understanding black box predictions via influence functions

You will be redirected to the full text document in the repository in a few seconds, if not click here.click here. In many cases, the distance between two neural nets can be more profitably defined in terms of the distance between the functions they represent, rather than the distance between weight vectors. The paper deals with the problem of finding infuential training samples using the Infuence Functions framework from classical statistics recently revisited in the paper "Understanding Black-box Predictions via Influence Functions" (code).The classical approach, however, is only applicable to smooth . They use influence functions, a classic technique from robust statistics (Cook & Weisberg, 1980) that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. P. Koh , and P. Liang . Security of Deep Learning. Let's study the change in model parameters due to removing a point zfrom training set: ^ z def= argmin 2 1 n X z i6=z L(z i; ) Than, the change is given by: ^ z . a model predicts in this . Best-performing models: complicated, black-box . In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the. . Understanding self-training for gradual domain adaptation. The datasets for the experiments . In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training . Understanding Black-box Predictions via Influence Functions Figure 3. Understanding Black- box Predictions via Influence Functions Pang Wei Koh， Percy Liang Stanford University ICML2017 DLゼミ 小川一太郎 2. In this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Convexified convolutional neural networks. How can we explain the predictions of a blackbox model? •Pearlmutter, B. Often we want to identify an influential group of training samples in a particular test prediction. Pang Wei Koh (Stanford), Percy Liang (Stanford) ICML 2017 Best Paper Award. International Conference on Machine . In SIGIR. Abstract: How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, identifying the points most responsible for a given prediction. Understanding Black-box Predictions via Influence Functions. ICML, 2017. pytorch-influence-functionsRelease 0.1.1. This . In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of . DNN等の複雑なモデルに対する影響関数の効率的な計算手法の提案 ナイーブに行うとパラメータ数の二乗のオーダーの計算となり、不可能 3. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. The datasets for the experiments . In International Conference on Machine Learning (ICML), pp. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. This package is a plug-n-play PyTorch reimplementation of Influence Functions. A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of . Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Pang Wei Koh, Percy Liang. The . 简介. The influence function could be very useful to understand and debug deep learning models. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data . A. 影響関数(influence function)を用いて、特定の学習データの有無や、 学習データに加える摂動が予測結果に与える影響を定式化 2. Contact; Boutique. 2018 link International Conference on Machine Learning (ICML), 2017. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv . Koh, Pang Wei, and Percy Liang. We are not allowed to display external PDFs yet. Abstract. Metrics give a local notion of distance on a manifold. This . Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. Then we . This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang. Pang Wei Koh and Percy Liang "Understanding Black-box Predictions via Influence Functions" ICML2017: class Influence (workspace, feeder, loss_op_train, loss_op_test, x_placeholder, y_placeholder, test_feed_options=None, train_feed_options=None, trainable_variables=None) [source] ¶ Influence Class. This Dockerfile specifies the run-time environment for the experiments in the paper "Understanding Black-box Predictions via Influence Functions" (ICML 2017). How can we explain the predictions of a black-box model? ICML , volume 70 of Proceedings of Machine Learning Research, page 1885-1894. Understanding black-box predictions via influence functions. 735-742, 2010. Ananya Kumar, Tengyu Ma, Percy Liang. While this might be useful for . How can we explain the predictions of a black-box model? The reference implementation can be found here: link. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Pang Wei Koh and Percy Liang. Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Influence Functions Pang Wei Koh & Perry Liang Presented by -Theo, Aditya, Patrick 1 1.Influence functions: definitions and theory 2.Efficiently calculating influence functions 3. lonely planet restaurant. Influence Functions: Understanding Black-box Predictions via Influence Functions. International Conference on Machine Learning (ICML), 2017. Different machine learning models have different ways of making predictions. This approach can give more exact explanation to a given prediction. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . ICML, 2017. Even if two models have the same performance, the way they make predictions from the features can be very different and therefore fail in different scenarios. 5. Understanding Black-box Predictions via Influence Functions. 2019. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Understanding Black-box Predictions via Influence Functions. Influence Functions for PyTorch. International conference on machine learning, 1885-1894, 2017. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… Based on some existing implementations, I'm developing reliable Pytorch implementation of influence function. Fast exact multiplication by the . tion (Krizhevsky et al.,2012) — are complicated, black-box models whose predictions seem hard to explain. 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . [ICML] Understanding Black-box Predictions via Influence Functions 李浩 低头玩手机相当于在脖子上挂两个大铁球。 156 人 赞同了该文章 1.摘要 本文是ICML 2017 best paper，来自Stanford的Pang Wei Koh和Percy liang。 文章从训练数据的角度出发，解释模型的预测结果。 具体地说，输入一个测试样本 ，模型给出了预测结果 ，我们想知道这一行为与哪些训练样本关系最大；换个角度说，把这些训练样本去掉，或者改变他们的label，那模型就很可能在 上给出不同的预测结果。 2.方法 假设有 个训练样本 ，其中 ， 令 表示样本 在模型参数为 下的损失函数，则经验风险为 Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. Let's study the change in model parameters due to removing a point zfrom training set: ^ z def= argmin 2 1 n X z i6=z L(z i; ) Than, the change is given by: ^ z . This work takes a novel look at black box interpretation of test predictions in terms of training examples, making use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. Understanding black-box predictions via influence functions. In this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Influence function for neural networks is proposed in the ICML2017 best paper (Wei Koh & Liang, 2017). Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. 1644 : 2017: Mobility network models of COVID-19 explain inequities and inform reopening. Understanding Black-box Predictions via Influence Functions. Validations 4. Proc 34th Int Conf on Machine Learning, p.1885-1894. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. How would the model's predictions change if didn't have particular training point? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. In this paper, they tackle this question by tracing a model's predictions through its learning algorithm and back to the training data, where the model parameters ultimately derive from. 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine . Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:1885-1894 •Martens, J. Training point influence Slides: Released Interpreting Interpretations: Organizing Attribution Methods by Criteria Representer point selection for DNN Understanding Black-box Predictions via Influence Functions: Pre-recorded lecture: Released Homework 2: Released Description: In Homework 2, students gain hands-on exposure to a variety of explanation toolkits. Google Scholar How can we explain the predictions of a black- box model? Understanding black-box predictions via influence functions. 63 Highly Influenced PDF View 10 excerpts, cites methods and background How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions. Metrics give a local notion of distance on a manifold. International Conference on Machine Learning (ICML), 2017. Understanding black-box predictions via influence functions. How can we explain the predictions of a black-box model? This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… 2017. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. Understanding Black-box Predictions via Influence Functions. Understanding Black-box Predictions via Influence Functions. Correspondence to: In this paper, they tackle this question by tracing a model's predictions through its learning algorithm and back to the training data, where the model parameters ultimately derive from. 3: 1/27: Metrics. will a model make and . Here, we plot I up,loss against variants that are missing these terms and show that they are necessary for picking up the truly influential training points. Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang. However, to the best of my knowledge, there is no generic PyTorch implementation with reliable test codes. why. Influence functions help you to debug the results of your deep learning model in terms of the dataset. How can we explain the predictions of a black-box model? Basu et. 这是ICML 2017的最佳论文，有时候虽然神经网络模型取得了非常高的预测精度，但是却无法解释模型是怎么得到这些结果的，这篇论文可以在一定程度上理解模型对于数据的敏感程度，分析每一个数据的变化 . 1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2.College of Intelligence and Computing, Tianjin University, Tianjin 300072, China; Received:2018-11-30 Online:2019-02-28 Published:2020-08-21 Understanding the particular weaknesses of a model by identifying influential instances helps to form a "mental model" of the . Understanding Black-box Predictions via Influence Functions. Koh P, Liang P, 2017. S Chang*, E Pierson*, PW Koh*, J Gerardin, B Redbird, D Grusky, . Work on interpreting these black-box models has focused on un-derstanding how a fixed model leads to particular predic-tions, e.g., by locally fitting a simpler model around the test 1Stanford University, Stanford, CA. Yeh et. al. How can we explain the predictions of a black-box model? Imagenet classification with deep convolutional neural networks. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. explainability. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. If a model's influential training points for a specific action are unrelated to this action, we might suppose that . 3: 1/28: Metrics. Title:Understanding black-box predictions via influence functions by Pang Wei Koh, Percy Liang, International Conference on Machine Learning (ICML), 2017 November 14, 2017 Speaker: Jiae Kim Title: The Geometry of Nonlinear Embeddings in Discriminant Analysis with Gaussian Kernel 作者也分别在不同规模 (CIFAR, ImageNet)和不同应用 (Classification, Denoising)中证明了 . First, a local prediction explanation has been designed, which combines the key training points identified via influence function and the framework of LIME. Background. Modern deep learning models for NLP are notoriously opaque. (a) Compared to I up,loss, the inner product is missing two key terms, train loss and H^θ. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Here is an open source project that implements calculation of the influence function for any Tensorflow models. 実際の解析例 . (a) By varying t, we can approximate the hinge loss with arbitrary accuracy: the green and blue lines are overlaid on top of each other. In ICML. Uses cases Roadmap 2 Koh and Liang 2017 link; Influence Functions and Non-convex models: Influence functions in Deep Learning are Fragile. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only . (b) Using a random, wrongly-classified test point, we compared the predicted vs. actual differences in loss after leave-one-out retraining on the . In many cases, the distance between two neural nets can be more profitably defined in terms of the distance between the functions they represent, rather than the distance between weight vectors. Figure 1: Influence functions vs. Euclidean inner product. PW Koh, P Liang. How can we explain the predictions of a black-box model? 2020 link; Representer Points: Representer Point Selection for Explaining Deep Neural Networks. 783: 2020: Peer and self assessment in massive online classes. Koh, Pang Wei. Nature, 1-6, 2020. Tensorflow KR에서 진행하고 있는 논문읽기 모임 PR12에서 발표한 저의 네번째 발표입니다. This is the Dockerfile: FROM tensorflow/tensorflow:1.1.-gpu MAINTAINER Pang Wei Koh koh.pangwei@gmail.com RUN apt-get update && apt-get install -y python-tk RUN pip install keras==2.0.4 . To make the approach efficient, we propose a fast and effective approximation of the influence function. Understanding black-box predictions via influence functions. Nos marques; Galeries; Wishlist When testing for a single test image, you can then calculate which training images had the largest result on the classification outcome. Smooth approximations to the hinge loss. Table 2: Counterfactual sets generated by ACCENT . NIPS, p.1097-1105. They use influence functions, a classic technique from robust statistics (Cook & Weisberg, 1980) that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. On linear models and ConvNets, we show that influence functions can be used to understand model behavior, Understanding Black-box Predictions via Influence Functions. How would the model's predictions change if didn't have particular training point? Honorable Mentions. This repository implements the LeafRefit and LeafInfluence methods described in the paper __.. How a fixed model leads to particular predictions, i.e., what predictions . C Kulkarni, PW . How can we explain the predictions of a black-box model? In this paper, we proposed a novel model explanation method to explain the predictions or black-box models. Tensorflow KR에서 진행하고 있는 논문읽기 모임 PR12에서 발표한 저의 네번째 발표입니다. In this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Pang Wei Koh 1, Percy Liang 1 • Institutions (1) 14 Mar 2017-arXiv: Machine Learning. Yuchen Zhang, Percy Liang, Martin J. Wainwright. Baselines: Influence estimation methods & Deep KNN [4] poison defense Attack #1: Convex polytope data poisoning [5] on CIFAR10 Attack #2: Speech recognition backdoor dataset [6] References Experimental Results Using CosIn to Detect a Target [1] Koh et al., "Understanding black-box predictions via influence functions" ICML, 2017. Criticism for Interpretability: Xu Chu Nidhi Menon Yue Hu : 11/15: Reducing Training Set: Introduction to papers in this class LightGBM: A Highly Efficient Gradient Boosting Decision Tree BlinkML: Approximate Machine Learning with Probabilistic Guarantees: Xu Chu Eric Qin Xiang Cheng . Why Use Influence Functions? Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. old friend extra wide slippers. of ML models. "Understanding black-box predictions via influence functions." arXiv preprint arXiv:1703.04730 (2017). What is now often being studied? Tue Apr 12: More deep learning . Understanding Black-box Predictions via Influence Functions and Estimating Training Data Influence by Tracking Gradient Descent are both methods designed to find training data which is influential for specific model decisions. Lost Relatives of the Gumbel Trick Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. 4. How can we explain the predictions of a black-box model? To scale up influence . How can we explain the predictions of a black-box model?

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