Power, Sam
- Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
2022/10/21 by Lionel Riou-Durand, Pavel Sountsov, Riou-Durand, Lionel +7 · 3 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference
- Prediction-Centric Uncertainty Quantification via MMD
2024/10/15 by Zheyang Shen, Jeremias Knoblauch, Shen, Zheyang +5 · 5 citations
Engineering · #FOS: Computer and information sciences #Fault Detection and Control Systems #Methodology (stat.ME)
- PDMP Monte Carlo methods for piecewise-smooth densities
2021/11/10 by Augustin Chevallier, Sam Power, Chevallier, Augustin +5 · 2 citations
Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Bayesian Methods and Mixture Models #Statistical Methods and Bayesian Inference
- A New Proof of Sub-Gaussian Norm Concentration Inequality
2025/03/18 by Zishun Liu, Sam Power, Liu, Zishun +3 · 1 voice · 1 citation
#math.PR #math.ST
- Distributional Training Data Attribution: What do Influence Functions Sample?
2025/06/15 by Mlodozeniec, Bruno, Reid, Isaac, Power, Sam +4 · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Analysis of Multiple-try Metropolis via Poincaré inequalities
2025/04/25 by Caprio, Rocco, Power, Sam, Wang, Andi Q. · 1 citation
#Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Probability (math.PR)
- Weak Poincaré inequality comparisons for ideal and hybrid slice sampling
2024/02/21 by Power, Sam, Rudolf, Daniel, Sprungk, Björn +1 · 1 citation
#60J22 #65C05 #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Probability (math.PR)