Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.
problem Small and biased RCT data leads to significant bias when generalizing to broader user base.
method Pseudo-sample matching to expand and match RCT data with observational data.
result 0.41% improvement in profit through pseudo-sample matching.
Bayesian Pseudo Label Selection reduces overfitting in semi-supervised learning.
problem Overfitting in pseudo-label selection for semi-supervised learning.
method BPLS, a Bayesian framework that approximates the posterior predictive of pseudo-samples.
result BPLS outperforms traditional PLS methods, especially in high-dimensional data.
A neural network method estimates densities from characteristic functions.
problem Estimating fixed-horizon probability densities from empirical characteristic functions.
method Data-driven Fourier-mixture neural-network method trained in Fourier space.
result Competitive performance and clear gains on heavy-tailed targets.
Develops a framework for multi-objective learning in diffusion models with limited labeled data.
problem Achieving good trade-offs in multi-objective learning with diffusion models requires a generalist model class with larger capacity than individual tasks.
method Proposes a two-stage training procedure: first fitting specialist models from limited paired data, then distilling them into a generalist model.
result Establishes generalization bounds showing the number of paired samples depends only on specialist model complexity.
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC…
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is determinis…
The application of Stochastic Differential Equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a non-parametric method for estimating the drift and diffusion terms of …
Bayesian framework improves robustness in nonlinear regression models.
problem Measurement error, model misspecification, and distributional misspecification in regression analyses.
method Joint Dirichlet process prior on latent covariate-response distribution, updating with posterior pseudo-samples.
result Improved stability and consistency in estimators under increasing measurement error.
Method transfers knowledge without label overlap, source data, or target architecture consistency.
problem Difficulties in transfer learning due to label mismatch, restricted source data, and specialized target architectures.
method Uses deep generative models in two stages: pseudo pre-training and pseudo semi-supervised learning.
result Outperforms scratch training and knowledge distillation methods.
Method learns statistics of return distributions via neural networks and maximum mean discrepancy.
problem Learning probability distributions in reinforcement learning.
method Maximum mean discrepancy (MMD) for learning unrestricted statistics of return distributions.
result Method outperforms standard distributional RL baselines on Atari games.
Develops a generic approach for stable model distillation.
problem Stability of student models in model distillation when data variability is present.
method Generic approach based on central limit theorem for average loss, multiple testing framework.
result Demonstrates selection of a corpus size for consistent student models across different pseudo samples.
Pseudo-label selection affects semi-supervised learning performance.
problem Selection of pseudo-labeled data impacts semi-supervised learning's generalization performance.
method Embedding pseudo-label selection into decision theory, deriving a novel selection criterion based on posterior predictive.
result BPLS (Bayesian pseudo-label selection) outperforms traditional methods in overfitting-prone data.