A probabilistic SIP network simulation system. - Baris Kurt; Çagatay Yildiz; Taha Yusuf Ceritli; Mehmet Yamac; Murat Semerci; Bülent Sankur; Ali Taylan Cemgil (SIU, 2016)

Attack detection in VOIP networks using Bayesian multiple change-point models. - Çagatay Yildiz; Taha Yusuf Ceritli; Baris Kurt; Bülent Sankur; Ali Taylan Cemgil (SIU, 2016)

A Bayesian change point model for epileptic seizure detection. - Çagatay Yildiz; Haluk O. Bingol; Gulcin Irim-Celik; Berrin Aktekin; Canan Aykut-Bingöl (SIU, 2017)

Learning unknown ODE models with Gaussian processes. - Markus Heinonen; Çagatay Yildiz; Henrik Mannerström; Jukka Intosalmi; Harri Lähdesmäki (ICML, 2018)

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization. - Umut Simsekli; Çagatay Yildiz; Thanh Huy Nguyen 001; A. Taylan Cemgil; Gaël Richard (ICML, 2018)

Learning stochastic differential equations with Gaussian Processes without Gradient Matching. - Çagatay Yildiz; Markus Heinonen; Jukka Intosalmi; Henrik Mannerström; Harri Lähdesmäki (MLSP, 2018)

A Bayesian change point model for detecting SIP-based DDoS attacks. - Baris Kurt; Çagatay Yildiz; Taha Yusuf Ceritli; Bülent Sankur; Ali Taylan Cemgil (Digit. Signal Process., 2018)

A real-time SIP network simulation and monitoring system. - Çagatay Yildiz; Baris Kurt; Taha Yusuf Ceritli; Bülent Sankur; Ali Taylan Cemgil (SoftwareX, 2018)

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (NeurIPS, 2019)

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (CoRR, 2019)

Continuous-time Model-based Reinforcement Learning. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICML, 2021)

Bayesian inference of ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (CoRR, 2021)

Learning interacting dynamical systems with latent Gaussian process ODEs. - Çagatay Yildiz; Melih Kandemir; Barbara Rakitsch (NeurIPS, 2022)

Variational multiple shooting for Bayesian ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (UAI, 2022)

Differential Equations for Machine Learning. - Çagatay Yildiz (2022)

Latent Neural ODEs with Sparse Bayesian Multiple Shooting. - Valerii Iakovlev; Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICLR, 2023)

Modulated Neural ODEs. - Ilze Amanda Auzina; Çagatay Yildiz; Sara Magliacane; Matthias Bethge; Efstratios Gavves (NeurIPS, 2023)

Invariant Neural Ordinary Differential Equations. - Ilze Amanda Auzina; Çagatay Yildiz; Sara Magliacane; Matthias Bethge; Efstratios Gavves (CoRR, 2023)

Disentangled Continual Learning: Separating Memory Edits from Model Updates. - Sebastian Dziadzio; Çagatay Yildiz; Gido M. van de Ven; Tomasz Trzcinski; Tinne Tuytelaars; Matthias Bethge (CoRR, 2023)

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization. - Sebastian Dziadzio; Çagatay Yildiz; Gido M. van de Ven; Tomasz Trzcinski; Tinne Tuytelaars; Matthias Bethge (CoLLAs, 2024)

Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? - Firat Öncel; Matthias Bethge; Beyza Ermis; Mirco Ravanelli; Cem Subakan; Çagatay Yildiz (EMNLP, 2024)

Investigating Continual Pretraining in Large Language Models: Insights and Implications. - Çagatay Yildiz; Nishaanth Kanna Ravichandran; Prishruit Punia; Matthias Bethge; Beyza Ermis (CoRR, 2024)

Identifying latent state transition in non-linear dynamical systems. - Çaglar Hizli; Çagatay Yildiz; Matthias Bethge; St John; Pekka Marttinen (CoRR, 2024)

Identifying latent state transitions in non-linear dynamical systems. - Çaglar Hizli; Çagatay Yildiz; Matthias Bethge; S. T. John; Pekka Marttinen (ICLR, 2025)

Investigating Continual Pretraining in Large Language Models: Insights and Implications. - Çagatay Yildiz; Nishaanth Kanna Ravichandran; Nitin Sharma; Matthias Bethge; Beyza Ermis (Trans. Mach. Learn. Res., 2025)

Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning. - David Leeftink; Çagatay Yildiz; Steffen Ridderbusch; Max Hinne; Marcel van Gerven (CoRR, 2025)

A Reproduction Study: The Kernel PCA Interpretation of Self-Attention Fails Under Scrutiny. - Karahan Saritas; Çagatay Yildiz (CoRR, 2025)

Object-level Self-Distillation for Vision Pretraining. - Çaglar Hizli; Çagatay Yildiz; Pekka Marttinen (CoRR, 2025)

Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMs. - Atahan Özer; Çagatay Yildiz (CoRR, 2025)

Beyond Benchmarks: A Novel Framework for Domain-Specific LLM Evaluation and Knowledge Mapping. - Nitin Sharma; Thomas Wolfers; Çagatay Yildiz (CoRR, 2025)