understanding black box predictions via influence functions

Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! Best paper award. (influence function) 2. First, a local prediction explanation has been designed, which combines the key training points identified via influence function and the framework of LIME. 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. 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 . Understanding the particular weaknesses of a model by identifying influential instances helps to form a "mental model" of the . ; Liang, Percy. International conference on machine learning, 1885-1894, 2017. 2019. In this paper, we use influence functions a classic technique from robust statistics to trace a model's prediction through the learning. How a fixed model leads to particular predictions, i.e., what predictions . The reference implementation can be found here: link. 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 . We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Understanding Blackbox Predictions via Influence Functions 1. On linear models and ConvNets, we show that inuence functions can be used to understand model behavior, Imagenet classification with deep convolutional neural networks. How would the model's predictions change if didn't have particular training point? "Understanding black-box predictions via influence functions." arXiv preprint arXiv:1703.04730 (2017). . Pang Wei Koh (Stanford), Percy Liang (Stanford) ICML 2017 Best Paper Award. NIPS, p.1097-1105. 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? 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. 2018 link Understanding Black-box Predictions via Influence Functions. tion (Krizhevsky et al.,2012) are complicated, black-box models whose predictions seem hard to explain. 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. (a) Compared to I up,loss, the inner product is missing two key terms, train loss and H^. Basu et. When testing for a single test image, you can then calculate which training images had the largest result on the classification outcome. 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 . Koh, Pang Wei. 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 Metrics give a local notion of distance on a manifold. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. How can we explain the predictions of a black-box model? Uses cases Roadmap 2 A. Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. Koh P, Liang P, 2017. Then we . International Conference on Machine Learning (ICML), 2017. uence functions The goal is to understand the e ect of training points to model's predictions. This . This . Metrics give a local notion of distance on a manifold. A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of . We are not allowed to display external PDFs yet. 1644 : 2017: Mobility network models of COVID-19 explain inequities and inform reopening. Do you remember "Understanding Black-box Predictions via Influence Functions", the best paper at ICML this year? 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. Applying deep learning to solve security . Parameters: workspace - Path for workspace directory; feeder (InfluenceFeeder) - Dataset . 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. Lost Relatives of the Gumbel Trick Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. Background. 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. 783: 2020: Peer and self assessment in massive online classes. Influence function for neural networks is proposed in the ICML2017 best paper (Wei Koh & Liang, 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. While this might be useful for . Understanding Black-box Predictions via Influence Functions. How can we explain the predictions of a black-box model? How can we explain the predictions of a black-box model? al. Deep learning via hessian-free optimization. Pang Wei Koh and Percy Liang. Abstract: How can we explain the predictions of a black-box model? Understanding model behavior. 5. Koh, Pang Wei, and Percy Liang. 3: 1/27: Metrics. uence functions The goal is to understand the e ect of training points to model's predictions. In International Conference on Machine Learning (ICML), pp. lonely planet restaurant. International Conference on Machine . Understanding Black-box Predictions via Influence Functions. 3: 1/28: Metrics. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality Modern deep learning models for NLP are notoriously opaque. Honorable Mentions. Pearlmutter, B. 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. Different machine learning models have different ways of making predictions. 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. Abstract. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang. Tensorflow KR PR12 . Based on some existing implementations, I'm developing reliable Pytorch implementation of influence function. 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. S Chang*, E Pierson*, PW Koh*, J Gerardin, B Redbird, D Grusky, . 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. Contact; Boutique. 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 . The datasets for the experiments . Convexified convolutional neural networks. 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. 2017. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of . This approach can give more exact explanation to a given prediction. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. Understanding Black-box Predictions via Influence Functions. Understanding black-box predictions via influence functions. Figure 1: Influence functions vs. Euclidean inner product. explainability. Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. Understanding black-box predictions via influence functions. Influence Functions for PyTorch. Table 2: Counterfactual sets generated by ACCENT . Understanding Black-box Predictions via Influence Functions. Influence Functions: Understanding Black-box Predictions via Influence Functions. Understanding Black- box Predictions via Influence Functions Pang Wei Koh Percy Liang Stanford University ICML2017 DL 2. ICML2017 " . Understanding black-box predictions via influence functions. (CIFAR, ImageNet) (Classification, Denoising) . Ananya Kumar, Tengyu Ma, Percy Liang. Yuchen Zhang, Percy Liang, Martin J. Wainwright. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality Nos marques; Galeries; Wishlist ICML2017 " . Smooth approximations to the hinge loss. 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. P. Koh , and P. Liang . How can we explain the predictions of a black-box model? Influence functions help you to debug the results of your deep learning model in terms of the dataset. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . Pang Wei Koh, Percy Liang. Nature, 1-6, 2020. Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Often we want to identify an influential group of training samples in a particular test prediction. influenceloss. . Understanding Black-box Predictions via Influence Functions. Fast exact multiplication by the . To scale up influence . will a model make and . In this paper, we proposed a novel model explanation method to explain the predictions or black-box models. ICML , volume 70 of Proceedings of Machine Learning Research, page 1885-1894. International Conference on Machine Learning (ICML), 2017. Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:1885-1894 Martens, J. 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 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. a model predicts in this . International Conference on Machine Learning (ICML), 2017. Proc 34th Int Conf on Machine Learning, p.1885-1894. Yeh et. 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. 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 repository implements the LeafRefit and LeafInfluence methods described in the paper __.. Here is an open source project that implements calculation of the influence function for any Tensorflow models. Understanding Black-box Predictions via Influence Functions. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Understanding black-box predictions via influence functions. why. Understanding Black-box Predictions via Influence Functions. Google Scholar To make the approach efficient, we propose a fast and effective approximation of the influence function. 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 . Why Use Influence Functions? 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. Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. Pang Wei Koh 1, Percy Liang 1 Institutions (1) 14 Mar 2017-arXiv: Machine Learning. 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. (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. How can we explain the predictions of a black-box model? Understanding black-box predictions via influence functions. NeurIPS materials . To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only . ICML, 2017. Understanding Black-box Predictions via Influence Functions Figure 3. Correspondence to: 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 package is a plug-n-play PyTorch reimplementation of Influence Functions. How can we explain the predictions of a blackbox model? The influence function could be very useful to understand and debug deep learning models. Best-performing models: complicated, black-box . 4. pytorch-influence-functionsRelease 0.1.1. DNN 3. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv . However, to the best of my knowledge, there is no generic PyTorch implementation with reliable test codes. 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 . Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017). With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. How can we explain the predictions of a black-box model? Tue Apr 12: More deep learning . Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine . How would the model's predictions change if didn't have particular training point? 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. 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. If a model's influential training points for a specific action are unrelated to this action, we might suppose that . old friend extra wide slippers. (b) Using a random, wrongly-classified test point, we compared the predicted vs. actual differences in loss after leave-one-out retraining on the . 1.1. In ICML. 2020 link; Representer Points: Representer Point Selection for Explaining Deep Neural Networks. 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. The datasets for the experiments . ICML, 2017. [ICML] Understanding Black-box Predictions via Influence Functions 156 1. ICML 2017 best paperStanfordPang Wei KohPercy liang label 2. Understanding self-training for gradual domain adaptation. Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. You will be redirected to the full text document in the repository in a few seconds, if not click here.click here. 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. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the. PW Koh, P Liang. 63 Highly Influenced PDF View 10 excerpts, cites methods and background of ML models. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. 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. C Kulkarni, PW . What is now often being studied? How can we explain the predictions of a black-box model? This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. Understanding Black-box Predictions via Influence Functions. Validations 4. 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. 735-742, 2010. 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. 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? al. 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 SIGIR. Tensorflow KR PR12 .