:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Chenyang, Song, Zhao, Xu, Zhaoxing, Yin, Junze
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2408.11267
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How to Inverting the Leverage Score Distribution?
by: Li, Zhihang, et al.
Published: (2024)

Binary Hypothesis Testing for Softmax Models and Leverage Score Models
by: Gu, Yuzhou, et al.
Published: (2024)

Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation
by: Song, Zhao, et al.
Published: (2023)

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
by: Liang, Yingyu, et al.
Published: (2024)

Fast and Efficient Matching Algorithm with Deadline Instances
by: Song, Zhao, et al.
Published: (2023)

Solving Attention Kernel Regression Problem via Pre-conditioner
by: Song, Zhao, et al.
Published: (2023)

An Iterative Algorithm for Rescaled Hyperbolic Functions Regression
by: Gao, Yeqi, et al.
Published: (2023)

Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory
by: Liang, Jiehao, et al.
Published: (2022)

Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
by: Gu, Yuzhou, et al.
Published: (2023)

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

One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space
by: Addanki, Raghav, et al.
Published: (2023)

Sublinear Time Algorithm for Online Weighted Bipartite Matching
by: Hu, Hang, et al.
Published: (2022)

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

A Faster $k$-means++ Algorithm
by: Liang, Jiehao, et al.
Published: (2022)

When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
by: Li, Chenyang, et al.
Published: (2025)

Sparse Asymptotic PCA: Identifying Sparse Latent Factors Across Time Horizon in High-Dimensional Time Series
by: Gao, Zhaoxing
Published: (2024)

Ridge Leverage Score Sampling for $\ell_p$ Subspace Approximation
by: Woodruff, David P., et al.
Published: (2024)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
by: Orvieto, Antonio, et al.
Published: (2024)

Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
by: Chiang, John
Published: (2022)

Score Operator Newton transport
by: Chandramoorthy, Nisha, et al.
Published: (2023)

Gradient descent with generalized Newton's method
by: Bu, Zhiqi, et al.
Published: (2024)

Gradient-Free Score-Based Sampling Methods with Ensembles
by: Riel, Bryan, et al.
Published: (2024)

Split-and-Conquer: Distributed Factor Modeling for High-Dimensional Matrix-Variate Time Series
by: Jiang, Hangjin, et al.
Published: (2026)

Modeling High-Dimensional Dependent Data in the Presence of Many Explanatory Variables and Weak Signals
by: Gao, Zhaoxing, et al.
Published: (2024)

High-Dimensional Robust Mean Estimation with Untrusted Batches
by: Aliakbarpour, Maryam, et al.
Published: (2026)

Support Basis: Fast Attention Beyond Bounded Entries
by: Aliakbarpour, Maryam, et al.
Published: (2025)

A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
by: Li, Zehao, et al.
Published: (2025)

Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
by: Gao, Zhaoxing, et al.
Published: (2024)

Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting
by: Li, Hongyi, et al.
Published: (2026)

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
by: Niu, Chengmei, et al.
Published: (2025)

Inverting Gradient Attacks Makes Powerful Data Poisoning
by: Bouaziz, Wassim, et al.
Published: (2024)

Matrix Low-Rank Approximation For Policy Gradient Methods
by: Rozada, Sergio, et al.
Published: (2024)

Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and Applications
by: Chen, Jin, et al.
Published: (2025)

InstaHide's Sample Complexity When Mixing Two Private Images
by: Huang, Baihe, et al.
Published: (2020)

Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers
by: Song, Heekang, et al.
Published: (2025)

A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods
by: Lin, Wei, et al.
Published: (2024)

Transformers Trained via Gradient Descent Can Provably Learn a Class of Teacher Models
by: Zhang, Chenyang, et al.
Published: (2026)

Gradient Boosted Risk Scores
by: Georgantas, Costa, et al.
Published: (2026)

Supervised Score-Based Modeling by Gradient Boosting
by: Zhao, Changyuan, et al.
Published: (2024)

Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)