, Federico, et al.
Published: (2025)

Riemannian Multinomial Logistics Regression for SPD Neural Networks
by: Chen, Ziheng, et al.
Published: (2023)

LFFR: Logistic Function For (single-output) Regression
by: Chiang, John
Published: (2024)

Neural-g: A Deep Learning Framework for Mixing Density Estimation
by: Wang, Shijie, et al.
Published: (2024)

Data-Driven Logistic Regression Ensembles With Applications in Genomics
by: Christidis, Anthony-Alexander, et al.
Published: (2021)

$L_1$-norm Regularized Indefinite Kernel Logistic Regression
by: Wang, Shaoxin, et al.
Published: (2025)

Interpretable Predictive Models for Healthcare via Rational Logistic Regression
by: Suttaket, Thiti, et al.
Published: (2024)

Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)

Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
by: Crawshaw, Michael, et al.
Published: (2025)

Residue-based Label Protection Mechanisms in Vertical Logistic Regression
by: Tan, Juntao, et al.
Published: (2022)

A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
by: Nguyen, Huy, et al.
Published: (2023)

Mixed-feature Logistic Regression Robust to Distribution Shifts
by: Sun, Qingshi, et al.
Published: (2025)

RMLR: Extending Multinomial Logistic Regression into General Geometries
by: Chen, Ziheng, et al.
Published: (2024)