understanding black box predictions via influence functions

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