Kuang-Huei Lee, Ofir Nachum, Sherry Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch Multi-Game Decision Transformers, preprint 2022.
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton, Program Synthesis with Large Language Models, preprint 2021.
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski, Model-Based Reinforcement Learning for Atari, spotlight at ICRL 2020 (5% acceptance rate).
Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, Lukasz Kaiser, Wojciech Gajewski, Jonni Kanerva, Henryk Michalewski, Green Transformers: Sparse is Enough, NeurIPS 2021.
Sebastian Jaszczur, Michał Łuszczyk, Henryk Michalewski, Neural heuristics for SAT solving, poster and oral presentation at the Representation Learning on Graphs and Manifold Workshop at ICLR 2019.
Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, Cezary Kaliszyk, Josef Urban, Towards finding longer proofs, TABLEAUX 2021.
Pierre Pradic, 2016-2019, joint PhD program with the École normale supérieure de Lyon, thesis co-supervisied by Colin Riba; main topic: reverse mathematics of algorithms. Thesis defended at ENS in June 2020.
Błażej Osiński, 2017-2021, a former Googler, main topic: machine learning and robotics, currently working as a researcher at Lyft.
machine learning: a course run jointly with Jan Lasek and Piotr Migdał from deepsense.ai for consultants of the Boston Consulting Group, including a kaggle-style machine learning comptetition organized on a machine learning platform neptune.ml
optimization: a novel course at the University of Warsaw on linear optimization combining precise exposition of theory and practical classes in programming
advanced courses at the University of Warsaw: practical machine learning run jointly with with deepsense.ai, computer aided verification, logic and type theory - programming in Coq, artificial intelligence and games (a research seminar, which led to 2 papers on optimization), automata on infinite structures, mu-calculus, information theory, algorithmic aspects of game theory, functional analysis, descriptive set theory, tropical geometry and linear optimization