Publication
Measuring the relative effect of factors affecting species distribution model predictions
Publications associées (35)
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...
2022Sampling-Based AQP in Modern Analytical Engines
Anastasia Ailamaki, Viktor Sanca
As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...
ACM2022Spatial and temporal heterogeneity of methane ebullition in lowland headwater streams and the impact on sampling design
Headwater streams are known sources of methane (CH4) to the atmosphere, but their contribution to global scale budgets remains poorly constrained. While efforts have been made to better understand diffusive fluxes of CH4 in streams, much less attention has ...
2021Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps
Michele Ceriotti, Gareth Aneurin Tribello, Federico Giberti
Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...
AMER CHEMICAL SOC2021Practical issues with modeling extreme Brazilian rainfall
Anthony Christopher Davison, Isolde Santos Previdelli, Paulo Vitor Da Costa Pereira
Accurately quantifying extreme rainfall is important for the design of hydraulic structures, for flood mapping and zoning and for disaster management. In order to produce maps of estimates of 25-year rainfall return levels in Brazil, we selected 893 shorte ...
BRAZILIAN STATISTICAL ASSOCIATION2021Rethinking Sampling in Parallel MRI: A Data-Driven Approach
Volkan Cevher, Baran Gözcü, Thomas Sanchez
In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...
IEEE2019Retroactive Packet Sampling for Traffic Receipts
Pavlos Nikolopoulos, Adrian Perrig, Christos Pappas
Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this syst ...
ACM2019Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments
Andreas Pautz, Mathieu Hursin, Dimitri Rochman, Daniel Jerôme Siefman
Developments in data assimilation theory allow to adjust integral parameters and cross sections with stochastic sampling. This work investigates how two stochastic methods, MOCABA and BMC, perform relative to a sensitivity-based methodology called GLLS. St ...
SPRINGER HEIDELBERG2018Random sampling of bandlimited signals on graphs
Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay
We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...
Elsevier2018Safe Adaptive Importance Sampling
Martin Jaggi, Sebastian Urban Stich, Anant Raj
Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...
2017