neurips 2019 best paper

The above plots are a comparison of reproducibility in papers for the year 2019 at NeurIPS. We develop a new online algorithm, the regularized dual averaging method, that can explicitly exploit the regularization structure in an online setting. Every year, NeurIPS announces a category of awards for the top research papers in machine learning. NeurIPS 2019 is underway in Vancouver, and the committee has only in the near past introduced this 12 months’s Outstanding Paper Awards. Best Paper Awards in Computer Science (since 1996) By Conference: AAAI ACL CHI CIKM CVPR FOCS FSE ICCV ICML ICSE IJCAI INFOCOM KDD MOBICOM NEURIPS NSDI OSDI PLDI PODS S&P SIGCOMM SIGIR SIGMETRICS SIGMOD SODA SOSP STOC UIST VLDB WWW Institutions with the most Best Papers. The Excellent Paper Committee choice standards, immediately from NeurIPS: Much of this data was entered by hand (obtained by contacting past conference … See our blog post for more information. This also shows that these overparameterized models excessively depend on the parameter count and don’t account for variable batch sizes. Specifically, in RDA, instead of the current subgradient, the average subgradient is taken into account. But don’t worry! Phase 2 (Regular Submission): If you already acquired the NeurIPS ticket, please … The paper is a great leap forward toward achieving an excess risk of only epsilon. We can see that approximately 75% of accepted papers at NeurIPS 2019 included code, compared with 50% the previous year. June 12, 2020 -- NeurIPS 2020 will be held entirely online. The probability of flipping is bounded by some factor n which is always lesser than 1/2. As noted by reviewers, such self-organization in perceptual networks might give food for thought at the cross-road of algorithmic perspectives (sidestepping end-to-end optimization, its huge memory footprint and computational issues), and cognitive perspectives (exploiting the notion of so-called slow features and going toward more “biologically plausible” learning processes). This method achieves the optimal convergence rate and often enjoys a low complexity per iteration similar as the standard stochastic gradient method. Basically. This paper proposed a new regularizing technique, called the Regularised Dual Averaging Method (RDA) for solving online convex optimization problems. ... NeurIPS 2019 Poster. In a nutshell, this paper attacks one of the most influential machine learning problems – the problem of learning an unknown halfspace. Before looking at the papers, they agreed on the following criteria to guide their selection. Specifically, we are given a set of labeled examples (x,y) drawn from a distribution D on Rd+1 such that the marginal dis… As in previous years we created a committee to select a paper published 10 years ago at NeurIPS and that was deemed to have had a particularly significant and lasting impact on our community. Deploying Trained Models to Production with TensorFlow Serving, A Friendly Introduction to Graph Neural Networks. Distribution-Independent PAC Learning of Halfspaces with Massart Noise (arXiv) Authors: Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos Institutions: University of Southern California, Max Planck Institute for Informatics, University of Wisconsin-Madison Abstract: We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. One of my favorite papers this year! NeurIPS 2019 (also known as the Neural Information Processing Systems Conference), is one of the premier machine learning conferences—one of the “Big Three” that also includes ICML and ICLR.The latest event, which took place in Vancouver, featured more than 13,000 attendees, with 1,428 submissions having been accepted for presentation. NeurIPS is THE premier machine learning conference in the world. The research in this paper talks about an algorithm for learning halfspaces in the distribution-independent PAC model with Massart Noise. Which machine learning research paper caught your eye? ML and NLP enthusiast. All of the talks, including the spotlights and showcases, were broadcast live by the NeurIPS team. They go on to argue that they can’t do what they claim when they continue to lean on the machinery of two-sided uniform convergence. But these networks should not work as well as they do when the number of features is more than the number of training samples, right? NeurIPS 2019 was the 33rd edition of the conference, held between 8th and 14th December in Vancouver, Canada. This paper shows how to efficiently achieve excess risk equal to the Massart noise level plus epsilon (and runs in time poly(1/epsilon), as desired). var disqus_shortname = 'kdnuggets'; NeurIPS 2019 was an extremely educational and inspiring conference again. Each year, NeurIPS also gives an award to a paper presented at the conference 10 years ago and that has had a lasting impact on the field in its contributions (and is also a widely popular paper). There will be three tracks: 1) Best perf… In fact, with an increase in sparsity, the RDA method had demonstrably better results as well. (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); })(); By subscribing you accept KDnuggets Privacy Policy, Distribution-Independent PAC Learning of Halfspaces with Massart Noise, Uniform convergence may be unable to explain generalization in deep learning, Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses, Fast and Accurate Least-Mean-Squares Solvers, Putting An End to End-to-End: Gradient-Isolated Learning of Representations, Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations, Dual Averaging Method for Regularized Stochastic Learning and Online Optimization, AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020, 12 NLP Researchers, Practitioners & Innovators You Should Be Following, Why the Future of ETL Is Not ELT, But EL(T), Pruning Machine Learning Models in TensorFlow. How to Know if a Neural Network is Right for Your Machine Lear... Get KDnuggets, a leading newsletter on AI, If we go by the basic equation for generalization: Test Error – Training Error <= Generalisation bound. Search. All you have to do is replace the author, title, abstract, and text of the paper with your own. NeurIPS 2019 Outstanding New Directions Paper Award: Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan, J. Zico Kolter ----- … While the paper does not solve (nor pretend to solve) the question of generalisation in deep neural nets, it is an ``instance of the fingerpost’’ (to use Francis Bacon’s phrase) pointing the community to look in a different place. For instance, here is a comparison of the submitted and accepted papers for the past six NeurIPS conferences, dating back to 2014. There were 173 papers submitted as part of the challenge, a 92% increase over the number submitted for a similar challenge at ICLR 2019. We can clearly see why this machine learning research paper has received an award for Outstanding New Directions Paper at NeurIPS 2019. It also required a great deal of study on the paper itself and I will try to explain the gist of the paper without making it complex. They also agreed on some criteria that they would like to avoid: Finally, they determined it appropriate to introduce an additional Outstanding New Directions Paper Award, to highlight work that distinguished itself in setting a novel avenue for future research. My aim is to help you understand the essence of each paper by breaking down the key machine learning concepts into easy-to-understand bits for our community. No other research conference attracts a crowd of 6000+ people in one place – it is truly elite in its scope. It is reported that this year, […] This algorithm is the most efficient one yet in this space. We will notify accepted authors by October 1st and provide tickets to the author presenting the paper. ... and further references cited in this paper). These 7 Signs Show you have Data Scientist Potential! The paper studies the learning of linear threshold functions for binary classification in the presence of unknown, bounded label noise in the training data. (and their Resources), 40 Questions to test a Data Scientist on Clustering Techniques (Skill test Solution), 45 Questions to test a data scientist on basics of Deep Learning (along with solution), Commonly used Machine Learning Algorithms (with Python and R Codes), 40 Questions to test a data scientist on Machine Learning [Solution: SkillPower – Machine Learning, DataFest 2017], Introductory guide on Linear Programming for (aspiring) data scientists, 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R, 30 Questions to test a data scientist on K-Nearest Neighbors (kNN) Algorithm, 16 Key Questions You Should Answer Before Transitioning into Data Science. This is where they go against the idea of uniform convergence. This research proposed a novel method of Batch Optimization. We also encourage workshop participants to apply for NeurIPS 2019 travel grants and other grants (e.g. The novelty lies in the divide-and-conquer algorithm proposed to extract a coreset with affordable complexity (O(nd + d5 log n), granted that d << n). If you're looking to geek out a bit more on NeurIPS paper selection (and really, who isn't? Trainee Data Scientist at Analytics Vidhya. In this research on while the sample complexity was well established, a polynomial-time of (1/epsilon) was proved with an excess risk equal to the Massart noise level plus epsilon. A halfspace is a boolean function where the 2 classes (positive samples and negative samples) are separated by a hyperplane. The researchers have shown that merely uniform convergence is not enough to explain generalization in deep learning. This is used again in the iteration (at time t+1). Uniform convergence may be unable to explain generalization in deep learning, by Vaishnavh Nagarajan, J. Zico Kolter. What’s the point of the research if it isn’t reproducible? 前段时间跟同学们研讨了NeurIPS 2019 Best Paper,感受颇深,感受到了真正的大牛思考的维度和新颖程度,但是大家也对这篇文章提出了一些自己的看法,所以把一些核心点整理了出来。研讨的Slides可以见 … In particular, at each iteration, the learning variables are adjusted by solving a simple optimization problem that involves the running average of all past subgradients of the loss functions and the whole regularization term, not just its subgradient. However, despite the generalization, they prove that the decision boundary is quite complex. The 4th Conference on Robot Learning (CoRL) has announced the finalists for its Best Paper and Best System Paper awards. Let’s rephrase the title first. Fast and Accurate Least-Mean-Squares Solvers, by Alaa Maalouf, Ibrahim Jubran, Dan Feldman. They made their recommendations as follows. Data Science, and Machine Learning. NeurIPS is THE premier machine learning conference in the world. As one of the top events in the field of artificial intelligence and machine learning, it attracts a large number of experts, scholars and AI practitioners every year. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Advances in Neural Information Processing Systems 30 (NIPS 2017)

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