A Novel Local Patch Framework for Fixing Supervised Learning Models
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A Novel Local Patch Framework for Fixing Supervised Learning Models. Yilei Wang 1 , Bingzheng Wei 2 , Jun Yan 2 , Yang Hu 2 , Zhi-Hong Deng 1 , Zheng Chen 2. 1 Peking University 2 Microsoft Research Asia. Outline. Motivation & Background Problem Definition & Algorithm Overview

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A Novel Local Patch Framework for Fixing Supervised Learning Models

Yilei Wang1, Bingzheng Wei2, Jun Yan2, Yang Hu2, Zhi-Hong Deng1, Zheng Chen2

1Peking University

2Microsoft Research Asia

Outline Models

  • Motivation & Background

  • Problem Definition & Algorithm Overview

  • Algorithm Details

  • Experiments - Classification

  • Experiments - Search Ranking

  • Conclusion

Motivation background
Motivation & Background Models

  • Supervised Learning:

    • Machine Learning task of inferring a function from labeled training data

  • Prediction Error:

    • No matter how strong a learning model is, it will suffer from prediction errors.

    • Noise in training data, dynamically changing data distribution, weakness of learner

  • Feedback from User:

    • Good signal for learning models to find the limitation and then improve accordingly

Learning to fix errors from failure cases
Learning to Fix Errors from Failure Cases Models

  • Automatically fix model prediction errors from failure cases in feedback data.

    • Input:

      • A well trained supervised model (we name it as Mother Model)

      • A collection of failure cases in feedback dataset.

    • Output:

      • Learning to automatically fix the model bugs from failure cases

  • Previous Works

    • Model Retraining

    • Model Aggregation

    • Incremental Learning

Local patching from global to local
Local Patching: from ModelsGlobal to Local

  • Learning models are generally optimized globally

    • Introducing new prediction errors when fixing the old ones

  • Our key idea: learning to fix the model locally using patches

New Error

New Error

Problem definition
Problem Definition Models

  • Our proposed Local Patch Framework(LPF) aims to learn a new model

    • : the original mother model

    • : Patch model

    • : Gaussian distribution defined by a centroid and a range

Algorithm overview
Algorithm Overview Models

  • Failure Case Collection

  • Learning Patch Regions/Failure Case Clustering

    • Clustering Failure Cases into N groups through subspace learning, compute the centroid and range for every group, then define our patches

  • Learning Patch Model

    • Learn a patch model using only the data samples that sufficiently close to the patch centroid

Learning patch region key challenge
Learning Patch Region – Key Challenge Models

  • Failure cases may distribute diffusely

    • Small N = large patch range → many success cases will be patched

    • Big N = small patch range → high computational complexity

  • How to make trade-offs ?

Solution clustered metric learning
Solution: Clustered Metric Learning Models

  • Our solution to diffusion: Metric Learning

    • Learn a distance metric, i.e. subspace, for failure cases, such that the similar failure cases will aggregate, and keep distant from the success cases.

      (Red circle = failure cases; blue circle = success cases)

      Key idea of the patch model learning

      • (Left): The cases in original data space.

      • (Middle): The cases mapped to the learned subspace.

      • (Right): Repair the failure cases using a single patch.

Metric learning
Metric Learning Models

  • Conditional distribution over

  • Ideal distribution

  • Learn to satisfy

  • Which is equivalent to maximize

Clustered metric learning
Clustered Metric Learning Models

  • Algorithm:

    • 1. Initialize each failure case with a random group

    • 2. Repeat the following steps:

      • a) For the given clusters, proceeds metric learning step

      • b) Update the centroids of the groups, and re-assign the failure cases to its closest centroid.

  • Local Patch Region:

    • For each cluster i, we define a corresponding patch with as its centroid , and as its variance

    • Gaussian weight:

Learning patch model
Learning Patch Model Models

  • Objective:

    • Where are the parameters, are the labels

  • Update parameter:

  • For /, we have

    • Notice: dependent on the specific patch model

Experiments Models

Experiments classification
Experiments - Classification Models

  • Dataset

    • Randomly select 3 UCI subset

      • Spambase, Waveform, Optical Digit Recognition

      • Convert to binary classification dataset

      • ~5000 instances in each dataset

      • Split to: 60% - training, 20% - feedback, 20% - test

  • Baseline Algorithm

    • SVM

    • Logistic Regression

    • SVM - retrained with training + feedback data

    • Logistic Regression - retrained with training + feedback data

    • SVM – Incremental Learning

    • Logistic Regression - Incremental Learning

Classification accuracy
Classification Accuracy Models

  • Classification accuracy on feedback dataset

  • Classification accuracy on test dataset

Parameter tuning
Parameter Tuning Models

  • Number of Patches

    • Data sensitive, in our experiment the best N is 2

Experiments search ranking
Experiments – Search Ranking Models

  • Dataset

    • Data from a commonly used commercial search engine

    • ~14, 126 <q, d> pairs

    • With 5 grades label

  • Metrics

  • Baseline Algorithm

    • GBDT

    • GBDT + IL

Experiment results ranking cont
Experiment Results – ModelsRanking (Cont.)

Conclusion Models

  • We proposed

    • The local model fixing problem

    • A novel patch framework fox fixing the failure cases in feedback dataset in local view

  • The experiment results demonstrate the effectiveness of our proposed Local Patch Framework

Thank you! Models