6 papers
Enhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay
Mehmet Efe Lorasdagi, Dogan Can Cicek, Furkan Burak Mutlu +1
Background: Deep Deterministic Policy Gradient-based reinforcement learning algorithms utilize Actor-Critic architectures, where both networks are typically trained using identical…
Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression
Huseyin Karaca, Suleyman Serdar Kozat
We propose a soft gradient boosting framework for sequential regression that embeds a learnable linear feature transform within the boosting procedure. At each boosting iteration,…
PySAD: A Streaming Anomaly Detection Framework in Python
Selim F. Yilmaz, Suleyman S. Kozat
Streaming anomaly detection requires algorithms that operate under strict constraints: bounded memory, single-pass processing, and constant-time complexity. We present PySAD, a com…
Fitting Multiple Machine Learning Models with Performance Based Clustering
Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc +1
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism…
Binary Feature Mask Optimization for Feature Selection
Mehmet E. Lorasdagi, Mehmet Y. Turali, Suleyman S. Kozat
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framew…
Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting
Aysin Tumay, Mustafa E. Aydin, Ali T. Koc +1
We introduce a novel ensemble approach for feature selection based on hierarchical stacking for non-stationarity and/or a limited number of samples with a large number of features.…