Learning Graphical Models
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
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.
