Different machine learning models have different ways of making predictions. Understanding Black-box Predictions via Influence Functions. NeurIPS materials . Koh, Pang Wei, and Percy Liang. Convexified convolutional neural networks. This . Often we want to identify an influential group of training samples in a particular test prediction. 735-742, 2010. How can we explain the predictions of a blackbox model? 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. 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. Understanding Black-box Predictions via Influence Functions. 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, 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. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. The influence function could be very useful to understand and debug deep learning models. 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 . 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 . How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions. Nature, 1-6, 2020. Understanding Black-box Predictions via Influence Functions Figure 3. "Understanding black-box predictions via influence functions." arXiv preprint arXiv:1703.04730 (2017). Understanding Black-box Predictions via Influence Functions. Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Metrics give a local notion of distance on a manifold. 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. How can we explain the predictions of a black-box model? 2020 link; Representer Points: Representer Point Selection for Explaining Deep Neural Networks. Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017). Security of Deep Learning. Understanding black-box predictions via influence functions. Background. 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 . How can we explain the predictions of a black-box model? 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 . 2019. In this paper, we proposed a novel model explanation method to explain the predictions or black-box models. (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. ICML, 2017. Modern deep learning models for NLP are notoriously opaque. Understanding Black-box Predictions via Influence Functions. Understanding Black-box Predictions via Influence Functions. 作者也分别在不同规模 (CIFAR, ImageNet)和不同应用 (Classification, Denoising)中证明了 . In this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning. How would the model's predictions change if didn't have particular training point? Ananya Kumar, Tengyu Ma, Percy Liang. a model predicts in this . Contact; Boutique. Table 2: Counterfactual sets generated by ACCENT . ICML, 2017. lonely planet restaurant. Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. Abstract: How can we explain the predictions of a black-box model? In International Conference on Machine Learning (ICML), pp. 3: 1/27: Metrics. Koh and Liang 2017 link; Influence Functions and Non-convex models: Influence functions in Deep Learning are Fragile. uence functions The goal is to understand the e ect of training points to model's predictions. 2017. ICML , volume 70 of Proceedings of Machine Learning Research, page 1885-1894. International conference on machine learning, 1885-1894, 2017. Pang Wei Koh 1, Percy Liang 1 • Institutions (1) 14 Mar 2017-arXiv: Machine Learning. Understanding Black-box Predictions via Influence Functions. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only . Uses cases Roadmap 2 Here is an open source project that implements calculation of the influence function for any Tensorflow models. (a) Compared to I up,loss, the inner product is missing two key terms, train loss and H^θ. 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. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. 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. First, a local prediction explanation has been designed, which combines the key training points identified via influence function and the framework of LIME. S Chang*, E Pierson*, PW Koh*, J Gerardin, B Redbird, D Grusky, . 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. 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. Understanding black-box predictions via influence functions. Koh P, Liang P, 2017. of ML models. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. How can we explain the predictions of a black-box model? Influence Functions: Understanding Black-box Predictions via Influence Functions. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… Validations 4. 1.1. How can we explain the predictions of a black-box model? 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. why. Best paper award. How can we explain the predictions of a black-box model? This approach can give more exact explanation to a given prediction. 简介. This repository implements the LeafRefit and LeafInfluence methods described in the paper __.. Metrics give a local notion of distance on a manifold. The datasets for the experiments . International Conference on Machine Learning (ICML), 2017. International Conference on Machine Learning (ICML), 2017. Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:1885-1894 •Martens, J. Parameters: workspace - Path for workspace directory; feeder (InfluenceFeeder) - Dataset . With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Based on some existing implementations, I'm developing reliable Pytorch implementation of influence function. Understanding Black-box Predictions via Influence Functions. This Dockerfile specifies the run-time environment for the experiments in the paper "Understanding Black-box Predictions via Influence Functions" (ICML 2017). 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. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . International Conference on Machine . How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions (ICML 2017 Best Paper) DeepXplore: Automated Whitebox Testing of Deep Learning Systems (SOSP 2017 Best Paper) Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data(ICLR 2017 Best Paper) Overview of Deep Learning and Security in 2017. 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 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 This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… We are not allowed to display external PDFs yet. •Pearlmutter, B. Smooth approximations to the hinge loss. This package is a plug-n-play PyTorch reimplementation of Influence Functions. 783: 2020: Peer and self assessment in massive online classes. Koh, Pang Wei. Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang. tion (Krizhevsky et al.,2012) — are complicated, black-box models whose predictions seem hard to explain. Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine . 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. 実際の解析例 . Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. Proc 34th Int Conf on Machine Learning, p.1885-1894. P. Koh , and P. Liang . To make the approach efficient, we propose a fast and effective approximation of the influence function. Deep learning via hessian-free optimization. Understanding Black-box Predictions via Influence Functions. 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . Imagenet classification with deep convolutional neural networks. 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. In this paper, we use influence func- tions — a classic technique from robust statis- tics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most respon- sible for a given prediction. Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! Pang Wei Koh (Stanford), Percy Liang (Stanford) ICML 2017 Best Paper Award. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv . 3: 1/28: Metrics. 63 Highly Influenced PDF View 10 excerpts, cites methods and background The reference implementation can be found here: link. 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.
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