4,260 research outputs found
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Citizen science projects are successful at gathering rich datasets for
various applications. However, the data collected by citizen scientists are
often biased --- in particular, aligned more with the citizens' preferences
than with scientific objectives. We propose the Shift Compensation Network
(SCN), an end-to-end learning scheme which learns the shift from the scientific
objectives to the biased data while compensating for the shift by re-weighting
the training data. Applied to bird observational data from the citizen science
project eBird, we demonstrate how SCN quantifies the data distribution shift
and outperforms supervised learning models that do not address the data bias.
Compared with competing models in the context of covariate shift, we further
demonstrate the advantage of SCN in both its effectiveness and its capability
of handling massive high-dimensional data
Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning
In Verification and in (optimal) AI Planning, a successful method is to
formulate the application as boolean satisfiability (SAT), and solve it with
state-of-the-art DPLL-based procedures. There is a lack of understanding of why
this works so well. Focussing on the Planning context, we identify a form of
problem structure concerned with the symmetrical or asymmetrical nature of the
cost of achieving the individual planning goals. We quantify this sort of
structure with a simple numeric parameter called AsymRatio, ranging between 0
and 1. We run experiments in 10 benchmark domains from the International
Planning Competitions since 2000; we show that AsymRatio is a good indicator of
SAT solver performance in 8 of these domains. We then examine carefully crafted
synthetic planning domains that allow control of the amount of structure, and
that are clean enough for a rigorous analysis of the combinatorial search
space. The domains are parameterized by size, and by the amount of structure.
The CNFs we examine are unsatisfiable, encoding one planning step less than the
length of the optimal plan. We prove upper and lower bounds on the size of the
best possible DPLL refutations, under different settings of the amount of
structure, as a function of size. We also identify the best possible sets of
branching variables (backdoors). With minimum AsymRatio, we prove exponential
lower bounds, and identify minimal backdoors of size linear in the number of
variables. With maximum AsymRatio, we identify logarithmic DPLL refutations
(and backdoors), showing a doubly exponential gap between the two structural
extreme cases. The reasons for this behavior -- the proof arguments --
illuminate the prototypical patterns of structure causing the empirical
behavior observed in the competition benchmarks
XOR-Sampling for Network Design with Correlated Stochastic Events
Many network optimization problems can be formulated as stochastic network
design problems in which edges are present or absent stochastically.
Furthermore, protective actions can guarantee that edges will remain present.
We consider the problem of finding the optimal protection strategy under a
budget limit in order to maximize some connectivity measurements of the
network. Previous approaches rely on the assumption that edges are independent.
In this paper, we consider a more realistic setting where multiple edges are
not independent due to natural disasters or regional events that make the
states of multiple edges stochastically correlated. We use Markov Random Fields
to model the correlation and define a new stochastic network design framework.
We provide a novel algorithm based on Sample Average Approximation (SAA)
coupled with a Gibbs or XOR sampler. The experimental results on real road
network data show that the policies produced by SAA with the XOR sampler have
higher quality and lower variance compared to SAA with Gibbs sampler.Comment: In Proceedings of the Twenty-sixth International Joint Conference on
Artificial Intelligence (IJCAI-17). The first two authors contribute equall
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall
As providĂȘncias cautelares e o "princĂpio da precaução": ecos da jurisprudĂȘncia
As providĂȘncias cautelares e o "princĂpio da precaução": ecos da jurisprudĂȘnci
Treatment effects of selection behavior in managed care plans : evidence from Medicaid
This paper tests whether capitated payments to Medicaid managed care plans induce to plansâ strategic undercutting of treatment for specific diagnostic groups. I focus on treatment (measured by length of stay and cost) in acute care hospitals in Massachusetts. I use a âdifferences-in-differences-in-differencesâ ap- proach, where the third differences compare treatment patterns between managed care plans that receive capitated payments with those that do not. I find that the first reduce treatment significantly more to mental health patients than to patients in other disease groups, whereas the latter reduce hospital resource use more uniformly across disease groups. These results highlight the importance of using payment mechanisms in public programs that reflect the variability in costs of beneficiaries.info:eu-repo/semantics/publishedVersio
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