Lukasik, Michal

11 publications

ICLR 2025 Better Autoregressive Regression with LLMs via Regression-Aware Fine-Tuning Michal Lukasik, Zhao Meng, Harikrishna Narasimhan, Yin-Wen Chang, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar
ICML 2025 Bipartite Ranking from Multiple Labels: On Loss Versus Label Aggregation Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, Mohammadhossein Bateni, Sanjiv Kumar
ICLR 2024 On Bias-Variance Alignment in Deep Models Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar
ICLR 2024 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar
TMLR 2024 What Do Larger Image Classifiers Memorise? Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar
NeurIPS 2023 ResMem: Learn What You Can and Memorize the REST Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Rawat, Manzil Zaheer, Aditya K Menon, Sanjiv Kumar
UAI 2023 Robust Distillation for Worst-Class Performance: On the Interplay Between Teacher and Student Objectives Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon
NeurIPSW 2023 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar
TMLR 2022 Teacher’s Pet: Understanding and Mitigating Biases in Distillation Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar
ICML 2020 Does Label Smoothing Mitigate Label Noise? Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar
AAAI 2016 Convolution Kernels for Discriminative Learning from Streaming Text Michal Lukasik, Trevor Cohn