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