Joint Fusion Learning of Multiple Time Series Prediction

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Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

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Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

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Reliable data-driven decision-making through optimal transport

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Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

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EPFL2024

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

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Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...

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Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

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In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

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Ridesourcing has driven a lot of attention in recent years with the expansion of companies like Uber, Lift, and many others around the world. Companies use mobile applications connected through the internet to match drivers and their passengers real-time. ...

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