Publications related to Unbiased Monte Carlo cluster updates with autoregressive neural networks

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

TAYLOR & FRANCIS INC2023

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Yuejiang Liu

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

EPFL2023

Ride-hail vehicle routing (RIVER) as a congestion game

Kenan Zhang

The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-hail market form a dynamic routing strategy according to the expected reward in each zone of the market. We model this decision-making problem as a Markov decision process (MDP), ...

2023

Efficient ensemble summaries are inversely related to visual crowding

Michael Herzog, David Pascucci, Yury Markov, Natalia Tiurina

Visual crowding is the inability to perceive properly peripheral stimuli within clutter. Previous work has shown that crowding is affected by perceptual grouping: when the flankers do not group with the target, crowding decreases, leading to uncrowding. Ty ...

2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Monte Carlo Estimators for Differential Light Transport

Wenzel Alban Jakob, Tizian Lucien Zeltner, Sébastien Nicolas Speierer

Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased metho ...

ASSOC COMPUTING MACHINERY2021

Temperature-Dependent Electronic Ground-State Charge Transfer in van der Waals Heterostructures

Haiyuan Wang, Mariana Rossi Carvalho, Dmitrii Maksimov

Electronic charge rearrangement between components of a heterostructure is the fundamental principle to reach the electronic ground state. It is acknowledged that the density of state distribution of the components governs the amount of charge transfer, bu ...

2021

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Multivariate extremes over a random number of observations

Simone Padoan, Stefano Rizzelli

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

WILEY2020

Study of B -> ppi pi

Olivier Schneider, Mingkui Wang, Chao Wang, Tagir Aushev, Sun Hee Kim, Donghyun Kim, Xiao Wang, Lei Li

Using a data sample of 772 x 10(6) B (B) over bar pairs collected on the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric energy e(+)e(-) collider, we report the observation of B-0 -> p (p) over bar pi(+)pi(-) and the first observation ...

AMER PHYSICAL SOC2020

Unbiased Monte Carlo cluster updates with autoregressive neural networks

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Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Ride-hail vehicle routing (RIVER) as a congestion game

Efficient ensemble summaries are inversely related to visual crowding

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Monte Carlo Estimators for Differential Light Transport

Temperature-Dependent Electronic Ground-State Charge Transfer in van der Waals Heterostructures

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Multivariate extremes over a random number of observations

Study of B -> ppi pi

Ride-hail vehicle routing (RIVER) as a congestion game

Efficient ensemble summaries are inversely related to visual crowding

Multivariate extremes over a random number of observations

Monte Carlo Estimators for Differential Light Transport

Study of B -> ppi pi

Temperature-Dependent Electronic Ground-State Charge Transfer in van der Waals Heterostructures

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Learning Robust and Adaptive Representations: from Interactions, for Interactions

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples