Melih Kandemir
14 papers · 2015–2026 · 9 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (40) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Keyword Pioneer
🧬
Topic Evolution
📈
Trend Setter
🚀
Conference Pioneer
🌈
Renaissance Researcher
(6)
💎
Century Club · Bronze
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Keyword Collector
(71)
Top co-authors
Research topics
Machine Learning
(12)
Artificial Intelligence
(8)
Deep Learning
(6)
Learning Types · Machine Learning
(6)
Optimization & Theory
(6)
Bayesian & Probabilistic · Artificial Intelligence
(5)
Learning Types · Deep Learning
(4)
Bayesian & Probabilistic · Machine Learning
(3)
Core AI
(3)
Core Methods
(3)
Computer Vision
(2)
Mathematics & Optimization
(2)
Learning Paradigms
(2)
Optimization
(2)
Data Science & Analytics
(1)
Keywords
bayesian inference
(6)
gaussian process
(4)
variational inference
(3)
uncertainty quantification
(3)
stochastic differential equation
(2)
bayesian neural network
(2)
evidential deep learning
(2)
out-of-distribution detection
(2)
neural network
(2)
time series
(2)
hierarchical bayesian
(1)
regret minimization
(1)
weakly supervised learning
(1)
offline reinforcement learning
(1)
image classification
(1)
epistemic uncertainty
(1)
model-based reinforcement learning
(1)
dirichlet process
(1)
episodic memory
(1)
message passing
(1)
Papers
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
UAI 2026
Continual learning of multi-modal dynamics with external memory
L4DC 2024
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
NeurIPS 2024
Estimation of Counterfactual Interventions under Uncertainties
ACML 2023
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
NeurIPS 2023
Evidential Turing Processes
ICLR 2022
Learning interacting dynamical systems with latent Gaussian process ODEs
NeurIPS 2022
Traversing Time with Multi-Resolution Gaussian Process State-Space Models
L4DC 2022
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
AISTATS 2021
Deep Active Learning with Adaptive Acquisition
IJCAI 2019
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
UAI 2019
Evidential Deep Learning to Quantify Classification Uncertainty
NeurIPS 2018
Variational Bayesian Multiple Instance Learning With Gaussian Processes
CVPR 2017
Asymmetric Transfer Learning with Deep Gaussian Processes
ICML 2015