Markov Models
In Appendix A we provide heuristic justification for the scaling of the optimal error rate
In Appendix D we provide the proofs for Theorem 7. In Appendix E we include some useful results for the sake of completeness. Informally, we expect that there is one sign flip (i.e., The top left, top right and bottom left figures show the scaling of the minimax rates of GLM (cf. To begin with the analysis of the estimator in Figure 2, the following lemma is a simple, yet key tool for the proof. It establishes the variance of the random gain S . The proof relies on a sort of self-bounding property (cf.
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
It was also extensively studied from the theoretical perspective in the classical, fixed-dimensional and asymptotic regime, e.g., Gyรถrfi et al. [2002, Chapter 27 and
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
We study the problem of learning episodic Markov Decision Processes (MDPs).
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
We study the problem of learning episodic Markov Decision Processes (MDPs).
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
Continual learning agents experience a stream of (related) tasks.
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
In this paper, our goal is to design an enhanced RL agent with a reasoning process for text-based games.
When Counterpoint Meets Chinese Folk Melodies Nan Jiang
Counterpoint is an important concept in Western music theory.
Reconstruction on Trees and Low-Degree Polynomials Frederic Koehler
The study of Markov processes and broadcasting on trees has deep connections to a variety of areas including statistical physics, graphical models, phylogenetic reconstruction, Markov Chain Monte Carlo, and community detection in random graphs. Notably, the celebrated Belief Propagation (BP) algorithm achieves Bayes-optimal performance for the reconstruction problem of predicting the value of the Markov process at the root of the tree from its values at the leaves. Recently, the analysis of low-degree polynomials has emerged as a valuable tool for predicting computational-to-statistical gaps. In this work, we investigate the performance of low-degree polynomials for the reconstruction problem on trees.
