Berker Demirel

I am a PhD candidate at Causal Learning and Artificial Intelligence Lab, IST Austria supervised by Francesco Locatello. My research interests lie in the fields of domain generalization and out-of-distribution generalization, where statistical and information-theoretical interpretations of the models are emphasized.

Prior to beginning my PhD, I earned a master's degree from Sabanci University. During this time, I was a member of the VPA Lab and was under the supervision of Asst. Prof. Huseyin Ozkan and Asst. Prof. Erchan Aptoula. The primary focus of my master's studies was on group activity recognition and domain generalization.

In the summer of 2019, I completed a summer internship at Erasmus University Rotterdam.

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We present a novel technique -Look Around and Find Out (LAFO)- for OOD detection, which computes the angles between the feature representation and its projection to the decision boundaries, relative to the mean of ID-features. LAFO is model-agnostic, hyperparameter-free, and efficient, scaling linearly with the number of ID-classes. Therefore, it can flexibly be combined with various architectures without the need for additional tuning. In addition, the scale-invariant property of LAFO allows for straightforward aggregation of confidence scores from multiple pre-trained models, improving ensemble performance for OOD detection.

We propose a modular approach to tackle performative label shift for pretrained backbones. This additional module serves two main use cases: (i) adapting the model for performative shift and (ii) making informed model selection by anticipating future distributions caused by multiple models. For the first use case, it allows pre-shift adaptation for networks to better handle performative shifts. For the second, it can anticipate a model's robustness to performative shifts, enabling more informative model selection. Thanks to our modeling approach capturing the inherent relationship between the sufficient statistic and the performative shift, it is not coupled with the specific architecture it is trained with. Therefore, it can seamlessly combine with various pretrained networks, allowing zero-shot transfer during model updates.

We show that Group Activity Recognition Problem can be formulated using Attention Pooling mechanism and can perform on par with the other state-of-the-art methods even with a single RGB frame. Moreover, we manually reannotated the flawed instances in the Volleyball Dataset, which is one of the widely used datasets in Group Activity Recognition.

We propose an additive disentanglement of domain specific and domain invariant features for the domain generalization problem. Unlike prior work, we demonstrate the potential benefits of utilizing domain specific features along with domain invariant ones. Moreover, we introduce a new data augmentation technique to enhance the generalization capacity of the architecture, where samples from different domains are mixed within the latent space.

We developed a ranking algorithm that uses metrics of degree 1/2/3, closeness centrality, clustering coefficient, and page rank to rank the nodes in a graph. Then, we applied greedy coloring to the graph using the ranking as a guide, which resulted in significantly better colorings. Furthermore, we tried to extend this idea using a model-free policy based reinforcement learning algorithm while parallelizing C++ backend using OpenMP.

I re-implemented Andrej Karpathy's nanoGPT. It is designed to be simpler and easier to update. Performance evaluation is performed on Tiny Shakespeare Dataset and results can be found in the repository.

I implemented Uniform Manifold Approximation and Projection (UMAP) algorithm in Python from scratch and performed experiments on MNIST and Load Digits datasets.

We implemented Direction-Optimized BFS using OpenMP and CUDA.

Website template credits to Jon Barron.