732 citations · 1.7k across the 44 of their papers we have counts for
47 papers
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen, Hrad Ghoukasian, Ruinan Jin +3
Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data…
ContextLeak: Auditing Leakage in Private In-Context Learning Methods
Jacob Choi, Shuying Cao, Xingjian Dong +4
In-Context Learning (ICL) has become a standard technique for adapting Large Language Models (LLMs) to specialized tasks by supplying task-specific exemplars within the prompt. How…
Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy +1
Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one usef…
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness
Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani +5
The ability to control LLMs' emulated emotional states and personality traits is an essential step in enabling rich, human-centered interactions in socially interactive settings. W…
Conformal Prediction Adaptive to Unknown Subpopulation Shifts
Nien-Shao Wang, Duygu Nur Yaldiz, Yavuz Faruk Bakman +1
Conformal prediction is widely used to equip black-box machine learning models with uncertainty quantification, offering formal coverage guarantees under exchangeable data. However…
Reconsidering LLM Uncertainty Estimation Methods in the Wild
Yavuz Bakman, Duygu Nur Yaldiz, Sungmin Kang +4
Large Language Model (LLM) Uncertainty Estimation (UE) methods have become a crucial tool for detecting hallucinations in recent years. While numerous UE methods have been proposed…
A Systematic Analysis of Base Model Choice for Reward Modeling
Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian +2
Reinforcement learning from human feedback (RLHF) and, at its core, reward modeling have become a crucial part of training powerful large language models (LLMs). One commonly overl…
Do Data Valuations Make Good Data Prices?
Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy
As large language models increasingly rely on external data sources, compensating data contributors has become a central concern. But how should these payments be devised? We revis…
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
Narasimha Raghavan Veeraragavan, Sai Praneeth Karimireddy, Jan Franz Nygård
This paper presents a differentially private approach to Kaplan-Meier estimation that achieves accurate survival probability estimates while safeguarding individual privacy. The Ka…
Defection-Free Collaboration between Competitors in a Learning System
Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan
We study collaborative learning systems in which the participants are competitors who will defect from the system if they lose revenue by collaborating. As such, we frame the syste…
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the co…
Privacy Can Arise Endogenously in an Economic System with Learning Agents
Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner +2
We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs…
DAVED: Data Acquisition via Experimental Design for Data Markets
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy +3
The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare,…
Scaff-PD: Communication Efficient Fair and Robust Federated Learning
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma +1
We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distribut…
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performanc…
Provably Personalized and Robust Federated Learning
Mariel Werner, Lie He, Michael Jordan +2
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so…
Federated Conformal Predictors for Distributed Uncertainty Quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy +2
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing ste…
Online Learning in a Creator Economy
Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao +1
The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by mo…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Mechanisms that Incentivize Data Sharing in Federated Learning
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve prob…
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy +2
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even whe…
Optimization with Access to Auxiliary Information
El Mahdi Chayti, Sai Praneeth Karimireddy
We investigate the fundamental optimization question of minimizing a target function , whose gradients are expensive to compute or have limited availability, given access to som…
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy +1
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To a…