277 research outputs found
Causally Denoise Word Embeddings Using Half-Sibling Regression
Distributional representations of words, also known as word vectors, have
become crucial for modern natural language processing tasks due to their wide
applications. Recently, a growing body of word vector postprocessing algorithm
has emerged, aiming to render off-the-shelf word vectors even stronger. In line
with these investigations, we introduce a novel word vector postprocessing
scheme under a causal inference framework. Concretely, the postprocessing
pipeline is realized by Half-Sibling Regression (HSR), which allows us to
identify and remove confounding noise contained in word vectors. Compared to
previous work, our proposed method has the advantages of interpretability and
transparency due to its causal inference grounding. Evaluated on a battery of
standard lexical-level evaluation tasks and downstream sentiment analysis
tasks, our method reaches state-of-the-art performance.Comment: Accepted by AAAI 202
Alternating Direction Method of Multipliers Based on -norm for Multiple Measurement Vector Problem
In this paper, we propose an alternating direction method of multipliers
(ADMM)-based optimization algorithm to achieve better undersampling rate for
multiple measurement vector (MMV) problem. The core is to introduce the
-norm sparsity constraint to describe the joint-sparsity of the MMV
problem, which is different from the widely used -norm constraint
in the existing research. In order to illustrate the better performance of
-norm, first this paper proves the equivalence of the sparsity of
the row support set of a matrix and its -norm. Afterward, the MMV
problem based on -norm is proposed. Moreover, building on the
Kurdyka-Lojasiewicz property, this paper establishes that the sequence
generated by ADMM globally converges to the optimal point of the MMV problem.
Finally, the performance of our algorithm and comparison with other algorithms
under different conditions is studied by simulated examples.Comment: 24 pages, 5 figures, 4 table
Cold-start Sequential Recommendation via Meta Learner
This paper explores meta-learning in sequential recommendation to alleviate
the item cold-start problem. Sequential recommendation aims to capture user's
dynamic preferences based on historical behavior sequences and acts as a key
component of most online recommendation scenarios. However, most previous
methods have trouble recommending cold-start items, which are prevalent in
those scenarios. As there is generally no side information in the setting of
sequential recommendation task, previous cold-start methods could not be
applied when only user-item interactions are available. Thus, we propose a
Meta-learning-based Cold-Start Sequential Recommendation Framework, namely
Mecos, to mitigate the item cold-start problem in sequential recommendation.
This task is non-trivial as it targets at an important problem in a novel and
challenging context. Mecos effectively extracts user preference from limited
interactions and learns to match the target cold-start item with the potential
user. Besides, our framework can be painlessly integrated with neural
network-based models. Extensive experiments conducted on three real-world
datasets verify the superiority of Mecos, with the average improvement up to
99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.Comment: Accepted at AAAI 202
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Trust Repair in Human-Swarm Teams+
Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trust-repair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions
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