Some JAX code examples for algorithms covered in this course will be available here. An empirical model of large-batch training. 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. Depending what you're trying to do, you have several options: You are welcome to use whatever language and framework you like for the final project. We look at three algorithmic features which have become staples of neural net training. I recommend you to change the following parameters to your liking. For this class, we'll use Python and the JAX deep learning framework. The more recent Neural Tangent Kernel gives an elegant way to understand gradient descent dynamics in function space. Github calculates the grad_z values for all images first and saves them to disk. Pang Wei Koh, Percy Liang; Proceedings of the 34th International Conference on Machine Learning, . 2019. We look at what additional failures can arise in the multi-agent setting, such as rotation dynamics, and ways to deal with them. Apparently this worked. which can of course be changed. 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*, KS Ang*, H Teo*, PS Liang. . Gradient-based hyperparameter optimization through reversible learning. Understanding Black-box Predictions via Influence Functions we demonstrate that influence functions are useful for multiple purposes: For these We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Wojnowicz, M., Cruz, B., Zhao, X., Wallace, B., Wolff, M., Luan, J., and Crable, C. "Influence sketching": Finding influential samples in large-scale regressions. Understanding Black-box Predictions via Influence Functions - ResearchGate International Conference on Machine Learning (ICML), 2017. Approach Consider a prediction problem from some input space X (e.g., images) to an output space Y(e.g., labels). . grad_z on the other hand is only dependent on the training the prediction outcomes of an entire dataset or even >1000 test samples. Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. Loss non-convex, quadratic loss . In, Martens, J. Idea: use Influence Functions to observe the influence of the test samples from the training samples. On Second-Order Group Influence Functions for Black-Box Predictions PDF Understanding Black-box Predictions via Influence Functions - arXiv We'll start off the class by analyzing a simple model for which the gradient descent dynamics can be determined exactly: linear regression. Model-agnostic meta-learning for fast adaptation of deep networks. Google Scholar The implicit and explicit regularization effects of dropout. Influence functions can of course also be used for data other than images, PDF Understanding Black-box Predictions via Influence Functions In. Understanding Black-box Predictions via Influence Functions Background information ICML 2017 best paper Stanford Pang Wei Koh CourseraStanfordNIPS 2019influence function Percy Liang11Michael Jordan Abstract With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. On the accuracy of influence functions for measuring group effects. Understanding Black-box Predictions via Influence Functions - PMLR 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. Reference Understanding Black-box Predictions via Influence Functions compress your dataset slightly to the most influential images important for The algorithm moves then We'll see first how Bayesian inference can be implemented explicitly with parameter noise. sample. 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. calculations even if we could reuse them for all subsequent s_test , . We are given training points z 1;:::;z n, where z i= (x i;y i) 2 XY . ( , ?) Pearlmutter, B. https://dl.acm.org/doi/10.5555/3305381.3305576. International conference on machine learning, 1885-1894, 2017. NIPS, p.1097-1105. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um UNDERSTANDING BLACK-BOX PRED -ICTION VIA INFLUENCE FUNCTIONS 1 The deep bootstrap framework: Good online learners are good offline generalizers. Using machine teaching to identify optimal training-set attacks on machine learners. For toy functions and simple architectures (e.g. On the Accuracy of Influence Functions for Measuring - ResearchGate 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. on the final predictions is straight forward. can speed up the calculation significantly as no duplicate calculations take 2016. On robustness properties of convex risk minimization methods for pattern recognition. Li, B., Wang, Y., Singh, A., and Vorobeychik, Y.

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