23 research outputs found
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing feature
patterns different from the majority, plays a fundamental role in various
applications. However, it is challenging for existing methods to handle the
scenarios where the instances are systems whose characteristics are not readily
observed as data. Appropriate interactions are needed to interact with the
systems and identify those with abnormal responses. Detecting system-wise
anomalies is a challenging task due to several reasons including: how to
formally define the system-wise anomaly detection problem; how to find the
effective activation signal for interacting with systems to progressively
collect the data and learn the detector; how to guarantee stable training in
such a non-stationary scenario with real-time interactions? To address the
challenges, we propose InterSAD (Interactive System-wise Anomaly Detection).
Specifically, first, we adopt Markov decision process to model the interactive
systems, and define anomalous systems as anomalous transition and anomalous
reward systems. Then, we develop an end-to-end approach which includes an
encoder-decoder module that learns system embeddings, and a policy network to
generate effective activation for separating embeddings of normal and anomaly
systems. Finally, we design a training method to stabilize the learning
process, which includes a replay buffer to store historical interaction data
and allow them to be re-sampled. Experiments on two benchmark environments,
including identifying the anomalous robotic systems and detecting user data
poisoning in recommendation models, demonstrate the superiority of InterSAD
compared with state-of-the-art baselines methods
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attributes
for all instances are fully available, which may not be true in many real-world
applications due to the high cost of acquiring sensitive information. When
sensitive attributes are not disclosed or available, it is needed to manually
annotate a small part of the training data to mitigate bias. However, the
skewed distribution across different sensitive groups preserves the skewness of
the original dataset in the annotated subset, which leads to non-optimal bias
mitigation. To tackle this challenge, we propose Active Penalization Of
Discrimination (APOD), an interactive framework to guide the limited
annotations towards maximally eliminating the effect of algorithmic bias. The
proposed APOD integrates discrimination penalization with active instance
selection to efficiently utilize the limited annotation budget, and it is
theoretically proved to be capable of bounding the algorithmic bias. According
to the evaluation on five benchmark datasets, APOD outperforms the
state-of-the-arts baseline methods under the limited annotation budget, and
shows comparable performance to fully annotated bias mitigation, which
demonstrates that APOD could benefit real-world applications when sensitive
information is limited
DISPEL: Domain Generalization via Domain-Specific Liberating
Domain generalization aims to learn a generalization model that can perform
well on unseen test domains by only training on limited source domains.
However, existing domain generalization approaches often bring in
prediction-irrelevant noise or require the collection of domain labels. To
address these challenges, we consider the domain generalization problem from a
different perspective by categorizing underlying feature groups into
domain-shared and domain-specific features. Nevertheless, the domain-specific
features are difficult to be identified and distinguished from the input data.
In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing
fine-grained masking approach that can filter out undefined and
indistinguishable domain-specific features in the embedding space.
Specifically, DISPEL utilizes a mask generator that produces a unique mask for
each input data to filter domain-specific features. The DISPEL framework is
highly flexible to be applied to any fine-tuned models. We derive a
generalization error bound to guarantee the generalization performance by
optimizing a designed objective loss. The experimental results on five
benchmarks demonstrate DISPEL outperforms existing methods and can further
generalize various algorithms
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Fairness in machine learning has attracted increasing attention in recent
years. The fairness methods improving algorithmic fairness for in-distribution
data may not perform well under distribution shifts. In this paper, we first
theoretically demonstrate the inherent connection between distribution shift,
data perturbation, and model weight perturbation. Subsequently, we analyze the
sufficient conditions to guarantee fairness (i.e., low demographic parity) for
the target dataset, including fairness for the source dataset, and low
prediction difference between the source and target datasets for each sensitive
attribute group. Motivated by these sufficient conditions, we propose robust
fairness regularization (RFR) by considering the worst case within the model
weight perturbation ball for each sensitive attribute group. We evaluate the
effectiveness of our proposed RFR algorithm on synthetic and real distribution
shifts across various datasets. Experimental results demonstrate that RFR
achieves better fairness-accuracy trade-off performance compared with several
baselines. The source code is available at
\url{https://github.com/zhimengj0326/RFR_NeurIPS23}.Comment: NeurIPS 202
Efficient GNN Explanation via Learning Removal-based Attribution
As Graph Neural Networks (GNNs) have been widely used in real-world
applications, model explanations are required not only by users but also by
legal regulations. However, simultaneously achieving high fidelity and low
computational costs in generating explanations has been a challenge for current
methods. In this work, we propose a framework of GNN explanation named LeArn
Removal-based Attribution (LARA) to address this problem. Specifically, we
introduce removal-based attribution and demonstrate its substantiated link to
interpretability fidelity theoretically and experimentally. The explainer in
LARA learns to generate removal-based attribution which enables providing
explanations with high fidelity. A strategy of subgraph sampling is designed in
LARA to improve the scalability of the training process. In the deployment,
LARA can efficiently generate the explanation through a feed-forward pass. We
benchmark our approach with other state-of-the-art GNN explanation methods on
six datasets. Results highlight the effectiveness of our framework regarding
both efficiency and fidelity. In particular, LARA is 3.5 times faster and
achieves higher fidelity than the state-of-the-art method on the large dataset
ogbn-arxiv (more than 160K nodes and 1M edges), showing its great potential in
real-world applications. Our source code is available at
https://anonymous.4open.science/r/LARA-10D8/README.md
Development of novel AMP-based absorbents for efficient CO2 capture with low energy consumption through modifying the electrostatic potential
The global deployment of aqueous amine absorbents for carbon dioxide (CO2) capture is hindered by their high energy consumption. A potential solution to this challenge lies in the utilization of non-aqueous amine systems, which offer energy-efficient alternatives. However, they are prone to form precipitation during CO2 absorption process, which limits their application. Combining experimental and theoretical studies, we found that the electrostatic potential of carbamate, instead of van der Waals force, is a major factor controlling the precipitation, and hydrogen bonds can effectively reduce the electrostatic potential of carbamate and prevent precipitation. Single solvent screening experiments have also demonstrated that the absorption rate is closely related to the viscosity of the organic solvent and the affinity of the functional group for CO2. The polar solvents (Dimethylformamide (DMF), Dimethyl sulfoxide (DMSO), and N-Methylformamide (NMF)) exhibit higher absorption rates, but suffer from issues of precipitation. Hydroxyl group riched solvents (Ethylene glycol (EG) and Glycerol) exhibit lower absorption rate, but they don’t have the issue of precipitation. Based on these findings, several novel 2-Amino-2-methyl-1-propanol (AMP)-based non-aqueous absorbents have been developed aiming at reducing the energy penalty, and improving CO2 absorption and desorption performance. Among these absorbents, AMP-EG-DMF (4–3) exhibits maximum CO2 absorption rate and absorption capacity of 9.91 g-CO2/(kg-soln.·min.) and 122 g-CO2/(kg-soln.), respectively, which are 64.1% and 28.4% higher than those of 30 wt% AMP aqueous solution, respectively. Additionally, compared to 30 wt% MEA, the energy consumption of AMP-EG-DMF (4–3) shows 46.30% reduction. The addition of EG effectively improves the electrostatic solubility of AMP-carbamate by increasing the number and strength of hydrogen bonds, thus avoiding the generation of precipitation. The final product species and reaction mechanism were analysed by using 13C and 1H NMR, In-situ ATR-FTIR, and quantum chemical calculation. The combination of theoretical and experimental results indicates that bi-solvent AMP-based absorbents can serve as a promising alternative for low-energy CO2 capture