We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables efficient decomposition of sums of high-order tensors. Our analysis builds upon Bhaskara et al.

Harsanyi's theorem shows that when the principals have a

Deep learning constructs are powerful tools for pattern matching and prediction.

Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.

However, many real-world problems inherently have multiple goals, where multi-objective formulations are more natural.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance, it is crucial to guarantee the safety of an agent during training as well as deployment (e.g., a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs.

The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample.

Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation.