Journal of Machine Learning Research.js

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	"label": "Journal of Machine Learning Research",
	"creator": "Abe Jellinek",
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/*
	***** BEGIN LICENSE BLOCK *****

	Copyright © 2021 Abe Jellinek
	
	This file is part of Zotero.

	Zotero is free software: you can redistribute it and/or modify
	it under the terms of the GNU Affero General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.

	Zotero is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	GNU Affero General Public License for more details.

	You should have received a copy of the GNU Affero General Public License
	along with Zotero. If not, see <http://www.gnu.org/licenses/>.

	***** END LICENSE BLOCK *****
*/


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				"title": "Data-dependent margin-based generalization bounds for classification",
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						"lastName": "Antos",
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						"lastName": "Kégl",
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					{
						"firstName": "Gábor",
						"lastName": "Lugosi",
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				"date": "2002",
				"ISSN": "ISSN 1533-7928",
				"abstractNote": "We derive new margin-based inequalities for the probability of error of classifiers. The main feature of these bounds is that they can be calculated using the training data and therefore may be effectively used for model selection purposes. In particular, the bounds involve empirical complexities measured on the training data (such as the empirical fat-shattering dimension) as opposed to their worst-case counterparts traditionally used in such analyses. Also, our bounds appear to be sharper and more general than recent results involving empirical complexity measures. In addition, we develop an alternative data-based bound for the generalization error of classes of convex combinations of classifiers involving an empirical complexity measure that is easier to compute than the empirical covering number or fat-shattering dimension. We also show examples of efficient computation of the new bounds.",
				"issue": "Jul",
				"libraryCatalog": "www.jmlr.org",
				"pages": "73-98",
				"publicationTitle": "Journal of Machine Learning Research",
				"url": "https://www.jmlr.org/papers/v3/antos02a.html",
				"volume": "3",
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		"url": "https://www.jmlr.org/papers/v22/20-1380.html",
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				"title": "River: machine learning for streaming data in Python",
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						"lastName": "Montiel",
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					{
						"firstName": "Max",
						"lastName": "Halford",
						"creatorType": "author"
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					{
						"firstName": "Saulo Martiello",
						"lastName": "Mastelini",
						"creatorType": "author"
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					{
						"firstName": "Geoffrey",
						"lastName": "Bolmier",
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						"lastName": "Sourty",
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						"lastName": "Zouitine",
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						"firstName": "Heitor Murilo",
						"lastName": "Gomes",
						"creatorType": "author"
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						"firstName": "Jesse",
						"lastName": "Read",
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						"lastName": "Abdessalem",
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						"firstName": "Albert",
						"lastName": "Bifet",
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				"date": "2021",
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				"abstractNote": "River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.",
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				"publicationTitle": "Journal of Machine Learning Research",
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				"title": "Problems using deep generative models for probabilistic audio source separation",
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						"firstName": "Maurice",
						"lastName": "Frank",
						"creatorType": "author"
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						"firstName": "Maximilian",
						"lastName": "Ilse",
						"creatorType": "author"
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				],
				"date": "2020-02-08",
				"abstractNote": "Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference. However, we find that distributions learned by deep generative models for audio signals do not exhibit the right properties that are necessary for tasks like audio source separation using a probabilistic approach. We observe that the learned prior distributions are either discriminative and extremely peaked or smooth and non-discriminative. We quantify this behavior for two types of deep generative models on two audio datasets.",
				"language": "en",
				"libraryCatalog": "proceedings.mlr.press",
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		"type": "web",
		"url": "http://proceedings.mlr.press/v131/kommrusch20a.html",
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				"title": "Self-Supervised Learning for Multi-Goal Grid World: Comparing Leela and Deep Q Network",
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						"lastName": "Kommrusch",
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				"date": "2020-09-01",
				"abstractNote": "Modern machine learning research has explored numerous approaches to solving reinforce- ment learning with multiple goals and sparse rewards as well as learning correct actions from a small number of exploratory samples. We explore the ability of a self-supervised system which automatically creates and tests symbolic hypotheses about the world to ad- dress these same issues. Leela is a system which builds an understanding of the world using constructivist artificial intelligence. For our study, we create an N ∗ N grid world with goals related to proprioceptive or visual positions for exploration. We compare Leela to a DQN which includes hindsight for improving multigoal learning with sparse rewards. Our results show that Leela is able to learn to solve multigoal problems in an N ∗ N world with approximately 160N2 exploratory steps compared to 360N2.7 steps required by the DQN.",
				"conferenceName": "International Workshop on Self-Supervised Learning",
				"language": "en",
				"libraryCatalog": "proceedings.mlr.press",
				"pages": "72-88",
				"proceedingsTitle": "Proceedings of the First International Workshop on Self-Supervised Learning",
				"publisher": "PMLR",
				"shortTitle": "Self-Supervised Learning for Multi-Goal Grid World",
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		"type": "web",
		"url": "http://proceedings.mlr.press/v130/",
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		"type": "web",
		"url": "https://proceedings.mlr.press/v130/lyle21a.html",
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			{
				"itemType": "conferencePaper",
				"title": "On the Effect of Auxiliary Tasks on Representation Dynamics",
				"creators": [
					{
						"firstName": "Clare",
						"lastName": "Lyle",
						"creatorType": "author"
					},
					{
						"firstName": "Mark",
						"lastName": "Rowland",
						"creatorType": "author"
					},
					{
						"firstName": "Georg",
						"lastName": "Ostrovski",
						"creatorType": "author"
					},
					{
						"firstName": "Will",
						"lastName": "Dabney",
						"creatorType": "author"
					}
				],
				"date": "2021-03-18",
				"abstractNote": "While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achieved. This work develops our understanding of the relationship between auxiliary tasks, environment structure, and representations by analysing the dynamics of temporal difference algorithms. Through this approach, we establish a connection between the spectral decomposition of the transition operator and the representations induced by a variety of auxiliary tasks. We then leverage insights from these theoretical results to inform the selection of auxiliary tasks for deep reinforcement learning agents in sparse-reward environments.",
				"conferenceName": "International Conference on Artificial Intelligence and Statistics",
				"language": "en",
				"libraryCatalog": "proceedings.mlr.press",
				"pages": "1-9",
				"proceedingsTitle": "Proceedings of The 24th International Conference on Artificial Intelligence and Statistics",
				"publisher": "PMLR",
				"url": "https://proceedings.mlr.press/v130/lyle21a.html",
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/** END TEST CASES **/