Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Muller. Understanding black-box predictions via influence functions. We use cookies to ensure that we give you the best experience on our website. So far, we've assumed gradient descent optimization, but we can get faster convergence by considering more general dynamics, in particular momentum. on the final predictions is straight forward. 2172: 2017: . How can we explain the predictions of a black-box model? On linear models and convolutional neural networks, ( , ?) I am grateful to my supervisor Tasnim Azad Abir sir, for his . 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. Check out CSC2541 for the Busy. On the origin of implicit regularization in stochastic gradient descent. We have 3 hours scheduled for lecture and/or tutorial. Data poisoning attacks on factorization-based collaborative filtering. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality Influence functions can of course also be used for data other than images, Rather, the aim is to give you the conceptual tools you need to reason through the factors affecting training in any particular instance. The datasets for the experiments can also be found at the Codalab link. Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. The most barebones way of getting the code to run is like this: Here, config contains default values for the influence function calculation In, Martens, J. Negative momentum for improved game dynamics. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. This leads to an important optimization tool called the natural gradient. Wojnowicz, M., Cruz, B., Zhao, X., Wallace, B., Wolff, M., Luan, J., and Crable, C. "Influence sketching": Finding influential samples in large-scale regressions. Requirements Installation Usage Background and Documentation config Misc parameters For this class, we'll use Python and the JAX deep learning framework. Understanding Black-box Predictions via Influence Functions 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. influences. Understanding Black-box Predictions via Influence Functions [ICML] Understanding Black-box Predictions via Influence Functions Pang Wei Koh and Percy Liang. Theano D. Team. Goodfellow, I. J., Shlens, J., and Szegedy, C. Explaining and harnessing adversarial examples. Gradient-based hyperparameter optimization through reversible learning. Understanding Black-box Predictions via Influence Functions - PMLR Visualised, the output can look like this: The test image on the top left is test image for which the influences were 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. Gradient-based Hyperparameter Optimization through Reversible Learning. A classic result tells us that the influence of upweighting z on the parameters ^ is given by. 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. Koh P, Liang P, 2017. Rethinking the Inception architecture for computer vision. ( , ) Inception, . Thus, you can easily find mislabeled images in your dataset, or For modern neural nets, the analysis is more often descriptive: taking the procedures practitioners are already using, and figuring out why they (seem to) work. This class is about developing the conceptual tools to understand what happens when a neural net trains. Insights from a noisy quadratic model. 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 models prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. understanding model behavior, debugging models, detecting dataset errors, We try to understand the effects they have on the dynamics and identify some gotchas in building deep learning systems. In. Your search export query has expired. Haoping Xu, Zhihuan Yu, and Jingcheng Niu. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Training test 7, Training 1, test 7 . The second mode is called calc_all_grad_then_test and thereby identifying training points most responsible for a given prediction. Tasha Nagamine, . This isn't the sort of applied class that will give you a recipe for achieving state-of-the-art performance on ImageNet. Deep learning via Hessian-free optimization. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. Decaf: A deep convolutional activation feature for generic visual recognition. With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chris Zhang, Dami Choi, Anqi (Joyce) Yang. In, Mei, S. and Zhu, X. << The infinitesimal jackknife. Understanding Black-box Predictions via Influence Functions (2017) numbers above the images show the actual influence value which was calculated. In Proceedings of the international conference on machine learning (ICML). We show that even on non-convex and non-differentiable models In. Understanding black-box predictions via influence functions. Li, J., Monroe, W., and Jurafsky, D. Understanding neural networks through representation erasure. If the influence function is calculated for multiple Explain and Predict, and then Predict Again | Proceedings of the 14th ordered by harmfulness. Linearization is one of our most important tools for understanding nonlinear systems. We would like to show you a description here but the site won't allow us. where the theory breaks down, ImageNet large scale visual recognition challenge. insignificant. Imagenet classification with deep convolutional neural networks. Components of inuence. Understanding Black-box Predictions via Influence Functions. Neither is it the sort of theory class where we prove theorems for the sake of proving theorems. The deep bootstrap framework: Good online learners are good offline generalizers. test images, the helpfulness is ordered by average helpfulness to the . Why neural nets generalize despite their enormous capacity is intimiately tied to the dynamics of training. ICML 2017 Best Paper - 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 Unofficial implementation of the paper "Understanding Black-box Preditions via Influence Functions", which got ICML best paper award, in Chainer. approximations to influence functions can still provide valuable information. Understanding Black-box Predictions via Influence Functions 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. We are given training points z 1;:::;z n, where z i= (x i;y i) 2 XY . Alex Adam, Keiran Paster, and Jenny (Jingyi) Liu, 25% Colab notebook and paper presentation. The power of interpolation: Understanding the effectiveness of SGD in modern over-parameterized learning. No description, website, or topics provided. Applications - Understanding model behavior Inuence functions reveal insights about how models rely on and extrapolate from the training data. On Second-Order Group Influence Functions for Black-Box Predictions In Artificial Intelligence and Statistics (AISTATS), pages 3382-3390, 2019. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., and Kripalani, S. Risk prediction models for hospital readmission: a systematic review. A. S. Benjamin, D. Rolnick, and K. P. Kording. Please try again. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through . Please download or close your previous search result export first before starting a new bulk export. While this class draws upon ideas from optimization, it's not an optimization class. Copyright 2023 ACM, Inc. Understanding black-box predictions via influence functions. 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. Therefore, if we bring in an idea from optimization, we need to think not just about whether it will minimize a cost function faster, but also whether it does it in a way that's conducive to generalization. In, Cadamuro, G., Gilad-Bachrach, R., and Zhu, X. Debugging machine learning models. Here, we used CIFAR-10 as dataset. Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. 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 . % Model selection in kernel based regression using the influence function. Idea: use Influence Functions to observe the influence of the test samples from the training samples. Another difference from the study of optimization is that the goal isn't simply to fit a finite training set, but rather to generalize. Delta-STN: Efficient bilevel optimization of neural networks using structured response Jacobians. Things get more complicated when there are multiple networks being trained simultaneously to different cost functions. Your file of search results citations is now ready. The reference implementation can be found here: link. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Understanding Black-box Predictions via Inuence Functions 2. test images, the harmfulness is ordered by average harmfullness to the Students are encouraged to attend synchronous lectures to ask questions, but may also attend office hours or use Piazza. S. McCandish, J. Kaplan, D. Amodei, and the OpenAI Dota Team. , loss , input space . Therefore, this course will finish with bilevel optimziation, drawing upon everything covered up to that point in the course. prediction outcome of the processed test samples. We'll also consider self-tuning networks, which try to solve bilevel optimization problems by training a network to locally approximate the best response function. GitHub - kohpangwei/influence-release Understanding Black-box Predictions via Influence Functions , . 2019. calculations, which could potentially be 10s of thousands. Understanding Black-box Predictions via Influence Functions (2017) 1. This site last compiled Wed, 08 Feb 2023 10:43:27 +0000. Pang Wei Koh, Percy Liang; Proceedings of the 34th International Conference on Machine Learning, . One would have expected this success to require overcoming significant obstacles that had been theorized to exist. All information about attending virtual lectures, tutorials, and office hours will be sent to enrolled students through Quercus. Overwhelmed? Interpreting black box predictions using Fisher kernels. Model-agnostic meta-learning for fast adaptation of deep networks. 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. How can we explain the predictions of a black-box model? Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition. Koh, Pang Wei. Understanding Black-box Predictions via Influence Functions - YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features 2022. Lage, E. Chen, J. Abstract. ordered by helpfulness. We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. In this paper, we use influence functions a classic technique from robust statistics to trace a models prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. We'll use the Hessian to diagnose slow convergence and interpret the dependence of a network's predictions on the training data. grad_z on the other hand is only dependent on the training Validations 4. SVM , . For toy functions and simple architectures (e.g. We'll use linear regression to understand two neural net training phenomena: why it's a good idea to normalize the inputs, and the double descent phenomenon whereby increasing dimensionality can reduce overfitting. We look at three algorithmic features which have become staples of neural net training. CSC2541 Winter 2021 - Department of Computer Science, University of Toronto A tag already exists with the provided branch name. C. Maddison, D. Paulin, Y.-W. Teh, B. O'Donoghue, and A. Doucet. The reference implementation can be found here: link. Gradient descent on neural networks typically occurs on the edge of stability. This is a tentative schedule, which will likely change as the course goes on. Aggregated momentum: Stability through passive damping. 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. samples for each test data sample. However, in a lower Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. The datasets for the experiments can also be found at the Codalab link. Biggio, B., Nelson, B., and Laskov, P. Support vector machines under adversarial label noise. 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. Approach Consider a prediction problem from some input space X (e.g., images) to an output space Y(e.g., labels). PDF Appendix: 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. 10.5 Influential Instances | Interpretable Machine Learning - GitHub Pages Natural gradient works efficiently in learning. Understanding Black-box Predictions via Influence Functions Neural tangent kernel: Convergence and generalization in neural networks. The more recent Neural Tangent Kernel gives an elegant way to understand gradient descent dynamics in function space. A. In. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. Reference Understanding Black-box Predictions via Influence Functions He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. Adaptive Gradient Methods, Normalization, and Weight Decay [Slides]. Chatterjee, S. and Hadi, A. S. Influential observations, high leverage points, and outliers in linear regression. You signed in with another tab or window. While one grad_z is used to estimate the PW Koh, P Liang. we demonstrate that influence functions are useful for multiple purposes: Cook, R. D. Detection of influential observation in linear regression. outcome. On the importance of initialization and momentum in deep learning. J. Cohen, S. Kaur, Y. Li, J. Apparently this worked. Fortunately, influence functions give us an efficient approximation. and Hessian-vector products. If Influence Functions are the Answer, Then What is the Question? Inception-V3 vs RBF SVM(use SmoothHinge) The inception networks(DNN) picked up on the distinctive characteristics of the fish. nimarb/pytorch_influence_functions - Github The idea is to compute the parameter change if z were upweighted by some small , giving us new parameters ^,z argmin(1 )1 nn i=1L(zi,)+L(z,). 7 1 . To manage your alert preferences, click on the button below. Understanding Black-box Predictions via Influence Functions Proceedings of the 34th International Conference on Machine Learning . While these topics had consumed much of the machine learning research community's attention when it came to simpler models, the attitude of the neural nets community was to train first and ask questions later. Z. Kolter, and A. Talwalkar. We look at what additional failures can arise in the multi-agent setting, such as rotation dynamics, and ways to deal with them. calculates the grad_z values for all images first and saves them to disk. Some JAX code examples for algorithms covered in this course will be available here. Theano: A Python framework for fast computation of mathematical expressions. to trace a model's prediction through the learning algorithm and back to its training data, This packages offers two modes of computation to calculate the influence (a) train loss, Hessian, train_loss + Hessian . Borys Bryndak, Sergio Casas, and Sean Segal. ; Liang, Percy. This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: I. Sutskever, J. Martens, G. Dahl, and G. Hinton. Understanding black-box predictions via influence functions. PVANet: Lightweight Deep Neural Networks for Real-time Object Detection. (a) What is the effect of the training loss and H 1 ^ terms in I up,loss? There are several neural net libraries built on top of JAX. Cook, R. D. Assessment of local influence. That can increase prediction accuracy, reduce RelEx: A Model-Agnostic Relational Model Explainer We are preparing your search results for download We will inform you here when the file is ready. Reconciling modern machine-learning practice and the classical bias-variance tradeoff. Uses cases Roadmap 2 Reviving an "old technique" from Robust statistics: Influence function Deep inside convolutional networks: Visualising image classification models and saliency maps. %PDF-1.5 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. Wei, B., Hu, Y., and Fung, W. Generalized leverage and its applications. A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach. : , , , . the original paper linked here. reading both values from disk and calculating the influence base on them. Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Inuence Functions Figure 1. Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks. One would have expected this success to require overcoming significant obstacles that had been theorized to exist. P. Nakkiran, B. Neyshabur, and H. Sedghi. For more details please see How can we explain the predictions of a black-box model? All Holdings within the ACM Digital Library. Ribeiro, M. T., Singh, S., and Guestrin, C. "why should I trust you? 2016. Fast exact multiplication by the hessian. For the final project, you will carry out a small research project relating to the course content. We'll mostly focus on minimax optimization, or zero-sum games. WhiteBox Part 2: Interpretable Machine Learning - TooTouch 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. 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 lecture, we consider the behavior of neural nets in the infinite width limit. International conference on machine learning, 1885-1894, 2017. 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. 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, Moosavi-Dezfooli, S., Fawzi, A., and Frossard, P. Deep-fool: a simple and accurate method to fool deep neural networks. Christmann, A. and Steinwart, I. 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. 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. While influence estimates align well with leave-one-out. To get the correct test outcome of ship, the Helpful images from 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. Goodman, B. and Flaxman, S. European union regulations on algorithmic decision-making and a "right to explanation". Thus, we can see that different models learn more from different images. Biggio, B., Nelson, B., and Laskov, P. Poisoning attacks against support vector machines. 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. stream Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. I recommend you to change the following parameters to your liking. We'll then consider how the gradient noise in SGD optimization can contribute an implicit regularization effect, Bayesian or non-Bayesian. Google Scholar Requirements chainer v3: It uses FunctionHook. To scale up influence functions to modern machine learning settings, (b) 7 , 7 . in terms of the dataset. Neural nets have achieved amazing results over the past decade in domains as broad as vision, speech, language understanding, medicine, robotics, and game playing. If there are n samples, it can be interpreted as 1/n. Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. The implicit and explicit regularization effects of dropout. In. Class will be held synchronously online every week, including lectures and occasionally tutorials. Understanding black-box predictions via influence functions Google Scholar Digital Library; Josua Krause, Adam Perer, and Kenney Ng. lehman2019inferringE. ICML 2017 best paperStanfordPang Wei KohCourseraStanfordNIPS 2019influence functionPercy Liang11Michael Jordan, , \hat{\theta}_{\epsilon, z} \stackrel{\text { def }}{=} \arg \min _{\theta \in \Theta} \frac{1}{n} \sum_{i=1}^{n} L\left(z_{i}, \theta\right)+\epsilon L(z, \theta), \left.\mathcal{I}_{\text {up, params }}(z) \stackrel{\text { def }}{=} \frac{d \hat{\theta}_{\epsilon, z}}{d \epsilon}\right|_{\epsilon=0}=-H_{\tilde{\theta}}^{-1} \nabla_{\theta} L(z, \hat{\theta}), , loss, \begin{aligned} \mathcal{I}_{\text {up, loss }}\left(z, z_{\text {test }}\right) &\left.\stackrel{\text { def }}{=} \frac{d L\left(z_{\text {test }}, \hat{\theta}_{\epsilon, z}\right)}{d \epsilon}\right|_{\epsilon=0} \\ &=\left.\nabla_{\theta} L\left(z_{\text {test }}, \hat{\theta}\right)^{\top} \frac{d \hat{\theta}_{\epsilon, z}}{d \epsilon}\right|_{\epsilon=0} \\ &=-\nabla_{\theta} L\left(z_{\text {test }}, \hat{\theta}\right)^{\top} H_{\hat{\theta}}^{-1} \nabla_{\theta} L(z, \hat{\theta}) \end{aligned}, \varepsilon=-1/n , z=(x,y) \\ z_{\delta} \stackrel{\text { def }}{=}(x+\delta, y), \hat{\theta}_{\epsilon, z_{\delta},-z} \stackrel{\text { def }}{=}\arg \min _{\theta \in \Theta} \frac{1}{n} \sum_{i=1}^{n} L\left(z_{i}, \theta\right)+\epsilon L\left(z_{\delta}, \theta\right)-\epsilon L(z, \theta), \begin{aligned}\left.\frac{d \hat{\theta}_{\epsilon, z_{\delta},-z}}{d \epsilon}\right|_{\epsilon=0} &=\mathcal{I}_{\text {up params }}\left(z_{\delta}\right)-\mathcal{I}_{\text {up, params }}(z) \\ &=-H_{\hat{\theta}}^{-1}\left(\nabla_{\theta} L(z_{\delta}, \hat{\theta})-\nabla_{\theta} L(z, \hat{\theta})\right) \end{aligned}, \varepsilon \delta \deltaloss, \left.\frac{d \hat{\theta}_{\epsilon, z_{\delta},-z}}{d \epsilon}\right|_{\epsilon=0} \approx-H_{\hat{\theta}}^{-1}\left[\nabla_{x} \nabla_{\theta} L(z, \hat{\theta})\right] \delta, \hat{\theta}_{z_{i},-z}-\hat{\theta} \approx-\frac{1}{n} H_{\hat{\theta}}^{-1}\left[\nabla_{x} \nabla_{\theta} L(z, \hat{\theta})\right] \delta, \begin{aligned} \mathcal{I}_{\text {pert,loss }}\left(z, z_{\text {test }}\right)^{\top} &\left.\stackrel{\text { def }}{=} \nabla_{\delta} L\left(z_{\text {test }}, \hat{\theta}_{z_{\delta},-z}\right)^{\top}\right|_{\delta=0} \\ &=-\nabla_{\theta} L\left(z_{\text {test }}, \hat{\theta}\right)^{\top} H_{\hat{\theta}}^{-1} \nabla_{x} \nabla_{\theta} L(z, \hat{\theta}) \end{aligned}, train lossH \mathcal{I}_{\text {up, loss }}\left(z, z_{\text {test }}\right) , -y_{\text {test }} y \cdot \sigma\left(-y_{\text {test }} \theta^{\top} x_{\text {test }}\right) \cdot \sigma\left(-y \theta^{\top} x\right) \cdot x_{\text {test }}^{\top} H_{\hat{\theta}}^{-1} x, influence functiondebug training datatraining point \mathcal{I}_{\text {up, loss }}\left(z, z_{\text {test }}\right) losstraining pointtraining point, Stochastic estimationHHHTFO(np)np, ImageNetdogfish900Inception v3SVM with RBF kernel, poisoning attackinfluence function59157%77%10590/591, attackRelated worktraining set attackadversarial example, influence functionbad case debug, labelinfluence function, \mathcal{I}_{\text {up,loss }}\left(z_{i}, z_{i}\right) , 10%labelinfluence functiontrain lossrandom, \mathcal{I}_{\text {up, loss }}\left(z, z_{\text {test }}\right), \mathcal{I}_{\text {up,loss }}\left(z_{i}, z_{i}\right), \mathcal{I}_{\text {pert,loss }}\left(z, z_{\text {test }}\right)^{\top}, H_{\hat{\theta}}^{-1} \nabla_{x} \nabla_{\theta} L(z, \hat{\theta}), Less Is Better: Unweighted Data Subsampling via Influence Function, influence functionleave-one-out retraining, 0.86H, SVMhinge loss0.95, straightforwardbest paper, influence functionloss.
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