1 citations · 1 across the 9 of their papers we have counts for
11 papers
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…
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization
Mustafa E. Aydın, Arda Fazla, Suleyman S. Kozat
We investigate nonlinear prediction/regression in an online setting and introduce a hybrid model that effectively mitigates, via a joint mechanism through a state space formulation…
Actor Prioritized Experience Replay
Baturay Saglam, Furkan B. Mutlu, Dogan C. Cicek +1
A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probabilit…
Optimal Tracking in Prediction with Expert Advice
Hakan Gokcesu, Suleyman S. Kozat
We study the prediction with expert advice setting, where the aim is to produce a decision by combining the decisions generated by a set of experts, e.g., independently running alg…