In this seminar, students will explore the Context Tree Weighting (CTW) method [1], a universal lossless technique for sequential data compression and prediction. CTW efficiently balances multiple context models using a weighted averaging approach. It is particularly appreciated for its strong theoretical guarantees, such as redundancy bounds in universal coding, while also demonstrating excellent practical performance in real-world scenarios.

Students will first read and understand the fundamentals of CTW, including its theoretical basis and algorithmic implementation. After grasping the core method, they can choose to delve into either extensions of CTW [2], or its applications, e.g. text/image compression [3, 4] or sequence prediction in various domains.

[1] https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=382012

[2] https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=661523

[3] https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=838152

[4] https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=488318

Prerequisites