On the Tunability of Optimizers in Deep Learning. - Prabhu Teja Sivaprasad; Florian Mai; Thijs Vogels; Martin Jaggi; François Fleuret (CoRR, 2019)

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. - Prabhu Teja Sivaprasad; Florian Mai; Thijs Vogels; Martin Jaggi; François Fleuret (ICML, 2020)

Uncertainty Reduction for Model Adaptation in Semantic Segmentation. - Prabhu Teja Sivaprasad; François Fleuret (CVPR, 2021)

Test time Adaptation through Perturbation Robustness. - Prabhu Teja Sivaprasad; François Fleuret (CoRR, 2021)

PAUMER: Patch Pausing Transformer for Semantic Segmentation. - Evann Courdier; Prabhu Teja Sivaprasad; François Fleuret (BMVC, 2022)

Continual Learning with Low Rank Adaptation. - Martin Wistuba; Prabhu Teja Sivaprasad; Lukas Balles; Giovanni Zappella (CoRR, 2023)

Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. - Martin Wistuba; Prabhu Teja Sivaprasad; Lukas Balles; Giovanni Zappella (CoRR, 2024)

On the Choice of Learning Rate for Local SGD. - Lukas Balles; Prabhu Teja Sivaprasad; Cédric Archambeau (Trans. Mach. Learn. Res., 2024)

SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents. - Muhammad Shihab Rashid; Christian Bock; Yuan Zhuang; Alexander Buchholz; Timothy B. Esler; Simon Valentin; Luca Franceschi 003; Martin Wistuba; Prabhu Teja Sivaprasad; Woo Jung Kim; Anoop Deoras; Giovanni Zappella; et al. (CoRR, 2025)

CoRet: Improved Retriever for Code Editing. - Fabio Fehr; Prabhu Teja Sivaprasad; Luca Franceschi 003; Giovanni Zappella (CoRR, 2025)