SATISFy: Towards a Self-Learning Analyzer for Time Series Forecasting in Self-Improving Systems. - Christian Krupitzer; Martin Pfannemüller; Jean Kaddour; Christian Becker 001 (FAS*W@SASO/ICAC, 2018)

Probabilistic Active Meta-Learning. - Jean Kaddour; Steindór Sæmundsson; Marc Peter Deisenroth (NeurIPS, 2020)

Causal Effect Inference for Structured Treatments. - Jean Kaddour; Yuchen Zhu; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2021)

Graph Intervention Networks for Causal Effect Estimation. - Jean Kaddour; Qi Liu 049; Yuchen Zhu; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2021)

When Do Flat Minima Optimizers Work? - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (NeurIPS, 2022)

Questions for Flat-Minima Optimization of Modern Neural Networks. - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (CoRR, 2022)

Evaluating Self-Supervised Learning for Molecular Graph Embeddings. - Hanchen Wang 002; Jean Kaddour; Shengchao Liu; Jian Tang 005; Matt J. Kusner; Joan Lasenby; Qi Liu 049 (CoRR, 2022)

Causal Machine Learning: A Survey and Open Problems. - Jean Kaddour; Aengus Lynch; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2022)

Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging. - Jean Kaddour (CoRR, 2022)

DAG Learning on the Permutahedron. - Valentina Zantedeschi; Luca Franceschi 001; Jean Kaddour; Matt J. Kusner; Vlad Niculae (ICLR, 2023)

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. - Jean Kaddour; Oscar Key; Piotr Nawrot; Pasquale Minervini; Matt J. Kusner (NeurIPS, 2023)

Evaluating Self-Supervised Learning for Molecular Graph Embeddings. - Hanchen Wang 002; Jean Kaddour; Shengchao Liu; Jian Tang 005; Joan Lasenby; Qi Liu 049 (NeurIPS, 2023)

Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. - Aengus Lynch; Gbètondji J.-S. Dovonon; Jean Kaddour; Ricardo Silva 001 (CoRR, 2023)

The MiniPile Challenge for Data-Efficient Language Models. - Jean Kaddour (CoRR, 2023)

TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models. - Yuwei Yin; Jean Kaddour; Xiang Zhang; Yixin Nie; Zhenguang Liu; Lingpeng Kong; Qi Liu 049 (CoRR, 2023)

Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. - Sunny Sanyal; Jean Kaddour; Abhishek Kumar; Sujay Sanghavi (CoRR, 2023)

Challenges and Applications of Large Language Models. - Jean Kaddour; Joshua Harris; Maximilian Mozes; Herbie Bradley; Roberta Raileanu; Robert McHardy (CoRR, 2023)

Text Data Augmentation in Low-Resource Settings via Fine-Tuning of Large Language Models. - Jean Kaddour; Qi Liu 049 (CoRR, 2023)

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions. - Terry Yue Zhuo; Minh Chien Vu; Jenny Chim; Han Hu; Wenhao Yu 002; Ratnadira Widyasari; Imam Nur Bani Yusuf; Haolan Zhan; Junda He; Indraneil Paul; Simon Brunner; Chen Gong 005; et al. (CoRR, 2024)

Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models. - Georgy Tyukin; Gbètondji J.-S. Dovonon; Jean Kaddour; Pasquale Minervini (CoRR, 2024)

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions. - Terry Yue Zhuo; Minh Chien Vu; Jenny Chim; Han Hu; Wenhao Yu 002; Ratnadira Widyasari; Imam Nur Bani Yusuf; Haolan Zhan; Junda He; Indraneil Paul; Simon Brunner; Chen Gong 005; et al. (ICLR, 2025)

Are We Done with MMLU? - Aryo Pradipta Gema; Joshua Ong Jun Leang; Giwon Hong; Alessio Devoto; Alberto Carlo Maria Mancino; Rohit Saxena; Xuanli He; Yu Zhao 043; Xiaotang Du; Mohammad Reza Ghasemi Madani; Claire Barale; Robert McHardy; et al. (NAACL (Long Papers), 2025)

REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards. - Zafir Stojanovski; Oliver Stanley; Joe Sharratt; Richard Jones; Abdulhakeem Adefioye; Jean Kaddour; Andreas Köpf (CoRR, 2025)