Markov Models
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
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
A notable example that motivates our work is the problem of minimizing costs (or maximizing rewards) in a single-trajectory Markov Decision Process (MDP).
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.
Interactive Visual Reasoning under Uncertainty
One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials.