127,691 research outputs found
Estimation of individual causal effects in network setup for multiple treatments
We study the problem of estimation of Individual Treatment Effects (ITE) in
the context of multiple treatments and networked observational data. Leveraging
the network information, we aim to utilize hidden confounders that may not be
directly accessible in the observed data, thereby enhancing the practical
applicability of the strong ignorability assumption. To achieve this, we first
employ Graph Convolutional Networks (GCN) to learn a shared representation of
the confounders. Then, our approach utilizes separate neural networks to infer
potential outcomes for each treatment. We design a loss function as a weighted
combination of two components: representation loss and Mean Squared Error (MSE)
loss on the factual outcomes. To measure the representation loss, we extend
existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from
the binary treatment setting to the multiple treatments scenario. To validate
the effectiveness of our proposed methodology, we conduct a series of
experiments on the benchmark datasets such as BlogCatalog and Flickr. The
experimental results consistently demonstrate the superior performance of our
models when compared to baseline methods.Comment: 7 pages, accepted at AAAI-GCLR 202
Domain Adaptation: Learning Bounds and Algorithms
This paper addresses the general problem of domain adaptation which arises in
a variety of applications where the distribution of the labeled sample
available somewhat differs from that of the test data. Building on previous
work by Ben-David et al. (2007), we introduce a novel distance between
distributions, discrepancy distance, that is tailored to adaptation problems
with arbitrary loss functions. We give Rademacher complexity bounds for
estimating the discrepancy distance from finite samples for different loss
functions. Using this distance, we derive novel generalization bounds for
domain adaptation for a wide family of loss functions. We also present a series
of novel adaptation bounds for large classes of regularization-based
algorithms, including support vector machines and kernel ridge regression based
on the empirical discrepancy. This motivates our analysis of the problem of
minimizing the empirical discrepancy for various loss functions for which we
also give novel algorithms. We report the results of preliminary experiments
that demonstrate the benefits of our discrepancy minimization algorithms for
domain adaptation.Comment: 12 pages, 4 figure
Kernel-Elastic Autoencoder for Molecular Design
We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised
generative model based on the transformer architecture with enhanced
performance for molecular design. KAE is formulated based on two novel loss
functions: modified maximum mean discrepancy and weighted reconstruction. KAE
addresses the long-standing challenge of achieving valid generation and
accurate reconstruction at the same time. KAE achieves remarkable diversity in
molecule generation while maintaining near-perfect reconstructions on the
independent testing dataset, surpassing previous molecule-generating models.
KAE enables conditional generation and allows for decoding based on beam search
resulting in state-of-the-art performance in constrained optimizations.
Furthermore, KAE can generate molecules conditional to favorable binding
affinities in docking applications as confirmed by AutoDock Vina and Glide
scores, outperforming all existing candidates from the training dataset. Beyond
molecular design, we anticipate KAE could be applied to solve problems by
generation in a wide range of applications
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