Learning Graphical Models
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers Siyu Chen Department of Statistics and Data Science, Yale University
In particular, most existing work only theoretically explains how the attention mechanism facilitates ICL under certain data models.
Flipping-based Policy for Chance-Constrained Markov Decision Processes
Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity.
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
Work done while visiting Harvard University.
Appendix A PCMCI Algorithm
The PCMCI algorithm is proposed by Runge et al. [2019], aiming to detect time-lagged causal See Fig.1 for more detail. A simple proof is shown below through Markov assumption ( A2). 3 Figure 2: Partial causal graph for 3-variate time series Fig.2 shows a partial causal graph for a 3-variate time series with Semi-Stationary SCM. However, they may not share the same marginal distribution. Still in Fig.2, based on the definition of homogenous time partition, time partition subset Based on Eq.(12) and Eq.(17), we have: p(X Without loss of generality, we assume T is a multiple of δ all the time. A1-A7 and with an oracle (infinite sample size limit), we have that: null G = G (47) almost surely.
Causal Discovery in Semi-Stationary Time Series
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge.