:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Singh, Prabhav, Sorrell, Jessica
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2508.04901
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Cost of Replicability in Active Learning
by: Hira, Rupkatha, et al.
Published: (2024)

Computationally Efficient Replicable Learning of Parities and Applications
by: Noivirt, Moshe, et al.
Published: (2026)

Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)

Content-based Recommendation Engine for Video Streaming Platform
by: Khadka, Puskal, et al.
Published: (2023)

Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
by: Sanga, Prabhav, et al.
Published: (2025)

Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search
by: Oguri, Yutaro, et al.
Published: (2024)

Stabilizing Multimodal Autoencoders: A Theoretical and Empirical Analysis of Fusion Strategies
by: Altinses, Diyar, et al.
Published: (2025)

Oracle-Efficient Reinforcement Learning for Max Value Ensembles
by: Hussing, Marcel, et al.
Published: (2024)

On the Generalization for Transfer Learning: An Information-Theoretic Analysis
by: Wu, Xuetong, et al.
Published: (2022)

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
by: Qin, Jiayong, et al.
Published: (2025)

Stability and List-Replicability for Agnostic Learners
by: Blondal, Ari, et al.
Published: (2025)

Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
by: Eaton, Eric, et al.
Published: (2025)

Distributional Adversarial Loss
by: Ahmadi, Saba, et al.
Published: (2024)

Data Selection for Transfer Unlearning
by: Sepahvand, Nazanin Mohammadi, et al.
Published: (2024)

Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection
by: Beck, Nathan, et al.
Published: (2024)

Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
by: Vranješ, Daniel, et al.
Published: (2024)

Adaptive Sampling for Hydrodynamic Stability
by: Singh, Anshima, et al.
Published: (2025)

Approximate Replicability in Learning
by: Hopkins, Max, et al.
Published: (2025)

Replicable Online Learning
by: Ahmadi, Saba, et al.
Published: (2024)

An Empirical Study of $μ$P Learning Rate Transfer
by: Lingle, Lucas
Published: (2024)

Replicable Bandits with UCB based Exploration
by: Deb, Rohan, et al.
Published: (2026)

Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
by: Bertsimas, Dimitris, et al.
Published: (2021)

When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models
by: Cherkaoui, Hamza, et al.
Published: (2025)

ERICA: Quantifying Replicability of Cluster Analysis
by: Sorooshyari, Siamak K., et al.
Published: (2026)

On the Computational Landscape of Replicable Learning
by: Kalavasis, Alkis, et al.
Published: (2024)

Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
by: Xu, Huangyu, et al.
Published: (2026)

Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure
by: Cai, T. Tony, et al.
Published: (2024)

Model Agreement via Anchoring
by: Eaton, Eric, et al.
Published: (2026)

Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs
by: Xiong, Ruoxuan, et al.
Published: (2024)

Control Theoretic Approach to Fine-Tuning and Transfer Learning
by: Bayram, Erkan, et al.
Published: (2024)

An Empirical Study of Scaling Laws for Transfer
by: Barnett, Matthew
Published: (2024)

Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets
by: Mrabet, Zakaria El, et al.
Published: (2025)

Ultra-lightweight Neural Differential DSP Vocoder For High Quality Speech Synthesis
by: Agrawal, Prabhav, et al.
Published: (2024)

Modeling Quantum Machine Learning for Genomic Data Analysis
by: Singh, Navneet, et al.
Published: (2025)

Data Shifts Hurt CoT: A Theoretical Study
by: Yin, Lang, et al.
Published: (2025)

Smoothness Adaptive Hypothesis Transfer Learning
by: Lin, Haotian, et al.
Published: (2024)

On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
by: Satou, Hana, et al.
Published: (2025)

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
by: Bai, Yuxuan, et al.
Published: (2025)

On Supernet Transfer Learning for Effective Task Adaptation
by: Singh, Prabhant, et al.
Published: (2024)

Poisoning Bayesian Inference via Data Deletion and Replication
by: Carreau, Matthieu, et al.
Published: (2025)