#Research Track Oral Presentations: RT11 Clustering and Visualization
Deep Landscape Forecasting for Real-time Bidding Advertising
Kan Ren (Shanghai Jiao Tong University); Jiarui Qin (Shanghai Jiao Tong University); Lei Zheng (Shanghai Jiao Tong University); Zhengyu Yang (Shanghai Jiao Tong University); Weinan Zhang (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University)
Bid landscape forecasting
Simple statistic -based (KDD16, ECML16)
Function form assumption
Survival analysis methodology
Survival analysis: time-to-event data analysis
Related work
Deep learning with survival analysis
Deep landscape forecasting
Deep learning solution with solution
Inference
Losing probability by multiply conditional prob
Model architecture.
Feature vector x and varying price feature vector
Loss function:
- uncensored data
- Based on C.D.E
Visualized analysis
Conclusion
- A novel deep learning method for market price modeling
- And distribution learning , in real-time bidding advertising
- Fine-grained forecasting for individual auction
QA
features
- User from web brousa
- Adviser itself
Applied 2nd price auction
Background
- Real time bidding
Predicting Path Failure In Time-Evolving Graphs
Jia Li (The Chinese University of Hong Kong); Zhichao Han (The Chinese University of Hong Kong); Hong Cheng (The Chinese University of Hong Kong); Jiao Su (The Chinese University of Hong Kong); Pengyun Wang (Noah’s Ark Lab, HuawTechnologies); Jianfeng Zhang (Noah’s Ark Lab, Huawei Technologies); Lujia Pan (Noah’s Ark Lab, Huawei Technologies)
Focus: Path classification in a time 0rvoluving graph, which predicts the status od a path in the near future
Define:
- Time-evolving graph
- Path availability
- Path classification
Three properties
- Node correlation
- Graph structure dynamics
- Temporal dependency
Model: two-layer LRGCN, obtain the hidden representation and Self-attentive
Static graph modeling
Relational GCN (R-GCN) by Kipf er al
Two-hop simplified
Adjacent graph snapshots modeling
Before diving into sequence of graph snapshot, we first focus on two adjacent time step t-1 and t
Time- evolving graph unit
Two challenges
- Size invariance
- Node importance
SAPE: self-attentive path embedding method - (Data: predicting path failure)
The benefit of graph evolution modeling
Training efficiency
conclusion
Path classification in time-evolution
Pairwise Comparisons with Flexible Time-Dynamics
Lucas Maystre Spotify lucasm@spotify.com Victor Kristof EPFL victor.kristof@epfl.ch Matthias Grossglauser EPFL matthias.grossglauser@epfl.ch
background
How can we quantify the skill of France team?
relate work: Latent skill model
Actual setting: Data come with a timestamp
We might want to ask
How strong was France in 1972? In 2018?
This work: kickscore: skill becomes a (latent) stochastic process
Covariance function defines time dynamics
Model is conditionally parametric
Covariance function
(Model inference: )
Inference algorithm
For each observation: using EP [minka, 2001] or CVI[khan et al ., 2017]
For each item: using SSM reformulation
Conclusion:
- Pairwise comparison model with Flexi bee time-dynamics
- Linear-time inference algorithm
Modeling Extreme Events in Time Series Prediction
Daizong Ding, Mi Zhang∗ Fudan University, China Xudong Pan, Min Yang Fudan University, China Xiangnan He University of Science and Technology of China
background:
- time series prediction, RNN
relate-work:
- extreme events in time-series data
Estimated distribution of labels y
Extreme event problem in DNN
Recalling extreme event in history
Attention mechanism
Extreme value theory
Extreme value loss
Conclusion:
Optimization:
Extreme events in time-series data
Why DNN is innately weak in predicting extreme events
(A framework )
Adversarial Substructured Representation Learning for Mobile User Profiling
Pengyang Wang∗ Missouri Univ. of Sci. and Tech. MO, USA pwqt3@mst.edu Yanjie Fu∗† Missouri Univ. of Sci. and Tech. MO, USA fuyan@mst.edu Hui Xiong Rutgers University NJ, USA hxiong@rutgers.edu Xiaolin Li Nanjing University Nanjing, China lixl@nju.edu.cn
background:
Mobile user profiling, similarity graph of users, transportation, OD pairs
Adaptive interfaces by inferring trip
Human activities are spatially , temporally, and socially structural
Spatial-temporal transition patterns
(Given a user and corresponding user activity)
Preserving entire-structure
Preserving substructures
Approximating substructure detector
(Summary: generator auto encoder linked with an approximated )
Study of Node and circle substructures
Research problem
- Method users as activity graphs
- Formulate modeling specific interests as preserving substructures of user activity graphs
- Propose an adversarial substructure learning model to integrate substructure into representations
relate-work:
Conclusion: