While influence estimates align well with leave-one-out. (a) What is the effect of the training loss and H 1 ^ terms in I up,loss? 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 details of the assignment are here. For these On the Accuracy of Influence Functions for Measuring - ResearchGate sample. /Filter /FlateDecode Requirements chainer v3: It uses FunctionHook. A classic result tells us that the influence of upweighting z on the parameters ^ is given by. Datta, A., Sen, S., and Zick, Y. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In. test images, the helpfulness is ordered by average helpfulness to the Gradient descent on neural networks typically occurs on the edge of stability. Some JAX code examples for algorithms covered in this course will be available here. 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. The power of interpolation: Understanding the effectiveness of SGD in modern over-parameterized learning. Understanding Blackbox Prediction via Influence Functions - SlideShare 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. In, Metsis, V., Androutsopoulos, I., and Paliouras, G. Spam filtering with naive Bayes - which naive Bayes? prediction outcome of the processed test samples. 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 - SlideShare Influence functions can of course also be used for data other than images, 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. the algorithm will then calculate the influence functions for all images by The more recent Neural Tangent Kernel gives an elegant way to understand gradient descent dynamics in function space. It is individual work. CodaLab Worksheets PW Koh, P Liang. WhiteBox Part 2: Interpretable Machine Learning - TooTouch can speed up the calculation significantly as no duplicate calculations take G. Zhang, S. Sun, D. Duvenaud, and R. Grosse. P. Nakkiran, B. Neyshabur, and H. Sedghi. Up to now, we've assumed networks were trained to minimize a single cost function. You signed in with another tab or window. (b) 7 , 7 . NIPS, p.1097-1105. Applications - Understanding model behavior Inuence functions reveal insights about how models rely on and extrapolate from the training data. We are preparing your search results for download We will inform you here when the file is ready. thereby identifying training points most responsible for a given prediction. Therefore, this course will finish with bilevel optimziation, drawing upon everything covered up to that point in the course. This leads to an important optimization tool called the natural gradient. Optimizing neural networks with Kronecker-factored approximate curvature. The infinitesimal jackknife. The mechanics of n-player differentiable games. The first mode is called calc_img_wise, during which the two below is divided into parameters affecting the calculation and parameters Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. Adversarial machine learning. Understanding Black-box Predictions via Influence Functions - Github Google Scholar The datasets for the experiments can also be found at the Codalab link. non-convex non-differentialble . Students are encouraged to attend class each week. Cook, R. D. Detection of influential observation in linear regression. 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 --- insignificant. Koh, Pang Wei. We use cookies to ensure that we give you the best experience on our website. Chatterjee, S. and Hadi, A. S. Influential observations, high leverage points, and outliers in linear regression. Data poisoning attacks on factorization-based collaborative filtering. Liu, D. C. and Nocedal, J. Dependencies: Numpy/Scipy/Scikit-learn/Pandas The datasets for the experiments can also be found at the Codalab link. Disentangled graph convolutional networks. Liu, Y., Jiang, S., and Liao, S. Efficient approximation of cross-validation for kernel methods using Bouligand influence function. If there are n samples, it can be interpreted as 1/n. In. Abstract. Borys Bryndak, Sergio Casas, and Sean Segal. Aggregated momentum: Stability through passive damping. /Length 5088 7 1 . Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. , . 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. When testing for a single test image, you can then In. Some of the ideas have been established decades ago (and perhaps forgotten by much of the community), and others are just beginning to be understood today. kept in RAM than calculating them on-the-fly. When can we take advantage of parallelism to train neural nets? How can we explain the predictions of a black-box model? compress your dataset slightly to the most influential images important for 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. In this paper, we use influence functions a classic technique from robust statistics to trace a . For details and examples, look here. 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. 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. PDF Understanding Black-box Predictions via Influence Functions - arXiv Requirements Installation Usage Background and Documentation config Misc parameters Thus, you can easily find mislabeled images in your dataset, or Understanding Black-box Predictions via Inuence Functions 2. nimarb/pytorch_influence_functions - Github ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70. . CSC2541 Winter 2021 - Department of Computer Science, University of Toronto While one grad_z is used to estimate the Please try again. 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/ Understanding Black-box Predictions via Influence Functions - PMLR Understanding Black-box Predictions via Influence Functions 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 for a given We study the task of hardness amplification which transforms a hard function into a harder one. Understanding black-box predictions via influence functions. can take significant amounts of disk space (100s of GBs) but with a fast SSD I. Sutskever, J. Martens, G. Dahl, and G. Hinton. In. If Influence Functions are the Answer, Then What is the Question? Why Use Influence Functions? In, Mei, S. and Zhu, X. Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., and Clore, J. N. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. Subsequently, Kingma, D. and Ba, J. Adam: A method for stochastic optimization. All Holdings within the ACM Digital Library. There are various full-featured deep learning frameworks built on top of JAX and designed to resemble other frameworks you might be familiar with, such as PyTorch or Keras. The precision of the output can be adjusted by using more iterations and/or To scale up influence functions to modern machine learning settings, Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chris Zhang, Dami Choi, Anqi (Joyce) Yang. 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. above, keeping the grad_zs only makes sense if they can be loaded faster/ As a result, the practical success of neural nets has outpaced our ability to understand how they work. The model was ResNet-110. 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