Evaluation of Features for Author Name Disambiguation Using Linear Support Vector Machines. - Piotr Jan Dendek; Lukasz Bolikowski; Michal Lukasik (Document Analysis Systems, 2012)

Generic Framework for Simulation of Cognitive Systems: A Case Study of Color Category Boundaries. - Dariusz Plewczynski; Michal Lukasik; Konrad Kurdej; Julian Zubek; Franciszek Rakowski; Joanna Raczaszek-Leonardi (ICMMI, 2013)

Multiobjective Differential Evolution: A Comparative Study on Benchmark Problems. - Indrajit Saha; Ujjwal Maulik; Michal Lukasik; Dariusz Plewczynski (ICMMI, 2013)

Threshold ML-KNN: Statistical Evaluation on Multiple Benchmarks. - Michal Lukasik; Marcin Sydow (IIS, 2013)

Comparing Hierarchical Mathematical Document Clustering against the Mathematics Subject Classification Tree. - Tomasz Kusmierczyk; Michal Lukasik; Lukasz Bolikowski; Hung Son Nguyen (Intelligent Tools for Building a Scientific Information Platform, 2013)

Hierarchical, Multi-label Classification of Scholarly Publications: Modifications of ML-KNN Algorithm. - Michal Lukasik; Tomasz Kusmierczyk; Lukasz Bolikowski; Hung Son Nguyen (Intelligent Tools for Building a Scientific Information Platform, 2013)

Point Process Modelling of Rumour Dynamics in Social Media. - Michal Lukasik; Trevor Cohn; Kalina Bontcheva (ACL (2), 2015)

Classifying Tweet Level Judgements of Rumours in Social Media. - Michal Lukasik; Trevor Cohn; Kalina Bontcheva (EMNLP, 2015)

Modeling Tweet Arrival Times using Log-Gaussian Cox Processes. - Michal Lukasik; P. K. Srijith; Trevor Cohn; Kalina Bontcheva (EMNLP, 2015)

Estimating collective judgement of rumours in social media. - Michal Lukasik; Trevor Cohn; Kalina Bontcheva (CoRR, 2015)

Convolution Kernels for Discriminative Learning from Streaming Text. - Michal Lukasik; Trevor Cohn (AAAI, 2016)

Metrics for Evaluation of Word-level Machine Translation Quality Estimation. - Varvara Logacheva; Michal Lukasik; Lucia Specia (ACL (2), 2016)

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter. - Michal Lukasik; P. K. Srijith; Duy Vu; Kalina Bontcheva; Arkaitz Zubiaga; Trevor Cohn (ACL (2), 2016)

Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations. - Arkaitz Zubiaga; Elena Kochkina; Maria Liakata; Rob Procter; Michal Lukasik (COLING, 2016)

Using Gaussian Processes for Rumour Stance Classification in Social Media. - Michal Lukasik; Kalina Bontcheva; Trevor Cohn; Arkaitz Zubiaga; Maria Liakata; Rob Procter (CoRR, 2016)

Computational Approach to Dendritic Spine Taxonomy and Shape Transition Analysis. - Grzegorz Bokota; Marta Magnowska; Tomasz Kusmierczyk; Michal Lukasik; Matylda Roszkowska; Dariusz Plewczynski (Frontiers Comput. Neurosci., 2016)

Longitudinal Modeling of Social Media with Hawkes Process Based on Users and Networks. - P. K. Srijith; Michal Lukasik; Kalina Bontcheva; Trevor Cohn (ASONAM, 2017)

Probabilistic modeling of rumour stance and popularity in social media. - Michal Lukasik (2017)

Content Explorer: Recommending Novel Entities for a Document Writer. - Michal Lukasik; Richard Zens (EMNLP, 2018)

A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. - Sherin Thomas; P. K. Srijith; Michal Lukasik (UMAP, 2018)

Discourse-aware rumour stance classification in social media using sequential classifiers. - Arkaitz Zubiaga; Elena Kochkina; Maria Liakata; Rob Procter; Michal Lukasik; Kalina Bontcheva; Trevor Cohn; Isabelle Augenstein (Inf. Process. Manag., 2018)

Gaussian Processes for Rumour Stance Classification in Social Media. - Michal Lukasik; Kalina Bontcheva; Trevor Cohn; Arkaitz Zubiaga; Maria Liakata; Rob Procter (ACM Trans. Inf. Syst., 2019)

Text Segmentation by Cross Segment Attention. - Michal Lukasik; Boris Dadachev; Kishore Papineni; Gonçalo Simões (EMNLP (1), 2020)

Semantic Label Smoothing for Sequence to Sequence Problems. - Michal Lukasik; Himanshu Jain; Aditya Krishna Menon; Seungyeon Kim 001; Srinadh Bhojanapalli; Felix X. Yu; Sanjiv Kumar (EMNLP (1), 2020)

Does label smoothing mitigate label noise? - Michal Lukasik; Srinadh Bhojanapalli; Aditya Krishna Menon; Sanjiv Kumar (ICML, 2020)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

XR Hackathon Going Online: Lessons Learned from a Case Study with Goethe-Institute. - Wieslaw Kopec; Kinga Skorupska; Anna Jaskulska; Michal Lukasik; Barbara Karpowicz; Julia Paluch; Kinga Kwiatkowska; Daniel Jablonski; Rafal Maslyk (MIDI, 2021)

Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. - Srinadh Bhojanapalli; Ayan Chakrabarti; Himanshu Jain; Sanjiv Kumar; Michal Lukasik; Andreas Veit (CoRR, 2021)

Leveraging redundancy in attention with Reuse Transformers. - Srinadh Bhojanapalli; Ayan Chakrabarti; Andreas Veit; Michal Lukasik; Himanshu Jain; Frederick Liu; Yin-Wen Chang; Sanjiv Kumar (CoRR, 2021)

HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation. - Wittawat Jitkrittum; Michal Lukasik; Ananda Theertha Suresh; Felix X. Yu; Gang Wang (CoRR, 2021)

Hawkes Process Classification through Discriminative Modeling of Text. - Rohan Tondulkar; Manisha Dubey; P. K. Srijith; Michal Lukasik (IJCNN, 2022)

Robust Distillation for Worst-class Performance. - Serena Lutong Wang; Harikrishna Narasimhan; Yichen Zhou; Sara Hooker; Michal Lukasik; Aditya Krishna Menon (CoRR, 2022)

XR Hackathon Going Online: Lessons Learned from a Case Study with Goethe-Institut. - Wieslaw Kopec; Kinga Skorupska; Anna Jaskulska; Michal Lukasik; Barbara Karpowicz; Julia Paluch; Kinga Kwiatkowska; Daniel Jablonski; Rafal Maslyk (CoRR, 2022)

Preserving In-Context Learning ability in Large Language Model Fine-tuning. - Yihan Wang; Si Si; Daliang Li; Michal Lukasik; Felix X. Yu; Cho-Jui Hsieh; Inderjit S. Dhillon; Sanjiv Kumar (CoRR, 2022)

Teacher's pet: understanding and mitigating biases in distillation. - Michal Lukasik; Srinadh Bhojanapalli; Aditya Krishna Menon; Sanjiv Kumar (Trans. Mach. Learn. Res., 2022)

Large Language Models with Controllable Working Memory. - Daliang Li; Ankit Singh Rawat; Manzil Zaheer; Xin Wang 016; Michal Lukasik; Andreas Veit; Felix X. Yu; Sanjiv Kumar (ACL (Findings), 2023)

ResMem: Learn what you can and memorize the rest. - Zitong Yang; Michal Lukasik; Vaishnavh Nagarajan; Zonglin Li; Ankit Singh Rawat; Manzil Zaheer; Aditya Krishna Menon; Sanjiv Kumar (NeurIPS, 2023)

Robust distillation for worst-class performance: on the interplay between teacher and student objectives. - Serena Wang 001; Harikrishna Narasimhan; Yichen Zhou; Sara Hooker; Michal Lukasik; Aditya Krishna Menon (UAI, 2023)

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models. - Lin Chen; Michal Lukasik; Wittawat Jitkrittum; Chong You; Sanjiv Kumar (CoRR, 2023)

Regression Aware Inference with LLMs. - Michal Lukasik; Harikrishna Narasimhan; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (EMNLP (Findings), 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 X. Yu; Cho-Jui Hsieh; Inderjit S. Dhillon; Sanjiv Kumar (ICLR, 2024)

Metric-aware LLM inference. - Michal Lukasik; Harikrishna Narasimhan; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (CoRR, 2024)

What do larger image classifiers memorise? - Michal Lukasik; Vaishnavh Nagarajan; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (Trans. Mach. Learn. Res., 2024)

TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge. - Cheng-Han Chiang; Hung-yi Lee; Michal Lukasik (ACL (1), 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 (ICLR, 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 (CoRR, 2025)