16 research outputs found
Continual Causal Effect Estimation: Challenges and Opportunities
A further understanding of cause and effect within observational data is
critical across many domains, such as economics, health care, public policy,
web mining, online advertising, and marketing campaigns. Although significant
advances have been made to overcome the challenges in causal effect estimation
with observational data, such as missing counterfactual outcomes and selection
bias between treatment and control groups, the existing methods mainly focus on
source-specific and stationary observational data. Such learning strategies
assume that all observational data are already available during the training
phase and from only one source. This practical concern of accessibility is
ubiquitous in various academic and industrial applications. That's what it
boiled down to: in the era of big data, we face new challenges in causal
inference with observational data, i.e., the extensibility for incrementally
available observational data, the adaptability for extra domain adaptation
problem except for the imbalance between treatment and control groups, and the
accessibility for an enormous amount of data. In this position paper, we
formally define the problem of continual treatment effect estimation, describe
its research challenges, and then present possible solutions to this problem.
Moreover, we will discuss future research directions on this topic.Comment: The 37th AAAI conference on artificial intelligence Continual
Causality Bridge Progra
Fair Attribute Completion on Graph with Missing Attributes
Tackling unfairness in graph learning models is a challenging task, as the
unfairness issues on graphs involve both attributes and topological structures.
Existing work on fair graph learning simply assumes that attributes of all
nodes are available for model training and then makes fair predictions. In
practice, however, the attributes of some nodes might not be accessible due to
missing data or privacy concerns, which makes fair graph learning even more
challenging. In this paper, we propose FairAC, a fair attribute completion
method, to complement missing information and learn fair node embeddings for
graphs with missing attributes. FairAC adopts an attention mechanism to deal
with the attribute missing problem and meanwhile, it mitigates two types of
unfairness, i.e., feature unfairness from attributes and topological unfairness
due to attribute completion. FairAC can work on various types of homogeneous
graphs and generate fair embeddings for them and thus can be applied to most
downstream tasks to improve their fairness performance. To our best knowledge,
FairAC is the first method that jointly addresses the graph attribution
completion and graph unfairness problems. Experimental results on benchmark
datasets show that our method achieves better fairness performance with less
sacrifice in accuracy, compared with the state-of-the-art methods of fair graph
learning. Code is available at: https://github.com/donglgcn/FairAC
Impact of China’s National Centralized Drug Procurement Policy on pharmaceutical enterprises’ financial performance: a quasi-natural experimental study
IntroductionIn China, the interest relationship between pharmaceutical enterprises and medical institutions has harmed the healthy development of pharmaceutical enterprises. In November 2018, the National Centralized Drug Procurement (NCDP) policy was published. The NCDP policy severs the interest relationship and significantly impacts on pharmaceutical enterprises’s financial performance.MethodsUsing the implementation of China’s National Centralized Drug Procurement (NCDP) policy as a quasi-natural experiment, this study evaluated the impact of participation in the NCDP policy on pharmaceutical enterprises’ financial performance. We developed a difference-in-difference model to estimate the change in financial performance after NCDP implementation, based on financial data on Chinese listed pharmaceutical enterprises.ResultsWe found that the bid-winning enterprises’ financial performance significantly improved after participating in NCDP. This may be related to lower costs, market share expansion, and increased research and development investment by the bid-winning enterprises.DiscussionTo further promote the high-quality development of pharmaceutical enterprises in China, the government should expand the variety of drugs on the NCDP list (NCDP drugs), while improving the drug patent protection system and the policies to support the bid-winning enterprises
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
Various probabilistic time series forecasting models have sprung up and shown
remarkably good performance. However, the choice of model highly relies on the
characteristics of the input time series and the fixed distribution that the
model is based on. Due to the fact that the probability distributions cannot be
averaged over different models straightforwardly, the current time series model
ensemble methods cannot be directly applied to improve the robustness and
accuracy of forecasting. To address this issue, we propose pTSE, a multi-model
distribution ensemble method for probabilistic forecasting based on Hidden
Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models
without requiring further information about each model. Besides, we provide a
complete theoretical analysis of pTSE to prove that the empirical distribution
of time series subject to an HMM will converge to the stationary distribution
almost surely. Experiments on benchmarks show the superiority of pTSE overall
member models and competitive ensemble methods.Comment: The 32nd International Joint Conference on Artificial Intelligence
(IJCAI 2023
Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in
our daily live and human society, people both enjoy the benefits brought by
these technologies and suffer many social issues induced by these systems. To
make AI systems good enough and trustworthy, plenty of researches have been
done to build guidelines for trustworthy AI systems. Machine learning is one of
the most important parts for AI systems and representation learning is the
fundamental technology in machine learning. How to make the representation
learning trustworthy in real-world application, e.g., cross domain scenarios,
is very valuable and necessary for both machine learning and AI system fields.
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy
representation learning across domains framework which includes four concepts,
i.e, robustness, privacy, fairness, and explainability, to give a comprehensive
literature review on this research direction. Specifically, we first introduce
the details of the proposed trustworthy framework for representation learning
across domains. Second, we provide basic notions and comprehensively summarize
existing methods for the trustworthy framework from four concepts. Finally, we
conclude this survey with insights and discussions on future research
directions.Comment: 38 pages, 15 figure
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem
in decision-making across various industrial scenarios. However, existing
time-series forecasting methods often overlook two important characteristics of
cumulative data, namely monotonicity and irregularity, which limit their
practical applicability. To address this limitation, we propose a principled
approach called Monotonic neural Ordinary Differential Equation (MODE) within
the framework of neural ordinary differential equations. By leveraging MODE, we
are able to effectively capture and represent the monotonicity and irregularity
in practical cumulative data. Through extensive experiments conducted in a
bonus allocation scenario, we demonstrate that MODE outperforms
state-of-the-art methods, showcasing its ability to handle both monotonicity
and irregularity in cumulative data and delivering superior forecasting
performance.Comment: Accepted as CIKM'23 Applied Research Trac
Continual Learning in Predictive Autoscaling
Predictive Autoscaling is used to forecast the workloads of servers and
prepare the resources in advance to ensure service level objectives (SLOs) in
dynamic cloud environments. However, in practice, its prediction task often
suffers from performance degradation under abnormal traffics caused by external
events (such as sales promotional activities and applications
re-configurations), for which a common solution is to re-train the model with
data of a long historical period, but at the expense of high computational and
storage costs. To better address this problem, we propose a replay-based
continual learning method, i.e., Density-based Memory Selection and Hint-based
Network Learning Model (DMSHM), using only a small part of the historical log
to achieve accurate predictions. First, we discover the phenomenon of sample
overlap when applying replay-based continual learning in prediction tasks. In
order to surmount this challenge and effectively integrate new sample
distribution, we propose a density-based sample selection strategy that
utilizes kernel density estimation to calculate sample density as a reference
to compute sample weight, and employs weight sampling to construct a new memory
set. Then we implement hint-based network learning based on hint representation
to optimize the parameters. Finally, we conduct experiments on public and
industrial datasets to demonstrate that our proposed method outperforms
state-of-the-art continual learning methods in terms of memory capacity and
prediction accuracy. Furthermore, we demonstrate remarkable practicability of
DMSHM in real industrial applications
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for
modeling continuous-time event sequences, such as user activities on the web
and financial transactions. In real-world applications, event data is typically
received in a \emph{streaming} manner, where the distribution of patterns may
shift over time. Additionally, \emph{privacy and memory constraints} are
commonly observed in practical scenarios, further compounding the challenges.
Therefore, the continuous monitoring of a TPP to learn the streaming event
sequence is an important yet under-explored problem. Our work paper addresses
this challenge by adopting Continual Learning (CL), which makes the model
capable of continuously learning a sequence of tasks without catastrophic
forgetting under realistic constraints. Correspondingly, we propose a simple
yet effective framework, PromptTPP\footnote{Our code is available at {\small
\url{ https://github.com/yanyanSann/PromptTPP}}}, by integrating the base TPP
with a continuous-time retrieval prompt pool. The prompts, small learnable
parameters, are stored in a memory space and jointly optimized with the base
TPP, ensuring that the model learns event streams sequentially without
buffering past examples or task-specific attributes. We present a novel and
realistic experimental setup for modeling event streams, where PromptTPP
consistently achieves state-of-the-art performance across three real user
behavior datasets.Comment: NeurIPS 2023 camera ready versio
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but
struggle when applied directly to complex domains like finance. LLMs have
difficulty reasoning about and integrating all relevant information. We propose
a data-centric approach to enable LLMs to better handle financial tasks. Our
key insight is that rather than overloading the LLM with everything at once, it
is more effective to preprocess and pre-understand the data. We create a
financial LLM (FLLM) using multitask prompt-based finetuning to achieve data
pre-processing and pre-understanding. However, labeled data is scarce for each
task. To overcome manual annotation costs, we employ abductive augmentation
reasoning (AAR) to automatically generate training data by modifying the pseudo
labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR
substantially outperforms baseline financial LLMs designed for raw text,
achieving state-of-the-art on financial analysis and interpretation tasks. We
also open source a new benchmark for financial analysis and interpretation. Our
methodology provides a promising path to unlock LLMs' potential for complex
real-world domains