279 research outputs found
Nonparametric Treatment Effect Identification in School Choice
We study identification and estimation of treatment effects in common school
choice settings, under unrestricted heterogeneity in individual potential
outcomes. We propose two notions of identification, corresponding to design-
and sampling-based uncertainty, respectively. We characterize the set of causal
estimands that are identified for a large variety of school choice mechanisms,
including ones that feature both random and non-random tie-breaking; we discuss
their policy implications. We also study the asymptotic behavior of
nonparametric estimators for these causal estimands. Lastly, we connect our
approach to the propensity score approach proposed in Abdulkadiroglu, Angrist,
Narita, and Pathak (2017a, forthcoming), and derive the implicit estimands of
the latter approach, under fully heterogeneous treatment effects.Comment: Presented at SOLE 202
Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators
Artiļ¬cial Neural Networks (ANNs) can be viewed as \emph{nonlinear sieves} that can approximate complex functions of high dimensional variables more eļ¬ectively than linear sieves. We investigate the computational performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two eļ¬icient procedures for estimation and inference on a weighted average derivative (WAD): an orthogonalized plug-in with optimally-weighted sieve minimum distance (OP-OSMD) procedure and a sieve eļ¬icient score (ES) procedure. Both estimators for WAD use ANN sieves to approximate the unknown NPIV function and are root-n asymptotically normal and ļ¬rst-order equivalent. We provide a detailed practitionerās recipe for implementing both eļ¬icient procedures. This involves the choice of tuning parameters for the unknown NPIV, the conditional expectations and the optimal weighting function that are present in both procedures but also the choice of tuning parameters for the unknown Riesz representer in the ES procedure. We compare their ļ¬nite-sample performances in various simulation designs that involve smooth NPIV function of up to 13 continuous covariates, diļ¬erent nonlinearities and covariate correlations. Some Monte Carlo ļ¬ndings include: 1) tuning and optimization are more delicate in ANN estimation; 2) given proper tuning, both ANN estimators with various architectures can perform well; 3) easier to tune ANN OP-OSMD estimators than ANN ES estimators; 4) stable inferences are more diļ¬icult to achieve with ANN (than spline) estimators; 5) there are gaps between current implementations and approximation theories. Finally, we apply ANN NPIV to estimate average partial derivatives in two empirical demand examples with multivariate covariates
Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
Object detection on VHR remote sensing images plays a vital role in
applications such as urban planning, land resource management, and rescue
missions. The large-scale variation of the remote-sensing targets is one of the
main challenges in VHR remote-sensing object detection. Existing methods
improve the detection accuracy of high-resolution remote sensing objects by
improving the structure of feature pyramids and adopting different attention
modules. However, for small targets, there still be seriously missed detections
due to the loss of key detail features. There is still room for improvement in
the way of multiscale feature fusion and balance. To address this issue, this
paper proposes two novel modules: Guided Attention and Tucker Bilinear
Attention, which are applied to the stages of early fusion and late fusion
respectively. The former can effectively retain clean key detail features, and
the latter can better balance features through semantic-level correlation
mining. Based on two modules, we build a new multi-scale remote sensing object
detection framework. No bells and whistles. The proposed method largely
improves the average precisions of small objects and achieves the highest mean
average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and
NWPU VHR-10.Code and models are available at
https://github.com/Shinichict/GTNet.Comment: arXiv admin note: text overlap with arXiv:1705.06676,
arXiv:2209.13351 by other author
Manin triples associated to -Lie bialgebras
In this paper, we study the Manin triples associated to -Lie bialgebras.
We develop the method of double constructions as well as operad matrices to
make -Lie bialgebras into Manin triples. Then, the related Manin triples
lead to a natural construction of metric -Lie algebras. Moreover, a
one-to-one correspondence between the double of -Lie bialgebras and Manin
triples of -Lie algebras be established
Inducing Causal Structure for Abstractive Text Summarization
The mainstream of data-driven abstractive summarization models tends to
explore the correlations rather than the causal relationships. Among such
correlations, there can be spurious ones which suffer from the language prior
learned from the training corpus and therefore undermine the overall
effectiveness of the learned model. To tackle this issue, we introduce a
Structural Causal Model (SCM) to induce the underlying causal structure of the
summarization data. We assume several latent causal factors and non-causal
factors, representing the content and style of the document and summary.
Theoretically, we prove that the latent factors in our SCM can be identified by
fitting the observed training data under certain conditions. On the basis of
this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq)
to learn the causal representations that can mimic the causal factors, guiding
us to pursue causal information for summary generation. The key idea is to
reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of
the document and summary variables from the training corpus. Experimental
results on two widely used text summarization datasets demonstrate the
advantages of our approach
Worse outcome in breast cancer with higher tumor-infiltrating FOXP3+ Tregs : a systematic review and meta-analysis
Table S1. Characteristics of the included studies. (DOCX 39ĆĀ kb
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
Recently, we have witnessed generative retrieval increasingly gaining
attention in the information retrieval (IR) field, which retrieves documents by
directly generating their identifiers. So far, much effort has been devoted to
developing effective generative retrieval models. There has been less attention
paid to the robustness perspective. When a new retrieval paradigm enters into
the real-world application, it is also critical to measure the
out-of-distribution (OOD) generalization, i.e., how would generative retrieval
models generalize to new distributions. To answer this question, firstly, we
define OOD robustness from three perspectives in retrieval problems: 1) The
query variations; 2) The unforeseen query types; and 3) The unforeseen tasks.
Based on this taxonomy, we conduct empirical studies to analyze the OOD
robustness of several representative generative retrieval models against dense
retrieval models. The empirical results indicate that the OOD robustness of
generative retrieval models requires enhancement. We hope studying the OOD
robustness of generative retrieval models would be advantageous to the IR
community.Comment: 4 pages, submit to GenIR2
Continual Learning for Generative Retrieval over Dynamic Corpora
Generative retrieval (GR) directly predicts the identifiers of relevant
documents (i.e., docids) based on a parametric model. It has achieved solid
performance on many ad-hoc retrieval tasks. So far, these tasks have assumed a
static document collection. In many practical scenarios, however, document
collections are dynamic, where new documents are continuously added to the
corpus. The ability to incrementally index new documents while preserving the
ability to answer queries with both previously and newly indexed relevant
documents is vital to applying GR models. In this paper, we address this
practical continual learning problem for GR. We put forward a novel
Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major
contributions to continual learning for GR: (i) To encode new documents into
docids with low computational cost, we present Incremental Product
Quantization, which updates a partial quantization codebook according to two
adaptive thresholds; and (ii) To memorize new documents for querying without
forgetting previous knowledge, we propose a memory-augmented learning
mechanism, to form meaningful connections between old and new documents.
Empirical results demonstrate the effectiveness and efficiency of the proposed
model.Comment: Accepted by CIKM 202
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