94,078 research outputs found
Multiwinner Voting with Fairness Constraints
Multiwinner voting rules are used to select a small representative subset of
candidates or items from a larger set given the preferences of voters. However,
if candidates have sensitive attributes such as gender or ethnicity (when
selecting a committee), or specified types such as political leaning (when
selecting a subset of news items), an algorithm that chooses a subset by
optimizing a multiwinner voting rule may be unbalanced in its selection -- it
may under or over represent a particular gender or political orientation in the
examples above. We introduce an algorithmic framework for multiwinner voting
problems when there is an additional requirement that the selected subset
should be "fair" with respect to a given set of attributes. Our framework
provides the flexibility to (1) specify fairness with respect to multiple,
non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score
function. We study the computational complexity of this constrained multiwinner
voting problem for monotone and submodular score functions and present several
approximation algorithms and matching hardness of approximation results for
various attribute group structure and types of score functions. We also present
simulations that suggest that adding fairness constraints may not affect the
scores significantly when compared to the unconstrained case.Comment: The conference version of this paper appears in IJCAI-ECAI 201
Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
Class prototype construction and matching are core aspects of few-shot action
recognition. Previous methods mainly focus on designing spatiotemporal relation
modeling modules or complex temporal alignment algorithms. Despite the
promising results, they ignored the value of class prototype construction and
matching, leading to unsatisfactory performance in recognizing similar
categories in every task. In this paper, we propose GgHM, a new framework with
Graph-guided Hybrid Matching. Concretely, we learn task-oriented features by
the guidance of a graph neural network during class prototype construction,
optimizing the intra- and inter-class feature correlation explicitly. Next, we
design a hybrid matching strategy, combining frame-level and tuple-level
matching to classify videos with multivariate styles. We additionally propose a
learnable dense temporal modeling module to enhance the video feature temporal
representation to build a more solid foundation for the matching process. GgHM
shows consistent improvements over other challenging baselines on several
few-shot datasets, demonstrating the effectiveness of our method. The code will
be publicly available at https://github.com/jiazheng-xing/GgHM.Comment: Accepted by ICCV202
Node Graph Optimization Using Differentiable Proxies
Graph-based procedural materials are ubiquitous in content production
industries. Procedural models allow the creation of photorealistic materials
with parametric control for flexible editing of appearance. However, designing
a specific material is a time-consuming process in terms of building a model
and fine-tuning parameters. Previous work [Hu et al. 2022; Shi et al. 2020]
introduced material graph optimization frameworks for matching target material
samples. However, these previous methods were limited to optimizing
differentiable functions in the graphs. In this paper, we propose a fully
differentiable framework which enables end-to-end gradient based optimization
of material graphs, even if some functions of the graph are non-differentiable.
We leverage the Differentiable Proxy, a differentiable approximator of a
non-differentiable black-box function. We use our framework to match structure
and appearance of an output material to a target material, through a
multi-stage differentiable optimization. Differentiable Proxies offer a more
general optimization solution to material appearance matching than previous
work
SMART: A statistical framework for optimal design matrix generation with application to fMRI
The general linear model (GLM) is a well established tool for analyzing
functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM
proceed in a massively univariate fashion where the same design matrix is used
for analyzing data from each voxel. A major limitation of this approach is the
locally varying nature of signals of interest as well as associated confounds.
This local variability results in a potentially large bias and uncontrolled
increase in variance for the contrast of interest. The main contributions of
this paper are two fold (1) We develop a statistical framework called SMART
that enables estimation of an optimal design matrix while explicitly
controlling the bias variance decomposition over a set of potential design
matrices and (2) We develop and validate a numerical algorithm for computing
optimal design matrices for general fMRI data sets. The implications of this
framework include the ability to match optimally the magnitude of underlying
signals to their true magnitudes while also matching the "null" signals to zero
size thereby optimizing both the sensitivity and specificity of signal
detection. By enabling the capture of multiple profiles of interest using a
single contrast (as opposed to an F-test) in a way that optimizes for both bias
and variance enables the passing of first level parameter estimates and their
variances to the higher level for group analysis which is not possible using
F-tests. We demonstrate the application of this approach to in vivo
pharmacological fMRI data capturing the acute response to a drug infusion, to
task-evoked, block design fMRI and to the estimation of a haemodynamic response
function (HRF) response in event-related fMRI. Our framework is quite general
and has potentially wide applicability to a variety of disciplines.Comment: 68 pages, 34 figure
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