整理筆記時,翻到兩年前去研討會的筆記
想說放著也不會增值,就整理出來了
雖然大部分的內容的印象都已經有點模糊了
不過就加減把當初的筆記湊起來

當時我覺得最有趣的論文是 Interactive Rule Refinement for Fraud Detection.
不過竟然沒有做到太多筆記

Day 1 - Keynote

  • In theoretical CS
    • Polynomial time → easy/fast
      • However, that's not always the case
      • e.g., \(O(n^{100})\)
      • When n grows, even \(O(n^2)\) is not efficient
  • We're stuck on many problems even just in \(O(n^2)\)
  • No \(N^{2-\epsilon}\) time algorithms known for
    • String matching
    • computational geometry
    • graph problem in sparse graphs
    • many problems from database
    • many other problems
  • Why are we stuck?
    • The traditional hardness in complexity tells us little about runtime
    • fine-grained hardness idea
      1. identify key hard problem
      2. ......

Large Scale Machine Learning: Where Do Relational Systems Fit In? (by Chris Jermaine)

Currently, ML community cares about new models instead of theory and fundamental ML design

ML vs AI

  • ML is one approach to AI
  • Classic AI: a programmer/expert imparting knowledge to a system
  • ML is fundamentally statistical

Intro to ML

  • Distributed ML
    • Most ML systems use a "parameter server" model
      • Essentially a distributed key-value pair
    • Negatives
      • Parameter server compute model very limiting
  • Data Parallel ML
    • Each compute server runs same computation on different data
    • Global state updated via aggregation

Want to scale out to speed up learning?

  • scale out ineffective in data parallel param server
    • no easy way to add machines and have a graph execute faster
  • Only easy way to scale out is to add compute servers

Take-Home Point

  • Current ML systems are easily applicable only to
    • Relatively small model problems
    • That is run on a single machine

Detecting Database File Tampering through Page Carving

  • Attack Vector: File Tampering
    • Occurs at the OS level → outside DBMS control
      • Bypass DBMS control
  • Page Deconstruction
    • Page Header
      • Checksum
      • PageID
      • Row Count
  • DBStorageAuditor
    • Goal: find inconsistency in storage
      • which is created by direct file manipulation

Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification

  • Time series: Any data that is ordered
  • Time Series Classification
    • similarity-based kNN (e.g., kNN-ED, kNN-DTW)
      • similarity can be unreliable
    • Shaplets
      • high computation complexity
  • Why multiscale
    • sometimes global features are more important while sometimes local features are more important
    • in this research, both global and local are considered
  • Visibility Graphs
  • Multiscale Visibility Graphs

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