Batuhan Mumcu Özgeçmiş 1983 yılında Erzincan’da dünyaya geldi. Yakın Doğu Üniversitesi Halkla İlişkiler ve Tanıtım Bölümü’nden mezun oldu. 2012 yılında Atılım Üniversitesi Kamu Yönetimi ve Siyaset Bilimi Bölümü’nde yüksek…
Continue readingThe article discusses ACE (Off-Policy Actor-Critic with Causality-Aware Entropy Regularization), a novel reinforcement learning (RL) algorithm. Traditional RL algorithms often overlook the varying significance of distinct primitive behaviors during the…
Continue readingThe article discusses the importance of heteroscedastic predictive uncertainties in Bayesian Neural Networks (BNNs). The authors illustrate how both heteroscedastic aleatoric and epistemic variance can be integrated into the variances…
Continue readingThe paper proposes OmniPred, a framework for training language models as universal regressors over evaluation data from various real-world experiments. This approach, which uses data from Google Vizier, demonstrates that…
Continue readingThe article discusses the problem of prediction with expert advice under bandit feedback. The authors propose a model where the learner can abstain from play, which has no reward or…
Continue readingThe paper introduces FedCQA, a method to answer complex queries on multi-source knowledge graphs (KGs) while preserving privacy. Current methods focus on single KGs and can’t be applied to multiple…
Continue readingThe article introduces ‘latrend’, an R package developed for clustering longitudinal data, which is data gathered from the same subjects over a period of time. This type of clustering allows…
Continue readingThis paper presents a study on Sparse Linear Regression (SLR), a statistical problem of predicting a response vector with a sparse vector and small arbitrary noise. The authors provide evidence…
Continue readingThe paper introduces a method based on Continuous Low Rank Adaptation (CoLoRA) that trains neural networks to predict the evolution of solution fields at new initial conditions and physics parameters….
Continue readingThe paper discusses adversarial training’s robustness-accuracy trade-off problem and proposes a solution. The authors focus on invariance regularization to create adversarially invariant representations without losing discriminative power. They identify two…
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