Research Track Session RT10: Embeddings II – Summit 2, Ground Level, Egan Center Chair: Jundong Li
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding Zhige Li (Shanghai Jiao Tong University)
Derek Yang (Tsinghua University); Li Zhao (Microsoft); Jiang Bian (Microsoft); Tao Qin (Microsoft); Tie-Yan Liu (Microsoft)
purpose
1, token embedding
2, analysis of embedding
Preliminaries about technical analysis
Introduction and properties
Technical indicators are developed to recognize reading patterns
Indicator-Based Portfolio construction
- Single indicator
- Multiple indicators
Evaluation metrics
Limitation: Unified Transformation
The stock-wise indicator optimization model
Distinguish stock properties
Stocks within the same fun as likely to share some common characteristics
Stock embedding
Fund-stock graph construction
Indicator rescaling method
Assumption:
- Constancy and continuity
Principle: - keep the original properties
- Stocks adjustment
Experimental insight
Effectiveness of optimized indicator
Performance in real-world investing
Case study:
Different indicator show different sensitives towards different stock
Similar rescaling weights distributed scales
Conclusion
- Leverage the difference in terms of indicator’s stock-wise affinity
- Data mining view to learn the stock representation by mining knowledge repository
- Proposed a delicately-designed rescaling network, for the purpose of retaining the original properties the indicator
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu (IBM Research); Ian En-Hsu Yen (CMU); Siyu Huo (IBM Research); Liang Zhao (George Mason University); Kun Xu (IBM Research); Liang Ma (IBM Research); Shouling Ji (Zhejiang University); Charu Aggarwal (IBM Research)
String kernel analysis: Applications
Analysis of large-scale sequential data has been one of the most crucial tasks
- Bioinformatics
- Text
- Audio mining
String kernels are effective method learning features from the sequence
difficulty
- Hardly capture long discriminative patterns
- Diagonal dominance the kernel matrix
- Experience quadratic complexity in the number of samples
Contribution
- A family of positive-definite global string kernels
- Random string embedding
- Theoretically Show Uniform convergence of RSE
Existing approaches for string kernels
String kernels by counting substructures > Ignore global properties and beed high computation of kernel matrix
Edit distance substitution > Invalid positive definite kernel
Key idea
-Develop a general framework of building string kernel and generating string embedding from edit distance
Global string kernel using edit distance and random features
The core task: to build a positive -definite string kernel utilizing the global alignment measure (via edit distance or Levenshtein distance)
Connection to distance substitution kernel
Random string embedding: random features of global string kernel
Efficient computation of RSE: use Random Features
Data-independent
Distance measure
The convergence of random string embedding
How many random features are required in (10) to have an accurate
Increasing R help improve the accuracy with linear complexity of R
conclusion
- presented scalable global string kernels
- A simple yet effective way to handle real-world large-scale string data
HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings
Changping Meng (Purdue University); Jiasen Yang (Purdue University); Bruno Ribeiro (Purdue University); Jennifer Neville (Purdue University)
typical deep learning tasks need a canonical orientation
traditional neural networks are permutation aware
set-of-set
example: Adamic-Adar index: measure the similarity between a pair of nodes in the graph based on the degree of their common neighbors
(since there's no canonical ordering, the data may have..)
learning representation of set-of-set
SOS is a set whose elements are themselves sets.
desired representation function properties
Output: SOS representation
architecture for SOS
intra-set representation
how to make HATS permutation invariant
which can be estimated using Monte Carlo at test time
Experiment models:
Deepset, MI-CNN, J-lstm, HATS, H-lstm
input: each SOS conatnains4 sets. each set has 10 integers from 0 to 9.
output: regression float output rounded to the nearest integer
task: predict Adamic-Adar index between two nodes in a graph
experiment hyperlink
In: each set contains m=4
Out: binary label
Task SOS:
- anomaly detection
- unique count
conclusion
- set-od set representation need to be permutation invariant and
- have no trivial representation collision
- HATS learn SOS representation following 2 properties variational learning methods for HATS, giving state of the art results in multiple tasks
TUBE: Embedding Behavior Outcomes for Predicting Success
Daheng Wang (University of Notre Dame); Tianwen Jiang (University of Notre Dame & Harbin Institute of Technology); Nitesh V. Chawla (University of Notre Dame); Meng Jiang (University of Notre Dame)
purpose
- predicting the success of publishing papers
- predicting effective medical resource for alleviating symptoms
Given a goal and a plan, how to predict the effectiveness of the plan
task: goal prediction: given plan p predict goals g that is likely to achieve
context recommendation: given goal and plan recommend item to be added into that maximize effectiveness.
plan effectiveness as point-to-point distance?
point-to-point distance is not effectiveness
Approach: Embeds Behavior oUTcomes TUBE
Approach: optimization
distance between observed and predicted distribution
KL-divergence distance
Approach: negative samples
experiments: building synthetic datasets
- simulate "game" scenarios of forming a team of players for passing game stage
experiments: synthetic datasets
experiments: synthetic datasets results
task: goal prediction and context recommendation
conclusion
- new problems representation learning for behavior context items and goals for success prediction
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
Ahmed Rashed (University of Hildesheim); Josif Grabocka (University of Hildesheim); Lars Schmidt-Thieme (University of Hildesheim) Learning Network-to-Network Model for Content-rich Network Embedding Zhicheng He (Nankai University); Jie Liu (Nankai University); Na Li (Nankai University); Yalou Huang (Nankai University)
intro
multi-relational settings: social network, citation networks, biological interactive
goals: predicting the missing edges
main challenges
- extremely sparse relation
- attributed entities
- proposed method bayesian ranked non-linear embedding
multi-relational classification
related work
- attribute-aware models
- non-attribute-aware models
- bayesian ranked non-linear model
step1: non-linear embeddings
step2: scoring function
step3: bayesian personalized ranking
data: Cora, Citeseer, PPI, email-Eu-core
conclusion
- propose Bayesian ranked non-linear embeddings(BRNLE)
- easily extendable for an arbitrary number od relation of entities