Sai Praneeth Karimireddy
25 papers · 2018–2026 · 7 venues · across top CS/AI venues
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federated learning
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distributed learning
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data heterogeneity
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decentralized learning
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stochastic gradient descent
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communication efficiency
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large language model
(3)
distributed optimization
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gradient compression
(3)
stochastic optimization
(2)
uncertainty estimation
(2)
accelerated convergence
(2)
ensemble learning
(2)
convex optimization
(2)
error feedback
(2)
byzantine robustness
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heterogeneous data
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coordinate descent
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low-rank approximation
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uncertainty quantification
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Papers
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness
ACL 2026
A Systematic Analysis of Base Model Choice for Reward Modeling
EMNLP 2025
Reconsidering LLM Uncertainty Estimation Methods in the Wild
ACL 2025
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
EMNLP 2025
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
ICML 2024
Data Acquisition via Experimental Design for Data Markets
NeurIPS 2024
Agree to Disagree: Diversity through Disagreement for Better Transferability
ICLR 2023
Federated Conformal Predictors for Distributed Uncertainty Quantification
ICML 2023
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
ICLR 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
NeurIPS 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
NeurIPS 2022
Towards Model Agnostic Federated Learning Using Knowledge Distillation
ICLR 2022
Breaking the centralized barrier for cross-device federated learning
NeurIPS 2021
Learning from History for Byzantine Robust Optimization
ICML 2021
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
ICML 2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data
NeurIPS 2021
Accelerating Gradient Boosting Machines
AISTATS 2020
Practical Low-Rank Communication Compression in Decentralized Deep Learning
NeurIPS 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
ICML 2020
The Error-Feedback framework: SGD with Delayed Gradients
JMLR 2020
Why are Adaptive Methods Good for Attention Models?
NeurIPS 2020
Efficient Greedy Coordinate Descent for Composite Problems
AISTATS 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
ICML 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
NeurIPS 2019
On Matching Pursuit and Coordinate Descent
ICML 2018