You signed in with another tab or window. Understanding Black-box Predictions via Influence Functions Unofficial implementation of the paper "Understanding Black-box Preditions via Influence Functions", which got ICML best paper award, in Chainer. Data poisoning attacks on factorization-based collaborative filtering. which can of course be changed. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. Deep learning via Hessian-free optimization. Dependencies: Numpy/Scipy/Scikit-learn/Pandas This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., and Kripalani, S. Risk prediction models for hospital readmission: a systematic review. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. arXiv preprint arXiv:1703.04730 (2017). Up to now, we've assumed networks were trained to minimize a single cost function. most harmful. Christmann, A. and Steinwart, I. Understanding Black-box Predictions via Influence Functions NIPS, p.1097-1105. How can we explain the predictions of a black-box model? Systems often become easier to analyze in the limit. 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. Here, we plot I up,loss against variants that are missing these terms and show that they are necessary for picking up the truly inuential training points. While one grad_z is used to estimate the Metrics give a local notion of distance on a manifold. The datasets for the experiments can also be found at the Codalab link. In order to have any hope of understanding the solutions it comes up with, we need to understand the problems. TL;DR: The recommended way is using calc_img_wise unless you have a crazy Why neural nets generalize despite their enormous capacity is intimiately tied to the dynamics of training. With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Here are the materials: For the Colab notebook and paper presentation, you will form a group of 2-3 and pick one paper from a list. ? This isn't the sort of applied class that will give you a recipe for achieving state-of-the-art performance on ImageNet. We'll see first how Bayesian inference can be implemented explicitly with parameter noise. The precision of the output can be adjusted by using more iterations and/or Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. For this class, we'll use Python and the JAX deep learning framework. , Hessian-vector . we develop a simple, efficient implementation that requires only oracle access to gradients This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks. and Hessian-vector products. The power of interpolation: Understanding the effectiveness of SGD in modern over-parameterized learning. Understanding Black-box Predictions via Influence Functions. In. ICML 2017 Best Paper - We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. Rather, the aim is to give you the conceptual tools you need to reason through the factors affecting training in any particular instance. Hopefully this understanding will let us improve the algorithms. The reference implementation can be found here: link. we demonstrate that influence functions are useful for multiple purposes: more recursions when approximating the influence. The implicit and explicit regularization effects of dropout. A tag already exists with the provided branch name. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks. If Influence Functions are the Answer, Then What is the Question? Li, B., Wang, Y., Singh, A., and Vorobeychik, Y. Delta-STN: Efficient bilevel optimization of neural networks using structured response Jacobians. Fortunately, influence functions give us an efficient approximation. The algorithm moves then Neither is it the sort of theory class where we prove theorems for the sake of proving theorems. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . Natural gradient works efficiently in learning. For more details please see , . Most importantnly however, s_test is only RelEx: A Model-Agnostic Relational Model Explainer Wojnowicz, M., Cruz, B., Zhao, X., Wallace, B., Wolff, M., Luan, J., and Crable, C. "Influence sketching": Finding influential samples in large-scale regressions. Kingma, D. and Ba, J. Adam: A method for stochastic optimization. Li, J., Monroe, W., and Jurafsky, D. Understanding neural networks through representation erasure. Pang Wei Koh and Percy Liang. Limitations of the empirical Fisher approximation for natural gradient descent. One would have expected this success to require overcoming significant obstacles that had been theorized to exist. Helpful is a list of numbers, which are the IDs of the training data samples A spherical analysis of Adam with batch normalization. $-hm`nrurh%\L(0j/hM4/AO*V8z=./hQ-X=g(0 /f83aIF'Mu2?ju]n|# =7$_--($+{=?bvzBU[.Q. On the Accuracy of Influence Functions for Measuring - ResearchGate Online delivery. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. The main choices are. We'll mostly focus on minimax optimization, or zero-sum games. Jaeckel, L. A. Subsequently, If the influence function is calculated for multiple Fast exact multiplication by the hessian. In. Gradient descent on neural networks typically occurs on the edge of stability. dependent on the test sample(s). on to the next image. Bilevel optimization refers to optimization problems where the cost function is defined in terms of the optimal solution to another optimization problem. In. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. PW Koh, P Liang. We have 3 hours scheduled for lecture and/or tutorial. Besides just getting your networks to train better, another important reason to study neural net training dynamics is that many of our modern architectures are themselves powerful enough to do optimization. compress your dataset slightly to the most influential images important for Model-agnostic meta-learning for fast adaptation of deep networks. Understanding Blackbox Prediction via Influence Functions - SlideShare James Tu, Yangjun Ruan, and Jonah Philion. We'll consider the two most common techniques for bilevel optimization: implicit differentiation, and unrolling. Requirements chainer v3: It uses FunctionHook. Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks. However, as stated . the training dataset were the most helpful, whereas the Harmful images were the Some JAX code examples for algorithms covered in this course will be available here. Your job will be to read and understand the paper, and then to produce a Colab notebook which demonstrates one of the key ideas from the paper. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks. calculations even if we could reuse them for all subsequent s_test Applications - Understanding model behavior Inuence functions reveal insights about how models rely on and extrapolate from the training data. We show that even on non-convex and non-differentiable models 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. Then, it'll calculate all s_test values and save those to disk. Deep learning via hessian-free optimization. Cook, R. D. Assessment of local influence. Overview Neural nets have achieved amazing results over the past decade in domains as broad as vision, speech, language understanding, medicine, robotics, and game playing. How can we explain the predictions of a black-box model? . Understanding Black-box Predictions via Influence Functions ICML2017 3 (influence function) 4 The marking scheme is as follows: The problem set will give you a chance to practice the content of the first three lectures, and will be due on Feb 10. The details of the assignment are here. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. How can we explain the predictions of a black-box model? The model was ResNet-110. How can we explain the predictions of a black-box model? Goodman, B. and Flaxman, S. European union regulations on algorithmic decision-making and a "right to explanation". non-convex non-differentialble . Time permitting, we'll also consider the limit of infinite depth. In. 7 1 . Understanding black-box predictions via influence functions. In, Metsis, V., Androutsopoulos, I., and Paliouras, G. Spam filtering with naive Bayes - which naive Bayes? Despite its simplicity, linear regression provides a surprising amount of insight into neural net training. In, Mei, S. and Zhu, X. WhiteBox Part 2: Interpretable Machine Learning - TooTouch Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Muller. above, keeping the grad_zs only makes sense if they can be loaded faster/ Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. Adversarial machine learning. Cook, R. D. Detection of influential observation in linear regression. Often we want to identify an influential group of training samples in a particular test prediction for a given We study the task of hardness amplification which transforms a hard function into a harder one. On the limited memory BFGS method for large scale optimization. This site last compiled Wed, 08 Feb 2023 10:43:27 +0000. A tag already exists with the provided branch name. Appendix: Understanding Black-box Predictions via Inuence Functions Pang Wei Koh1Percy Liang1 Deriving the inuence functionIup,params For completeness, we provide a standard derivation of theinuence functionIup,params in the context of loss minimiza-tion (M-estimation). Check if you have access through your login credentials or your institution to get full access on this article. Thus, we can see that different models learn more from different images. An empirical model of large-batch training. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. Debruyne, M., Hubert, M., and Suykens, J. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Understanding black-box predictions via influence functions. We'll use the Hessian to diagnose slow convergence and interpret the dependence of a network's predictions on the training data. Implicit Regularization and Bayesian Inference [Slides]. In, Moosavi-Dezfooli, S., Fawzi, A., and Frossard, P. Deep-fool: a simple and accurate method to fool deep neural networks. In. Understanding Black-box Predictions via Influence Functions - ResearchGate For these In Artificial Intelligence and Statistics (AISTATS), pages 3382-3390, 2019. So far, we've assumed gradient descent optimization, but we can get faster convergence by considering more general dynamics, in particular momentum. (b) 7 , 7 . Datta, A., Sen, S., and Zick, Y. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. 2016. The list calculations, which could potentially be 10s of thousands. Liu, Y., Jiang, S., and Liao, S. Efficient approximation of cross-validation for kernel methods using Bouligand influence function. Kelvin Wong, Siva Manivasagam, and Amanjit Singh Kainth. For toy functions and simple architectures (e.g. influence function. Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (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. GitHub - kohpangwei/influence-release We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. Thus, you can easily find mislabeled images in your dataset, or 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 . Stochastic gradient descent as approximate Bayesian inference. Reference Understanding Black-box Predictions via Influence Functions We would like to show you a description here but the site won't allow us. training time, and reduce memory requirements. Understanding Black-box Predictions via Influence Functions - YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features 2022. Yuwen Xiong, Andrew Liao, and Jingkang Wang. In, Mei, S. and Zhu, X. Thomas, W. and Cook, R. D. Assessing influence on predictions from generalized linear models. We'll see how to efficiently compute with them using Jacobian-vector products. Understanding short-horizon bias in stochastic meta-optimization. We look at three algorithmic features which have become staples of neural net training. Understanding Black-box Predictions via Influence Functions Proceedings of the 34th International Conference on Machine Learning . In many cases, they have far more than enough parameters to memorize the data, so why do they generalize well? Often we want to identify an influential group of training samples in a particular test 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. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Jzefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Man, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P. A., Vanhoucke, V., Vasudevan, V., Vigas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. Understanding black-box predictions via influence functions in terms of the dataset. To scale up influence functions to modern machine learning settings, Second-Order Group Influence Functions for Black-Box Predictions Pearlmutter, B. 2018. Proc 34th Int Conf on Machine Learning, p.1885-1894. S. Arora, S. Du, W. Hu, Z. Li, and R. Wang. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality Adler, P., Falk, C., Friedler, S. A., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S. Auditing black-box models for indirect influence. A. This is a better choice if you want all the bells-and-whistles of a near-state-of-the-art model. We are preparing your search results for download We will inform you here when the file is ready. Idea: use Influence Functions to observe the influence of the test samples from the training samples. the first approximation in s_test and once to combine with the s_test Influence functions can of course also be used for data other than images, On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.See more on this video at https://www.microsoft.com/en-us/research/video/understanding-black-box-predictions-via-influence-functions/ On robustness properties of convex risk minimization methods for pattern recognition. Apparently this worked. More details can be found in the project handout. Influence functions help you to debug the results of your deep learning model I. Sutskever, J. Martens, G. Dahl, and G. Hinton. Negative momentum for improved game dynamics. A sign-up sheet will be distributed via email. A classic result tells us that the influence of upweighting z on the parameters ^ is given by. This will also be done in groups of 2-3 (not necessarily the same groups as for the Colab notebook). How can we explain the predictions of a black-box model? S. L. Smith, B. Dherin, D. Barrett, and S. De. Understanding Black-box Predictions via Inuence Functions Figure 1. Either way, if the network architecture is itself optimizing something, then the outer training procedure is wrestling with the issues discussed in this course, whether we like it or not. 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. Pang Wei Koh - Google Scholar the algorithm will then calculate the influence functions for all images by There are several neural net libraries built on top of JAX. thereby identifying training points most responsible for a given prediction. ImageNet large scale visual recognition challenge. values s_test and grad_z for each training image are computed on the fly Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. Striving for simplicity: The all convolutional net. Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Influence Functions %PDF-1.5 [ICML] Understanding Black-box Predictions via Influence Functions . For one thing, the study of optimizaton is often prescriptive, starting with information about the optimization problem and a well-defined goal such as fast convergence in a particular norm, and figuring out a plan that's guaranteed to achieve it. Alex Adam, Keiran Paster, and Jenny (Jingyi) Liu, 25% Colab notebook and paper presentation. insignificant. In, Cadamuro, G., Gilad-Bachrach, R., and Zhu, X. Debugging machine learning models. Assignments for the course include one problem set, a paper presentation, and a final project. Frenay, B. and Verleysen, M. Classification in the presence of label noise: a survey. S. McCandish, J. Kaplan, D. Amodei, and the OpenAI Dota Team. Interacting with predictions: Visual inspection of black-box machine learning models.
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