P. Koh , and P. Liang . 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. Often we want to identify an influential group of training samples in a particular test prediction. What is now often being studied? tion (Krizhevsky et al.,2012) are complicated, black-box models whose predictions seem hard to explain. 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. Tensorflow KR PR12 . Understanding Black-box Predictions via Influence Functions. Proc 34th Int Conf on Machine Learning, p.1885-1894. In International Conference on Machine Learning (ICML), pp. Influence Functions for PyTorch. 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 . Understanding Black-box Predictions via Influence Functions. Here is an open source project that implements calculation of the influence function for any Tensorflow models. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. Understanding Black-box Predictions via Influence Functions. . Ananya Kumar, Tengyu Ma, Percy Liang. How can we explain the predictions of a black-box model? (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. Background. Figure 1: Influence functions vs. Euclidean inner product. Influence function for neural networks is proposed in the ICML2017 best paper (Wei Koh & Liang, 2017). influenceloss. ICML, 2017. (a) Compared to I up,loss, the inner product is missing two key terms, train loss and H^. Influence Functions: Understanding Black-box Predictions via Influence Functions. 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. 63 Highly Influenced PDF View 10 excerpts, cites methods and background 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 "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv . ICML2017 " . In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the. We are not allowed to display external PDFs yet. Yeh et. International Conference on Machine Learning (ICML), 2017. Modern deep learning models for NLP are notoriously opaque. Pang Wei Koh, Percy Liang. When testing for a single test image, you can then calculate which training images had the largest result on the classification outcome. Imagenet classification with deep convolutional neural networks. Understanding black-box predictions via influence functions. uence functions The goal is to understand the e ect of training points to model's predictions. 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. This Dockerfile specifies the run-time environment for the experiments in the paper "Understanding Black-box Predictions via Influence Functions" (ICML 2017). 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. 2019. How can we explain the predictions of a black- box model? Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. 735-742, 2010. Abstract: How can we explain the predictions of a black-box model? To scale up influence . 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. Understanding self-training for gradual domain adaptation. How can we explain the predictions of a black-box model? Koh P, Liang P, 2017. Nature, 1-6, 2020. DNN 3. To make the approach efficient, we propose a fast and effective approximation of the influence function. old friend extra wide slippers. 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. In ICML. 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. Yuchen Zhang, Percy Liang, Martin J. Wainwright. 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. ICML2017 " . How can we explain the predictions of a black-box model? Contact; Boutique. International Conference on Machine Learning (ICML), 2017. Pang Wei Koh (Stanford), Percy Liang (Stanford) ICML 2017 Best Paper Award. How would the model's predictions change if didn't have particular training point? The datasets for the experiments . They use inuence 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 innitesimal amount. Understanding Black-box Predictions via Influence Functions. al. Based on some existing implementations, I'm developing reliable Pytorch implementation of influence function. Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang. Why Use Influence Functions? How a fixed model leads to particular predictions, i.e., what predictions . 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. The . C Kulkarni, PW . A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of . S Chang*, E Pierson*, PW Koh*, J Gerardin, B Redbird, D Grusky, . (influence function) 2. In this paper, we use inuence 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. You will be redirected to the full text document in the repository in a few seconds, if not click here.click here. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. Pearlmutter, B. Best-performing models: complicated, black-box . Understanding black-box predictions via influence functions. Koh and Liang 2017 link; Influence Functions and Non-convex models: Influence functions in Deep Learning are Fragile. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality ICML, 2017. PW Koh, P Liang. This approach can give more exact explanation to a given prediction. In SIGIR. (CIFAR, ImageNet) (Classification, Denoising) . 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. 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. How can we explain the predictions of a black-box model? Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Correspondence to: why. How can we explain the predictions of a blackbox model? Understanding black-box predictions via influence functions. ICML 2017 . Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017). Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine . This . (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 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." arXiv preprint arXiv:1703.04730 (2017). NIPS, p.1097-1105. Basu et. International conference on machine learning, 1885-1894, 2017. Work on interpreting these black-box models has focused on un-derstanding how a xed model leads to particular predic-tions, e.g., by locally tting a simpler model around the test 1Stanford University, Stanford, CA. With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. 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. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. 3: 1/28: Metrics. A. Understanding Black-box Predictions via Influence Functions Figure 3. . How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions. ; Liang, Percy. Understanding Black-box Predictions via Influence Functions. 3: 1/27: Metrics. Pang Wei Koh 1, Percy Liang 1 Institutions (1) 14 Mar 2017-arXiv: Machine Learning. Koh, Pang Wei. Table 2: Counterfactual sets generated by ACCENT . How can we explain the predictions of a black-box model? 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 . They use inuence 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 innitesimal amount. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. 5. Understanding Black-box Predictions via Influence Functions. Then we . 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 (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. 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 Blackbox Predictions via Influence Functions 1. 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. 2020 link; Representer Points: Representer Point Selection for Explaining Deep Neural Networks. Understanding model behavior. This . Understanding black-box predictions via influence functions. We use inuence 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. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . 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 . of ML models. Different machine learning models have different ways of making predictions. Applying deep learning to solve security . Convexified convolutional neural networks. International Conference on Machine . 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 . Deep learning via hessian-free optimization. Tensorflow KR PR12 . 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. Influence functions help you to debug the results of your deep learning model in terms of the dataset. Metrics give a local notion of distance on a manifold. 4. 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 . Validations 4. How can we explain the predictions of a black-box model? Koh, Pang Wei, and Percy Liang. Nos marques; Galeries; Wishlist Pang Wei Koh and Percy Liang. If a model's influential training points for a specific action are unrelated to this action, we might suppose that . We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Understanding black-box predictions via influence functions. How can we explain the predictions of a black-box model? a model predicts in this . Understanding Black-box Predictions via Influence Functions. Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. 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. Honorable Mentions. Uses cases Roadmap 2 [ICML] Understanding Black-box Predictions via Influence Functions 156 1. ICML 2017 best paperStanfordPang Wei KohPercy liang label 2. How can we explain the predictions of a black-box model? Fast exact multiplication by the . 783: 2020: Peer and self assessment in massive online classes. Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. . We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Understanding black-box predictions via influence functions. 2018 link While this might be useful for . 2017. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of . explainability. Criticism for Interpretability: Xu Chu Nidhi Menon Yue Hu : 11/15: Reducing Training Set: Introduction to papers in this class LightGBM: A Highly Efcient 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 Pang Wei Koh Percy Liang Stanford University ICML2017 DL 2. al. 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 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 . However, to the best of my knowledge, there is no generic PyTorch implementation with reliable test codes. uence functions The goal is to understand the e ect of training points to model's predictions. On linear models and ConvNets, we show that inuence functions can be used to understand model behavior, The influence function could be very useful to understand and debug deep learning models. ICML , volume 70 of Proceedings of Machine Learning Research, page 1885-1894. Smooth approximations to the hinge loss. 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. Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! will a model make and . 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. Tue Apr 12: More deep learning . This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality pytorch-influence-functionsRelease 0.1.1. Google Scholar In this paper, we proposed a novel model explanation method to explain the predictions or black-box models. 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. Parameters: workspace - Path for workspace directory; feeder (InfluenceFeeder) - Dataset . Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:1885-1894 Martens, J. Abstract. International Conference on Machine Learning (ICML), 2017. 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. 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 . Best paper award. This repository implements the LeafRefit and LeafInfluence methods described in the paper __.. 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. This package is a plug-n-play PyTorch reimplementation of Influence Functions. Metrics give a local notion of distance on a manifold. Lost Relatives of the Gumbel Trick Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller.