72 research outputs found
Specify Robust Causal Representation from Mixed Observations
Learning representations purely from observations concerns the problem of
learning a low-dimensional, compact representation which is beneficial to
prediction models. Under the hypothesis that the intrinsic latent factors
follow some casual generative models, we argue that by learning a causal
representation, which is the minimal sufficient causes of the whole system, we
can improve the robustness and generalization performance of machine learning
models. In this paper, we develop a learning method to learn such
representation from observational data by regularizing the learning procedure
with mutual information measures, according to the hypothetical factored causal
graph. We theoretically and empirically show that the models trained with the
learned causal representations are more robust under adversarial attacks and
distribution shifts compared with baselines. The supplementary materials are
available at https://github.com/ymy .Comment: arXiv admin note: substantial text overlap with arXiv:2202.0838
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Learning-to-rank is a core technique in the top-N recommendation task, where
an ideal ranker would be a mapping from an item set to an arrangement (a.k.a.
permutation). Most existing solutions fall in the paradigm of probabilistic
ranking principle (PRP), i.e., first score each item in the candidate set and
then perform a sort operation to generate the top ranking list. However, these
approaches neglect the contextual dependence among candidate items during
individual scoring, and the sort operation is non-differentiable. To bypass the
above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework
directly generates the permutations of the candidate items without the need for
individually scoring and sort operations; and is end-to-end differentiable. As
a result, STARank can operate when only the ground-truth permutations are
accessible without requiring access to the ground-truth relevance scores for
items. For this purpose, STARank first reads the candidate items in the context
of the user browsing history, whose representations are fed into a
Plackett-Luce module to arrange the given items into a list. To effectively
utilize the given ground-truth permutations for supervising STARank, we
leverage the internal consistency property of Plackett-Luce models to derive a
computationally efficient list-wise loss. Experimental comparisons against 9
the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3
top-N real-world recommendation datasets demonstrate the superiority of STARank
in terms of conventional ranking metrics. Notice that these ranking metrics do
not consider the effects of the contextual dependence among the items in the
list, we design a new family of simulation-based ranking metrics, where
existing metrics can be regarded as special cases. STARank can consistently
achieve better performance in terms of PBM and UBM simulation-based metrics.Comment: CIKM 202
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
Learning disentanglement aims at finding a low dimensional representation
which consists of multiple explanatory and generative factors of the
observational data. The framework of variational autoencoder (VAE) is commonly
used to disentangle independent factors from observations. However, in real
scenarios, factors with semantics are not necessarily independent. Instead,
there might be an underlying causal structure which renders these factors
dependent. We thus propose a new VAE based framework named CausalVAE, which
includes a Causal Layer to transform independent exogenous factors into causal
endogenous ones that correspond to causally related concepts in data. We
further analyze the model identifiabitily, showing that the proposed model
learned from observations recovers the true one up to a certain degree.
Experiments are conducted on various datasets, including synthetic and real
word benchmark CelebA. Results show that the causal representations learned by
CausalVAE are semantically interpretable, and their causal relationship as a
Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we
demonstrate that the proposed CausalVAE model is able to generate
counterfactual data through "do-operation" to the causal factors
Gut microbiota and risk of endocarditis: a bidirectional Mendelian randomization study
BackgroundThe associations between gut microbiota and cardiovascular disease have been reported in previous studies. However, the relationship between gut microbiota and endocarditis remains unclear.MethodsA bidirectional Mendelian randomization (MR) study was performed to detect the association between gut microbiota and endocarditis. Inverse variance weighted (IVW) method was considered the main result. Simultaneously, heterogeneity and pleiotropy tests were conducted.ResultsOur study suggests that family Victivallaceae (p = 0.020), genus Eubacterium fissicatena group (p = 0.047), genus Escherichia Shigella (p = 0.024), genus Peptococcus (p = 0.028) and genus Sellimonas (p = 0.005) play protective roles in endocarditis. Two microbial taxa, including genus Blautia (p = 0.006) and genus Ruminococcus2 (p = 0.024) increase the risk of endocarditis. At the same time, endocarditis has a negative effect on genus Eubacterium fissicatena group (p = 0.048). Besides, no heterogeneity or pleiotropy was found in this study.ConclusionOur study emphasized the certain role of specific gut microbiota in patients with endocarditis and clarified the negative effect of endocarditis on gut microbiota
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through “do-operation” to the causal factors
Lending Interaction Wings to Recommender Systems with Conversational Agents
Recommender systems trained on offline historical user behaviors are
embracing conversational techniques to online query user preference. Unlike
prior conversational recommendation approaches that systemically combine
conversational and recommender parts through a reinforcement learning
framework, we propose CORE, a new offline-training and online-checking paradigm
that bridges a COnversational agent and REcommender systems via a unified
uncertainty minimization framework. It can benefit any recommendation platform
in a plug-and-play style. Here, CORE treats a recommender system as an offline
relevance score estimator to produce an estimated relevance score for each
item; while a conversational agent is regarded as an online relevance score
checker to check these estimated scores in each session. We define uncertainty
as the summation of unchecked relevance scores. In this regard, the
conversational agent acts to minimize uncertainty via querying either
attributes or items. Based on the uncertainty minimization framework, we derive
the expected certainty gain of querying each attribute and item, and develop a
novel online decision tree algorithm to decide what to query at each turn.
Experimental results on 8 industrial datasets show that CORE could be
seamlessly employed on 9 popular recommendation approaches. We further
demonstrate that our conversational agent could communicate as a human if
empowered by a pre-trained large language model.Comment: NeurIPS 202
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