Learning Graphical Models
Structured Prediction with Stronger Consistency Guarantees
In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.
Causal Discovery in Semi-Stationary Time Series
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge.
Pairwise Causality Guided Transformers for Event Sequences
Such statements embed the notion of pairwise causality and can be better paraphrased as'occurrences of event type Z
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In Reinforcement Learning (RL), an agent engages with an environment in a sequential manner.
Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely
Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely
