Sai Praneeth Karimireddy
25 papers · 2018–2026 · 7 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (7) π Conference Polyglot (7) π§ Keyword Pioneer π£ Hot Topic Early Bird π Cross-Pollinator (10)
πΊοΈ
Taxonomy Completionist
(33)
π§
Keyword Pioneer
π
Conference Polyglot
(7)
π¬
Deep Specialist
(10)
π€
Dynamic Duo
(12)
π₯
Mega-Team
(24)
ποΈ
Keyword Collector
(83)
β
The Questioner
β‘
Prolific Year
(5)
π
Century Club
(24)
π₯
Unstoppable
(8)
Conferences
NIPS (8)
ICML (7)
ICLR (3)
ACL (2)
AISTATS (2)
EMNLP (2)
JMLR (1)
Top co-authors
Keywords
federated learning
(6)
distributed learning
(4)
communication efficiency
(3)
data heterogeneity
(3)
decentralized learning
(3)
large language model
(3)
stochastic gradient descent
(3)
gradient compression
(3)
distributed optimization
(3)
client drift
(2)
convex optimization
(2)
stochastic optimization
(2)
uncertainty quantification
(2)
error feedback
(2)
control variate
(2)
uncertainty estimation
(2)
coordinate descent
(2)
low-rank approximation
(2)
accelerated convergence
(2)
model calibration
(1)
Papers
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness
ACL 2026
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
EMNLP 2025
Reconsidering LLM Uncertainty Estimation Methods in the Wild
ACL 2025
A Systematic Analysis of Base Model Choice for Reward Modeling
EMNLP 2025
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
ICML 2024
Data Acquisition via Experimental Design for Data Markets
NIPS 2024
Federated Conformal Predictors for Distributed Uncertainty Quantification
ICML 2023
Agree to Disagree: Diversity through Disagreement for Better Transferability
ICLR 2023
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
ICLR 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
NIPS 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
NIPS 2022
Towards Model Agnostic Federated Learning Using Knowledge Distillation
ICLR 2022
RelaySum for Decentralized Deep Learning on Heterogeneous Data
NIPS 2021
Learning from History for Byzantine Robust Optimization
ICML 2021
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
ICML 2021
Breaking the centralized barrier for cross-device federated learning
NIPS 2021
Accelerating Gradient Boosting Machines
AISTATS 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?
NIPS 2020
Practical Low-Rank Communication Compression in Decentralized Deep Learning
NIPS 2020
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
ICML 2019
Efficient Greedy Coordinate Descent for Composite Problems
AISTATS 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
NIPS 2019
On Matching Pursuit and Coordinate Descent
ICML 2018