567 research outputs found
Differential Good Arm Identification
This paper targets a variant of the stochastic multi-armed bandit problem
called good arm identification (GAI). GAI is a pure-exploration bandit problem
with the goal to output as many good arms using as few samples as possible,
where a good arm is defined as an arm whose expected reward is greater than a
given threshold. In this work, we propose DGAI - a differentiable good arm
identification algorithm to improve the sample complexity of the
state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that
the DGAI can further boost the performance of a general multi-arm bandit (MAB)
problem given a threshold as a prior knowledge to the arm set. Extensive
experiments confirm that our algorithm outperform the baseline algorithms
significantly in both synthetic and real world datasets for both GAI and MAB
tasks
Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model
The goal of this paper is to report certain scientific discoveries about a
Seq2Seq model. It is known that analyzing the behavior of RNN-based models at
the neuron level is considered a more challenging task than analyzing a DNN or
CNN models due to their recursive mechanism in nature. This paper aims to
provide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq model
without attention can achieve token-positioning. We found four different types
of neurons: storing, counting, triggering, and outputting and further uncover
the mechanism for these neurons to work together in order to produce the right
token in the right position.Comment: 9 pages (excluding reference), 10 figure
Environment Diversification with Multi-head Neural Network for Invariant Learning
Neural networks are often trained with empirical risk minimization; however,
it has been shown that a shift between training and testing distributions can
cause unpredictable performance degradation. On this issue, a research
direction, invariant learning, has been proposed to extract invariant features
insensitive to the distributional changes. This work proposes EDNIL, an
invariant learning framework containing a multi-head neural network to absorb
data biases. We show that this framework does not require prior knowledge about
environments or strong assumptions about the pre-trained model. We also reveal
that the proposed algorithm has theoretical connections to recent studies
discussing properties of variant and invariant features. Finally, we
demonstrate that models trained with EDNIL are empirically more robust against
distributional shifts.Comment: In Proceedings of 36th Conference on Neural Information Processing
Systems (NeurIPS 2022
GraphFC: Customs Fraud Detection with Label Scarcity
Custom officials across the world encounter huge volumes of transactions.
With increased connectivity and globalization, the customs transactions
continue to grow every year. Associated with customs transactions is the
customs fraud - the intentional manipulation of goods declarations to avoid the
taxes and duties. With limited manpower, the custom offices can only undertake
manual inspection of a limited number of declarations. This necessitates the
need for automating the customs fraud detection by machine learning (ML)
techniques. Due the limited manual inspection for labeling the new-incoming
declarations, the ML approach should have robust performance subject to the
scarcity of labeled data. However, current approaches for customs fraud
detection are not well suited and designed for this real-world setting. In this
work, we propose ( neural networks for
ustoms raud), a model-agnostic, domain-specific,
semi-supervised graph neural network based customs fraud detection algorithm
that has strong semi-supervised and inductive capabilities. With upto 252%
relative increase in recall over the present state-of-the-art, extensive
experimentation on real customs data from customs administrations of three
different countries demonstrate that GraphFC consistently outperforms various
baselines and the present state-of-art by a large margin
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