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Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
This view enables a structured variational approximation capturing dependencies across variables in the model.
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado P. van Hasselt, David Silver
The goal of reinforcement learning algorithms is to estimate and/or optimise the value function.
ANear-OptimalBest-of-Both-WorldsAlgorithm forOnlineLearningwithFeedbackGraphs
We present a computationally efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both stochastic and adversarial environments. The bound against oblivious adversaries is O( ฮฑT), where T is the time horizon andฮฑ is the independence number of the feedback graph.
Debiasing Synthetic Data Generated by Deep Generative Models
While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions.
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu
Neural Information Processing Systems http://nips.cc/
Pareto Multi-Task Learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, Sam Kwong
Theproposed algorithm first formulates a multi-task learning problem as a multiobjective optimization problem, and then decomposes the multiobjective optimization problem into a set of constrained subproblems with different trade-off preferences.