DAVED: Data Acquisition via Experimental Design for Data Markets
Fuente:
arXiv
Saved in:
| Main Authors: | Lu, Charles, Huang, Baihe, Karimireddy, Sai Praneeth, Vepakomma, Praneeth, Jordan, Michael, Raskar, Ramesh |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes
by: Vepakomma, Praneeth
Published: (2026)
by: Vepakomma, Praneeth
Published: (2026)
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
by: Guo, Tianyu, et al.
Published: (2024)
by: Guo, Tianyu, et al.
Published: (2024)
Do Data Valuations Make Good Data Prices?
by: Fan, Dongyang, et al.
Published: (2025)
by: Fan, Dongyang, et al.
Published: (2025)
Defection-Free Collaboration between Competitors in a Learning System
by: Werner, Mariel, et al.
Published: (2024)
by: Werner, Mariel, et al.
Published: (2024)
Predicting Survival of Hemodialysis Patients using Federated Learning
by: Raju, Abhiram, et al.
Published: (2024)
by: Raju, Abhiram, et al.
Published: (2024)
Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption
by: Vepakomma, Praneeth, et al.
Published: (2025)
by: Vepakomma, Praneeth, et al.
Published: (2025)
Optimization with Access to Auxiliary Information
by: Chayti, El Mahdi, et al.
Published: (2022)
by: Chayti, El Mahdi, et al.
Published: (2022)
On the Limits of Momentum in Decentralized and Federated Optimization
by: Zaccone, Riccardo, et al.
Published: (2025)
by: Zaccone, Riccardo, et al.
Published: (2025)
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
by: Rokvic, Ljubomir, et al.
Published: (2022)
by: Rokvic, Ljubomir, et al.
Published: (2022)
Data Measurements for Decentralized Data Markets
by: Lu, Charles, et al.
Published: (2024)
by: Lu, Charles, et al.
Published: (2024)
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
by: Issaid, Chaouki Ben, et al.
Published: (2025)
by: Issaid, Chaouki Ben, et al.
Published: (2025)
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment
by: Bakman, Yavuz, et al.
Published: (2026)
by: Bakman, Yavuz, et al.
Published: (2026)
Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
by: Fan, Dongyang, et al.
Published: (2025)
by: Fan, Dongyang, et al.
Published: (2025)
VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
by: Tsaprazlis, Efthymios, et al.
Published: (2025)
by: Tsaprazlis, Efthymios, et al.
Published: (2025)
Learning in the Null Space: Small Singular Values for Continual Learning
by: Pham, Cuong Anh, et al.
Published: (2026)
by: Pham, Cuong Anh, et al.
Published: (2026)
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
by: Zaccone, Riccardo, et al.
Published: (2023)
by: Zaccone, Riccardo, et al.
Published: (2023)
Entropy-driven Fair and Effective Federated Learning
by: Wang, Lin, et al.
Published: (2023)
by: Wang, Lin, et al.
Published: (2023)
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
by: Yun, Vincent-Daniel, et al.
Published: (2026)
by: Yun, Vincent-Daniel, et al.
Published: (2026)
Offline and Online KL-Regularized RLHF under Differential Privacy
by: Wu, Yulian, et al.
Published: (2025)
by: Wu, Yulian, et al.
Published: (2025)
DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
by: Thareja, Rushil, et al.
Published: (2025)
by: Thareja, Rushil, et al.
Published: (2025)
ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
by: Singhal, Raghav, et al.
Published: (2025)
by: Singhal, Raghav, et al.
Published: (2025)
Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study
by: Ponkshe, Kaustubh, et al.
Published: (2025)
by: Ponkshe, Kaustubh, et al.
Published: (2025)
LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction
by: Vadlapati, Praneeth
Published: (2024)
by: Vadlapati, Praneeth
Published: (2024)
Modulated learning for private and distributed regression with just a single sample per client device
by: Vepakomma, Praneeth, et al.
Published: (2026)
by: Vepakomma, Praneeth, et al.
Published: (2026)
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
by: Oh, Seungeun, et al.
Published: (2024)
by: Oh, Seungeun, et al.
Published: (2024)
Conformal Prediction Adaptive to Unknown Subpopulation Shifts
by: Wang, Nien-Shao, et al.
Published: (2025)
by: Wang, Nien-Shao, et al.
Published: (2025)
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
by: Panda, Subhodip, et al.
Published: (2025)
by: Panda, Subhodip, et al.
Published: (2025)
Robust Multi-Agent LLMs under Byzantine Faults
by: Lee, Haejoon, et al.
Published: (2026)
by: Lee, Haejoon, et al.
Published: (2026)
EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
by: Banayeeanzade, Amin, et al.
Published: (2026)
by: Banayeeanzade, Amin, et al.
Published: (2026)
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
by: Chen, Minghui, et al.
Published: (2025)
by: Chen, Minghui, et al.
Published: (2025)
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
by: Ponkshe, Kaustubh, et al.
Published: (2024)
by: Ponkshe, Kaustubh, et al.
Published: (2024)
Reconsidering LLM Uncertainty Estimation Methods in the Wild
by: Bakman, Yavuz, et al.
Published: (2025)
by: Bakman, Yavuz, et al.
Published: (2025)
Reject Only Critical Tokens: Pivot-Aware Speculative Decoding
by: Ziashahabi, Amir, et al.
Published: (2025)
by: Ziashahabi, Amir, et al.
Published: (2025)
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
by: Singhal, Raghav, et al.
Published: (2025)
by: Singhal, Raghav, et al.
Published: (2025)
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels
by: Kacham, Praneeth, et al.
Published: (2023)
by: Kacham, Praneeth, et al.
Published: (2023)
The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
by: Chizat, Lénaïc, et al.
Published: (2023)
by: Chizat, Lénaïc, et al.
Published: (2023)
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
by: Tasneem, Zaid, et al.
Published: (2024)
by: Tasneem, Zaid, et al.
Published: (2024)
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models
by: Singhal, Raghav, et al.
Published: (2024)
by: Singhal, Raghav, et al.
Published: (2024)
Study on Downlink CSI compression: Are Neural Networks the Only Solution?
by: Praneeth, K. Sai, et al.
Published: (2025)
by: Praneeth, K. Sai, et al.
Published: (2025)
Similar Items
-
Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes
by: Vepakomma, Praneeth
Published: (2026) -
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
by: Guo, Tianyu, et al.
Published: (2024) -
Do Data Valuations Make Good Data Prices?
by: Fan, Dongyang, et al.
Published: (2025) -
Defection-Free Collaboration between Competitors in a Learning System
by: Werner, Mariel, et al.
Published: (2024) -
Predicting Survival of Hemodialysis Patients using Federated Learning
by: Raju, Abhiram, et al.
Published: (2024)