15 research outputs found
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Strong intelligent machines powered by deep neural networks are increasingly
deployed as black boxes to make decisions in risk-sensitive domains, such as
finance and medical. To reduce potential risk and build trust with users, it is
critical to interpret how such machines make their decisions. Existing works
interpret a pre-trained neural network by analyzing hidden neurons, mimicking
pre-trained models or approximating local predictions. However, these methods
do not provide a guarantee on the exactness and consistency of their
interpretation. In this paper, we propose an elegant closed form solution named
to compute exact and consistent interpretations for the family of
Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a
PLNN into a mathematically equivalent set of linear classifiers, then interpret
each linear classifier by the features that dominate its prediction. We further
apply to demonstrate the effectiveness of non-negative and sparse
constraints on improving the interpretability of PLNNs. The extensive
experiments on both synthetic and real world data sets clearly demonstrate the
exactness and consistency of our interpretation.Comment: KDD 201
ALID: Scalable Dominant Cluster Detection
Detecting dominant clusters is important in many analytic applications. The
state-of-the-art methods find dense subgraphs on the affinity graph as the
dominant clusters. However, the time and space complexity of those methods are
dominated by the construction of the affinity graph, which is quadratic with
respect to the number of data points, and thus impractical on large data sets.
To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT)
and develop a scalable algorithm, Approximate Localized Infection Immunization
Dynamics (ALID). The major idea is to perform Localized Infection Immunization
Dynamics (LID) to find dense subgraph within local range of the affinity graph.
LID is further scaled up with guaranteed high efficiency and detection quality
by an estimated Region of Interest (ROI) and a carefully designed Candidate
Infective Vertex Search method (CIVS). ALID only constructs small local
affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space
complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest
dominant cluster and C << n and {\delta} << n are small constants. We
demonstrate by extensive experiments on both synthetic data and real world data
that ALID achieves state-of-the-art detection quality with much lower time and
space cost on single machine. We also demonstrate the encouraging
parallelization performance of ALID by implementing the Parallel ALID (PALID)
on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours,
achieving a speedup ratio of 7.51 with 8 executors
Mining Density Contrast Subgraphs
Dense subgraph discovery is a key primitive in many graph mining
applications, such as detecting communities in social networks and mining gene
correlation from biological data. Most studies on dense subgraph mining only
deal with one graph. However, in many applications, we have more than one graph
describing relations among a same group of entities. In this paper, given two
graphs sharing the same set of vertices, we investigate the problem of
detecting subgraphs that contrast the most with respect to density. We call
such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used
graph density measures, average degree and graph affinity, are considered. For
both density measures, mining DCS is equivalent to mining the densest subgraph
from a "difference" graph, which may have both positive and negative edge
weights. Due to the existence of negative edge weights, existing dense subgraph
detection algorithms cannot identify the subgraph we need. We prove the
computational hardness of mining DCS under the two graph density measures and
develop efficient algorithms to find DCS. We also conduct extensive experiments
on several real-world datasets to evaluate our algorithms. The experimental
results show that our algorithms are both effective and efficient.Comment: Full version of an ICDE'18 pape
Robust Counterfactual Explanations on Graph Neural Networks
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications
generates a strong demand for explanations that are robust to noise and align
well with human intuition. Most existing methods generate explanations by
identifying a subgraph of an input graph that has a strong correlation with the
prediction. These explanations are not robust to noise because independently
optimizing the correlation for a single input can easily overfit noise.
Moreover, they do not align well with human intuition because removing an
identified subgraph from an input graph does not necessarily change the
prediction result. In this paper, we propose a novel method to generate robust
counterfactual explanations on GNNs by explicitly modelling the common decision
logic of GNNs on similar input graphs. Our explanations are naturally robust to
noise because they are produced from the common decision boundaries of a GNN
that govern the predictions of many similar input graphs. The explanations also
align well with human intuition because removing the set of edges identified by
an explanation from the input graph changes the prediction significantly.
Exhaustive experiments on many public datasets demonstrate the superior
performance of our method
Personalized Cross-Silo Federated Learning on Non-IID Data
Non-IID data present a tough challenge for federated learning. In this paper,
we explore a novel idea of facilitating pairwise collaborations between clients
with similar data. We propose FedAMP, a new method employing federated
attentive message passing to facilitate similar clients to collaborate more. We
establish the convergence of FedAMP for both convex and non-convex models, and
propose a heuristic method to further improve the performance of FedAMP when
clients adopt deep neural networks as personalized models. Our extensive
experiments on benchmark data sets demonstrate the superior performance of the
proposed methods.Comment: Accepted by AAAI 2021. The API of this work is available at Huawei
Cloud
(https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=6d4a9521-6a4d-4b6d-b84d-943d7c7b1cbd),
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