666 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
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
CLR-DRNets: Curriculum Learning with Restarts to Solve Visual Combinatorial Games
We introduce a curriculum learning framework for challenging tasks that require a combination of pattern recognition and combinatorial reasoning, such as single-player visual combinatorial games. Our work harnesses Deep Reasoning Nets (DRNets) [Chen et al., 2020], a framework that combines deep learning with constraint reasoning for unsupervised pattern demixing. We propose CLR-DRNets (pronounced Clear-DRNets), a curriculum-learning-with-restarts framework to boost the performance of DRNets. CLR-DRNets incrementally increase the difficulty of the training instances and use restarts, a new model selection method that selects multiple models from the same training trajectory to learn a set of diverse heuristics and apply them at inference time. An enhanced reasoning module is also proposed for CLR-DRNets to improve the ability of reasoning and generalize to unseen instances. We consider Visual Sudoku, i.e., Sudoku with hand-written digits or letters, and Visual Mixed Sudoku, a substantially more challenging task that requires the demixing and completion of two overlapping Visual Sudokus. We propose an enhanced reasoning module for the DRNets framework for encoding these visual games We show how CLR-DRNets considerably outperform DRNets and other approaches on these visual combinatorial games
Graph Value Iteration
In recent years, deep Reinforcement Learning (RL) has been successful in
various combinatorial search domains, such as two-player games and scientific
discovery. However, directly applying deep RL in planning domains is still
challenging. One major difficulty is that without a human-crafted heuristic
function, reward signals remain zero unless the learning framework discovers
any solution plan. Search space becomes \emph{exponentially larger} as the
minimum length of plans grows, which is a serious limitation for planning
instances with a minimum plan length of hundreds to thousands of steps.
Previous learning frameworks that augment graph search with deep neural
networks and extra generated subgoals have achieved success in various
challenging planning domains. However, generating useful subgoals requires
extensive domain knowledge. We propose a domain-independent method that
augments graph search with graph value iteration to solve hard planning
instances that are out of reach for domain-specialized solvers. In particular,
instead of receiving learning signals only from discovered plans, our approach
also learns from failed search attempts where no goal state has been reached.
The graph value iteration component can exploit the graph structure of local
search space and provide more informative learning signals. We also show how we
use a curriculum strategy to smooth the learning process and perform a full
analysis of how graph value iteration scales and enables learning
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