391 citations · 881 across the 63 of their papers we have counts for
67 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,…
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…
CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
Arda Sarp Yenicesu, Furkan B. Mutlu, Suleyman S. Kozat +1
The utilization of the experience replay mechanism enables agents to effectively leverage their experiences on several occasions. In previous studies, the sampling probability of t…
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…
AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking
Mehmet Y. Turali, Mehmet E. Lorasdagi, Ali T. Koc +1
We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature sele…