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Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
Adversarial Conversational Shaping for Intelligent Agents
The recent emergence of deep learning methods has enabled the research community to achieve state-of-the art results in several domains including natural language processing.
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport
One of the key approaches to IL is to define a distance between agent and expert and to find an agent policy that minimizes that distance.
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