Directed Networks
Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order.
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.
When Y our AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback Leon Lang University of Amsterdam Davis Foote
Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is
PAC Learning Linear Thresholds from Label Proportions
The goal is to train a good instance classifier.
Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng Kevin Bello
These results are then generalized to general models and likelihoods, where the same claims hold.
