pwc-archive/papers-with-abstracts huggingface dataset:
On-Line Portfolio Selection with Moving Average Reversion
Authors: Bin Li, Steven C. H. Hoi
Date: 2012-06-01 00:00:00
Proceeding: Proceedings of the 29thInternational Confer-ence on Machine Learning 2012 6

Online portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences how that stock’s high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in some real datasets. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called “Moving Average Reversion” (MAR), and a new on-line portfolio selection strategy named “On-Line Moving Average Reversion” (OLMAR), which exploits MAR by applying powerful online learning techniques.  From our empirical results, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to superior performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.
Can We Learn to Beat the Best Stock
Authors: Allan Borodin, Ran El-Yaniv, Vincent Gogan
Date: 2004-05-01 00:00:00
Proceeding: Advances in Neural Information Processing Systems 16 2004 5

A novel algorithm for actively trading stocks is presented.  While traditional universal algorithms (and technical trading heuristics) attempt topredict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can “beat the market” and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.
PAMR: Passive aggressive mean reversion strategy for portfolio selection
Authors: Bin Li, Peilin Zhao, Steven C. H. Hoi
Date: 2012-02-21 00:00:00
Proceeding: Machine Learning 2012 2

This article proposes a novel online portfolio selection strategy named “Passive Aggressive Mean Reversion” (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR’s update scheme, we find that it nicely trades off between portfolio returnand volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available athttp://www.cais.ntu.edu.sg/~chhoi/PAMR/.
CORN: Correlation-driven Non parametric Learning Approach for Portfolio Selection
Authors: Bin Li, Steven C. H. Hoi, Vivekanand GOPALKRISHNAN
Date: 2011-04-01 00:00:00
Proceeding: ACM Transactions on Intelligent Systems and Technology. 2 2011 4

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Non parametric learning strategy (CORN) for activelytrading stocks, which effectively exploits statistical relations between stock market windows via a non parametric learning approach.  We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, in which the encouraging results show that the proposed new algorithm can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpasses avariety of state-of-the-art techniques significantly.
Universal Portfolios
Authors: Thomas M. Cover
Date: 1991-01-01 00:00:00
Proceeding: Mathematical Finance, Vol. 1 1991 1

We exhibit an algorithm for portfolio selection that asymptotically outperforms the best stock in the market.
Robust Median Reversion Strategy for On-Line Portfolio Selection
Authors: Dingjiang Huang, Junlong Zhou, Bin Li, Steven C.H. Hoi, Shuigeng Zhou
Date: 2013-08-01 00:00:00
Proceeding: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence 2013 8

Online portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies.  Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. Toovercome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named “Robust Median Reversion” (RMR), which makes optimal portfolios based on the improved reversion estimation. Empirical results on various real markets show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale trading applications.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Authors: Ling Luo, Zhihao Yang, Pei Yang, Yin Zhang, Lei Wang, Hongfei Lin, Jian Wang
Date: 2019-04-15 00:00:00
Proceeding: Bioinformatics 2019 4
Tasks: Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. Results: In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document- level chemical NER. The approach leverages document-level global information obtained by atten- tion mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) cor- pus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). Availability and implementation: Data and code are available at https://github.com/lingluodlut/Att- ChemdNER. Contact: yangzh@dlut.edu.cn or wangleibihami@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
Reweighted Price Relative Tracking System for Automatic Portfolio Optimization
Authors: Zhao-Rong Lai, Pei-Yi Yang, Liangda Fang, Xiaotian Wu
Date: 2018-07-16 00:00:00
Proceeding: IEEE Transactions on Systems, Man, and Cybernetics 2018 7
Tasks: Portfolio Optimization

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset's performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system.
A Search Engine for Scientific Publications: a Cybersecurity Case Study
Authors: Nuno Oliveira, Norberto Sousa, Isabel Praça
Date: 2021-06-30 00:00:00
Tasks: Information Retrieval, Management, Reading Comprehension, Retrieval

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.
Classical Planning in Deep Latent Space
Authors: Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise
Date: 2021-06-30 00:00:00
Tasks: Deep Learning, Sokoban

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.
Applications of the Free Energy Principle to Machine Learning and Neuroscience
Authors: Beren Millidge
Date: 2021-06-30 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Deep Reinforcement Learning

In this PhD thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems that maintain a separation from their environment. A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy. The thesis is structured into three independent sections. Firstly, we focus on predictive coding, a neurobiologically plausible process theory derived from the free energy principle which argues that the primary function of the brain is to minimize prediction errors, showing how predictive coding can be scaled up and extended to be more biologically plausible, and elucidating its close links with other methods such as Kalman Filtering. Secondly, we study active inference, a neurobiologically grounded account of action through variational message passing, and investigate how these methods can be scaled up to match the performance of deep reinforcement learning methods. We additionally provide a detailed mathematical understanding of the nature and origin of the information-theoretic objectives that underlie exploratory behaviour. Finally, we investigate biologically plausible methods of credit assignment in the brain. We first demonstrate a close link between predictive coding and the backpropagation of error algorithm. We go on to propose novel and simpler algorithms which allow for backprop to be implemented in purely local, biologically plausible computations.
A Study of the Quality of Wikidata
Authors: Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe, Pedro Szekely
Date: 2021-07-01 00:00:00

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we develop a framework to detect and analyze low-quality statements in Wikidata by shedding light on the current practices exercised by the community. We explore three indicators of data quality in Wikidata, based on: 1) community consensus on the currently recorded knowledge, assuming that statements that have been removed and not added back are implicitly agreed to be of low quality; 2) statements that have been deprecated; and 3) constraint violations in the data. We combine these indicators to detect low-quality statements, revealing challenges with duplicate entities, missing triples, violated type rules, and taxonomic distinctions. Our findings complement ongoing efforts by the Wikidata community to improve data quality, aiming to make it easier for users and editors to find and correct mistakes.
Cross-Lingual Transfer Learning for Statistical Type Inference
Authors: Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, Yang Liu
Date: 2021-07-01 00:00:00
Tasks: Code Summarization, Cross-Lingual Transfer, Fault localization, Language Modelling, Transfer Learning, Vocal Bursts Type Prediction

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, PLATO, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. PLATO is powered by a novel kernelized attention mechanism to constrain the attention scope of the backbone Transformer model such that the model is forced to base its prediction on commonly shared features among languages. In addition, we propose the syntax enhancement that augments the learning on the feature overlap among language domains. Furthermore, PLATO can also be used to improve the performance of the conventional supervised learning-based type inference by introducing cross-lingual augmentation, which enables the model to learn more general features across multiple languages. We evaluated PLATO under two settings: 1) under the cross-domain scenario that the target language data is not labeled or labeled partially, the results show that PLATO outperforms the state-of-the-art domain transfer techniques by a large margin, e.g., it improves the Python to TypeScript baseline by +5.40%@EM, +5.40%@weighted-F1, and 2) under the conventional monolingual supervised learning based scenario, PLATO improves the Python baseline by +4.40%@EM, +3.20%@EM (parametric).
Bilinear Scoring Function Search for Knowledge Graph Learning
Authors: Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Date: 2021-07-01 00:00:00
Tasks: AutoML, Graph Embedding, Graph Learning, Knowledge Graph Embedding

Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been human-designed to measure the plausibility of triples and capture different kinds of relations in KGs. However, as relations exhibit intricate patterns that are hard to infer before training, none of them consistently perform the best on benchmark tasks. In this paper, inspired by the recent success of automated machine learning (AutoML), we search bilinear scoring functions for different KG tasks through the AutoML techniques. However, it is non-trivial to explore domain-specific information here. We first set up a search space for AutoBLM by analyzing existing scoring functions. Then, we propose a progressive algorithm (AutoBLM) and an evolutionary algorithm (AutoBLM+), which are further accelerated by filter and predictor to deal with the domain-specific properties for KG learning. Finally, we perform extensive experiments on benchmarks in KG completion, multi-hop query, and entity classification tasks. Empirical results show that the searched scoring functions are KG dependent, new to the literature, and outperform the existing scoring functions. AutoBLM+ is better than AutoBLM as the evolutionary algorithm can flexibly explore better structures in the same budget.
Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation
Authors: Qiwei Zhong, Guanxiong Zeng, Danqing Zhu, Yang Zhang, Wangli Lin, Ben Chen, Jiayu Tang
Date: 2021-07-01 00:00:00
Tasks: document understanding, Word Embeddings

An obstacle to scientific document understanding is the extensive use of acronyms which are shortened forms of long technical phrases. Acronym disambiguation aims to find the correct meaning of an ambiguous acronym in a given text. Recent efforts attempted to incorporate word embeddings and deep learning architectures, and achieved significant effects in this task. In general domains, kinds of fine-grained pretrained language models have sprung up, thanks to the largescale corpora which can usually be obtained through crowdsourcing. However, these models based on domain agnostic knowledge might achieve insufficient performance when directly applied to the scientific domain. Moreover, obtaining large-scale high-quality annotated data and representing high-level semantics in the scientific domain is challenging and expensive. In this paper, we consider both the domain agnostic and specific knowledge, and propose a Hierarchical Dual-path BERT method coined hdBERT to capture the general fine-grained and high-level specific representations for acronym disambiguation. First, the context-based pretrained models, RoBERTa and SciBERT, are elaborately involved in encoding these two kinds of knowledge respectively. Second, multiple layer perceptron is devised to integrate the dualpath representations simultaneously and outputs the prediction. With a widely adopted SciAD dataset contained 62,441 sentences, we investigate the effectiveness of hdBERT. The experimental results exhibit that the proposed approach outperforms state-of-the-art methods among various evaluation metrics. Specifically, its macro F1 achieves 93.73%.
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms
Authors: Fredrik Präntare, Mattias Tiger, David Bergström, Herman Appelgren, Fredrik Heintz
Date: 2021-07-01 00:00:00

This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generate feasible solutions of higher quality more quickly. Our results indicate that our approach could be a promising future method for constructing such heuristics.
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph
Authors: Yunxin Li, Yu Zhao, Baotian Hu, Qingcai Chen, Yang Xiang, Xiaolong Wang, Yuxin Ding, Lin Ma
Date: 2021-07-01 00:00:00

Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters. The typical method to utilize the glyph features is by incorporating them into the character embedding space. Inspired by previous methods, we innovatively propose a Chinese pre-trained representation model named as GlyphCRM, which abandons the ID-based character embedding method yet solely based on sequential character images. We render each character into a binary grayscale image and design two-channel position feature maps for it. Formally, we first design a two-layer residual convolutional neural network, namely HanGlyph to generate the initial glyph representation of Chinese characters, and subsequently adopt multiple bidirectional encoder Transformer blocks as the superstructure to capture the context-sensitive information. Meanwhile, we feed the glyph features extracted from each layer of the HanGlyph module into the underlying Transformer blocks by skip-connection method to fully exploit the glyph features of Chinese characters. As the HanGlyph module can obtain a sufficient glyph representation of any Chinese character, the long-standing out-of-vocabulary problem could be effectively solved. Extensive experimental results indicate that GlyphCRM substantially outperforms the previous BERT-based state-of-the-art model on 9 fine-tuning tasks, and it has strong transferability and generalization on specialized fields and low-resource tasks. We hope this work could spark further research beyond the realms of well-established representation of Chinese texts.
Differentiable Particle Filters through Conditional Normalizing Flow
Authors: Xiongjie Chen, Hao Wen, Yunpeng Li
Date: 2021-07-01 00:00:00
Tasks: Visual Tracking

Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiable particle filters are within the bootstrap particle filtering framework and fail to incorporate the information from latest observations to construct better proposals. In this paper, we utilize conditional normalizing flows to construct proposal distributions for differentiable particle filters, enriching the distribution families that the proposal distributions can represent. In addition, normalizing flows are incorporated in the construction of the dynamic model, resulting in a more expressive dynamic model. We demonstrate the performance of the proposed conditional normalizing flow-based differentiable particle filters in a visual tracking task.
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
Authors: Lars Malmqvist, Tommy Yuan, Suresh Manandhar
Date: 2021-07-01 00:00:00

This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different methods. It shows that such a visualisation approach can work for argumentation and show interesting structural properties of argumentation graphs, opening up paths for further research in the area.