26,495 research outputs found
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Federated learning is an emerging machine learning paradigm that enables
clients to train collaboratively without exchanging local data. The clients
participating in the training process have a crucial impact on the convergence
rate, learning efficiency, and model generalization. In this work, we propose
FilFL, a new approach to optimizing client participation and training by
introducing client filtering. FilFL periodically filters the available clients
to identify a subset that maximizes a combinatorial objective function using an
efficient greedy filtering algorithm. From this filtered-in subset, clients are
then selected for the training process. We provide a thorough analysis of FilFL
convergence in a heterogeneous setting and evaluate its performance across
diverse vision and language tasks and realistic federated scenarios with
time-varying client availability. Our empirical results demonstrate several
benefits of our approach, including improved learning efficiency, faster
convergence, and up to 10 percentage points higher test accuracy compared to
scenarios where client filtering is not utilized
Smoothness for Simultaneous Composition of Mechanisms with Admission
We study social welfare of learning outcomes in mechanisms with admission. In
our repeated game there are bidders and mechanisms, and in each round
each mechanism is available for each bidder only with a certain probability.
Our scenario is an elementary case of simple mechanism design with incomplete
information, where availabilities are bidder types. It captures natural
applications in online markets with limited supply and can be used to model
access of unreliable channels in wireless networks.
If mechanisms satisfy a smoothness guarantee, existing results show that
learning outcomes recover a significant fraction of the optimal social welfare.
These approaches, however, have serious drawbacks in terms of plausibility and
computational complexity. Also, the guarantees apply only when availabilities
are stochastically independent among bidders.
In contrast, we propose an alternative approach where each bidder uses a
single no-regret learning algorithm and applies it in all rounds. This results
in what we call availability-oblivious coarse correlated equilibria. It
exponentially decreases the learning burden, simplifies implementation (e.g.,
as a method for channel access in wireless devices), and thereby addresses some
of the concerns about Bayes-Nash equilibria and learning outcomes in Bayesian
settings. Our main results are general composition theorems for smooth
mechanisms when valuation functions of bidders are lattice-submodular. They
rely on an interesting connection to the notion of correlation gap of
submodular functions over product lattices.Comment: Full version of WINE 2016 pape
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Graph networks are a new machine learning (ML) paradigm that supports both
relational reasoning and combinatorial generalization. Here, we develop
universal MatErials Graph Network (MEGNet) models for accurate property
prediction in both molecules and crystals. We demonstrate that the MEGNet
models outperform prior ML models such as the SchNet in 11 out of 13 properties
of the QM9 molecule data set. Similarly, we show that MEGNet models trained on
crystals in the Materials Project substantially outperform prior
ML models in the prediction of the formation energies, band gaps and elastic
moduli of crystals, achieving better than DFT accuracy over a much larger data
set. We present two new strategies to address data limitations common in
materials science and chemistry. First, we demonstrate a physically-intuitive
approach to unify four separate molecular MEGNet models for the internal energy
at 0 K and room temperature, enthalpy and Gibbs free energy into a single free
energy MEGNet model by incorporating the temperature, pressure and entropy as
global state inputs. Second, we show that the learned element embeddings in
MEGNet models encode periodic chemical trends and can be transfer-learned from
a property model trained on a larger data set (formation energies) to improve
property models with smaller amounts of data (band gaps and elastic moduli)
SimCrime: A Spatial Microsimulation Model for the Analysing of Crime in Leeds.
This Working Paper is a part of PhD thesis 'Modelling Crime: A Spatial Microsimulation Approach' which aims to investigate the potential of spatial microsimulation for modelling crime. This Working Paper presents SimCrime, a static spatial microsimulation model for crime in Leeds. It is designed to estimate the likelihood of being a victim of crime and crime rates at the small area level in Leeds and to answer what-if questions about the effects of changes in the demographic and socio-economic characteristics of the future population. The model is based on individual microdata. Specifically, SimCrime combines individual microdata from the British Crime Survey (BCS) for which location data is only at the scale of large areas, with census statistics for smaller areas to create synthetic microdata estimates for output areas ?(OAs) in Leeds using a simulated annealing method. The new microdata dataset includes all the attributes from the original datasets. This allows variables such as crime victimisation from the BCS to be directly estimated for OAs
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