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HCI Supporting Food and Wine Futures: Fostering Community Resilience through Reflective practice 23

HCI Supporting Food and Wine Futures: Fostering Community Re...

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Proceedings of the 35th Australian computer-Human Interaction conference

作者： Jeni Paay Hilary Davis Axel Munoz Rivas Yvonne Rogers Margot Brereton Centre for Design Innovation Swinburne University of Technology Australia Centre for Social Impact Swinburne University of Technology Australia UCLIC University College London United Kingdom School of Computer Science Queensland University of Technology Australia

ISBN: (纸本)9798400717079

In this full-day workshop we will identify opportunities for HCI to support the Australian and New Zealand food and wine industries to become more future ready while recognising and preserving cultural heritage. We will do this through examining practices and interactions that positively impact on community resilience. We explore past and current ways of making, producing and consuming food and wine, including Indigenous practices. Our experiential workshop will take place in transit (on the bus), on excursions (to townships in the Wairarapa region of Wellington) and in productive spaces such as vineyards. We will hear from industry experts in growing, producing and harvesting of honey, wine and other food produce. We will critically reflect on how technology can be deployed to support sustainable and resilient practices within these industries and communities in the face of climate change and other challenges.

关键词： Community Resilience Food Interaction Design Sustainability User Experience Wine

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Better algorithms for individually fair k-clustering 21

Better algorithms for individually fair k-clustering

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Deeparnab Chakrabarty Maryam Negahbani Department of Computer Science Dartmouth College Hanover NH

ISBN: (纸本)9781713845393

We study data clustering problems with ℓp-norm objectives (e.g. k-MEDIAN and k-MEANS) in the context of individual fairness. the dataset consists of n points, and we want to find k centers such that (a) the objective is minimized, while (b) respecting the individual fairness constraint that every point v has a center within a distance at most r(v), where r(v) is v's distance to its (n/k)th nearest point. Jung, Kannan, and Lutz [FORC 2020] introduced this concept and designed a clustering algorithm with provable (approximate) fairness and objective guarantees for the ℓ∞ or k-C ENTER objective. Mahabadi and Vakilian [ICML 2020] revisited this problem to give a local-search algorithm for all ∞p-norms. Empirically, their algorithms outperform Jung et. al.'s by a large margin in terms of cost (for k-MEDIAN and k-MEANS), but they incur a reasonable loss in fairness. In this paper, our main contribution is to use Linear Programming (LP) techniques to obtain better algorithms for this problem, both in theory and in practice. We prove that by modifying known LP rounding techniques, one gets a worst-case guarantee on the objective which is much better than in MV20, and empirically, this objective is extremely close to the optimal. Furthermore, our theoretical fairness guarantees are comparable with MV20 in theory, and empirically, we obtain noticeably fairer solutions. Although solving the LP exactly might be prohibitive, we demonstrate that in practice, a simple sparsification technique drastically improves the run-time of our algorithm.

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Crowdsourcing Contest Dilemma Game with Unreliable channels

Crowdsourcing Contest Dilemma Game with Unreliable channels

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第35届中国控制会议

作者： Qing Dai Jianfeng Lu Changbing Tang DewuXu Hao Peng Department of Computer Science and Engineering Zhejiang Normal University

ISBN: (纸本)9781509009107

the Internet has caused the skills of world wide-scale collective problem solving,which is known as crowdsourcing that connects the individuals around the world to cooperate and solve the multifarious complex ***,due to the openness prominent features of crowdsourcing,it conceives some antisocial behaviors,such as sabotaging,attacking,plagiarizing,and manipulating the solution obtained by collective *** contest dilemma game was recently proposed,which shows that the malicious behavior is the norm in crowdsourcing *** this paper,we extend the two-stage game by introducing the unreliable channels to analyze the process of equilibrium based on game *** the current framework structure,we first analyze the situation in the second stage(to attack or not),and then take a step back and discuss the players' decisions(crowdsource or in-house) in the first *** find that the unreliable channels would affect a less desirable total profit to some extent,what's more,there are still crowdsourcing contest dilemmas in the crowdsourcing competitions under the unreliable channels.

关键词： Crowdsourcing antisocial behavior unreliable channels equilibrium game theory.

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Two sides of meta-learning evaluation: in vs. out of distribution 21

Two sides of meta-learning evaluation: in vs. out of distrib...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Amrith Setlur Oscar Li Virginia Smith Language Technologies Institute School of Computer Science Carnegie Mellon University Machine Learning Department School of Computer Science Carnegie Mellon University

ISBN: (纸本)9781713845393

We categorize meta-learning evaluation into two settings: in-distribution [ID], in which the train and test tasks are sampled iid from the same underlying task distribution, and out-of-distribution [OOD], in which they are not. While most meta-learning theory and some FSL applications follow the ID setting, we identify that most existing few-shot classification benchmarks instead refect OOD evaluation, as they use disjoint sets of train (base) and test (novel) classes for task generation. this discrepancy is problematic because—as we show on numerous benchmarks— meta-learning methods that perform better on existing OOD datasets may perform significantly worse in the ID setting. In addition, in the OOD setting, even though current FSL benchmarks seem befitting, our study highlights concerns in 1) reliably performing model selection for a given meta-learning method, and 2) consistently comparing the performance of different methods. To address these concerns, we provide suggestions on how to construct FSL benchmarks to allow for ID evaluation as well as more reliable OOD evaluation. Our work† aims to inform the meta-learning community about the importance and distinction of ID vs. OOD evaluation, as well as the subtleties of OOD evaluation with current benchmarks.

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Statistical inference with M-estimators on adaptively collected data 21

Statistical inference with M-estimators on adaptively collec...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Kelly W. Zhang Lucas Janson Susan A. Murphy Department of Computer Science Harvard University Departments of Statistics Harvard University Departments of Statistics and Computer Science Harvard University

ISBN: (纸本)9781713845393

Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use the resulting datasets to answer scientific questions like: Did one type of ad lead to more purchases? In which contexts is a mobile health intervention effective? However, classical statistical approaches fail to provide valid confidence intervals when used with data collected with bandit algorithms. Alternative methods have recently been developed for simple models (e.g., comparison of means). Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward. In this work, we develop theory justifying the use of M-estimators—which includes estimators based on empirical risk minimization as well as maximum likelihood—on data collected with adaptive algorithms, including (contextual) bandit algorithms. Specifically, we show that M-estimators, modified with particular adaptive weights, can be used to construct asymptotically valid confidence regions for a variety of inferential targets.

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Proxy convexity: a unified framework for the analysis of neural networks trained by gradient descent 21

Proxy convexity: a unified framework for the analysis of neu...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Spencer Frei Quanquan Gu Simons Institute for the Theory of Computing University of California Berkeley Department of Computer Science University of California Los Angeles

ISBN: (纸本)9781713845393

Although the optimization objectives for learning neural networks are highly non-convex, gradient-based methods have been wildly successful at learning neural networks in practice. this juxtaposition has led to a number of recent studies on provable guarantees for neural networks trained by gradient descent. Unfortunately, the techniques in these works are often highly specific to the particular setup in each problem, making it difficult to generalize across different settings. To address this drawback in the literature, we propose a unified non-convex optimization framework for the analysis of neural network training. We introduce the notions of proxy convexity and proxy Polyak-Lojasiewicz (PL) inequalities, which are satisfied if the original objective function induces a proxy objective function that is implicitly minimized when using gradient methods. We show that stochastic gradient descent (SGD) on objectives satisfying proxy convexity or the proxy PL inequality leads to efficient guarantees for proxy objective functions. We further show that many existing guarantees for neural networks trained by gradient descent can be unified through proxy convexity and proxy PL inequalities.

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Mitigating covariate shift in imitation learning via offline data without great coverage 21

Mitigating covariate shift in imitation learning via offline...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Jonathan D. Chang Masatoshi Uehara Dhruv Sreenivas Rahul Kidambi Wen Sun Department of Computer Science Cornell University Amazon Search & AI

ISBN: (纸本)9781713845393

this paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert demonstrator without additional online environment interactions. Instead, the learner is presented with a static offline dataset of state-action-next state transition triples from a potentially less proficient behavior policy. We introduce Model-based IL from Offline data (MILO): an algorithmic framework that utilizes the static dataset to solve the offline IL problem efficiently both in theory and in practice. In theory, even if the behavior policy is highly sub-optimal compared to the expert, we show that as long as the data from the behavior policy provides sufficient coverage on the expert state-action traces (and with no necessity for a global coverage over the entire state-action space), MILO can provably combat the covariate shift issue in IL. Complementing our theory results, we also demonstrate that a practical implementation of our approach mitigates covariate shift on benchmark MuJoCo continuous control tasks. We demonstrate that with behavior policies whose performances are less than half of that of the expert, MILO still successfully imitates with an extremely low number of expert state-action pairs while traditional offline IL methods such as behavior cloning (BC) fail completely.

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Rough Sets and Intelligent Systems Paradigms: Second International conference, RSEISP 2014, Held as Part of JRS 2014, Granada and Madrid, Spain, July 9-13, 2014. Proceedings 1

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丛书名： Lecture Notes in computer science 8537 Lecture Notes in Artificial Intelligence

2014年

作者： Marzena Kryszkiewicz, Chris Cornelis, Davide Ciucci, Jesús Medina-Moreno, Hiroshi Motoda, Zbigniew W. Raś (eds.)

ISBN: (数字)9783319087290

ISBN: (纸本)9783319087283;9783319087290

this book constitutes the refereed proceedings of the 23rd Australasian Joint conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International conference on Rough Sets and current trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short papers which were carefully reviewed and selected from 120 submissions and presented in two volumes. this volume contains the papers accepted for the conference RSEISP 2014, as well as the three invited papers presented at the conference. the papers are organized in topical sections on plenary lecture and tutorial papers; foundations of rough set theory; granular computing and covering-based rough sets; applications of rough sets; induction of decision rules - theory and practice; knowledge discovery; spatial data analysis and spatial databases; information extraction from images.

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Fast projection onto the capped simplex with applications to sparse regression in bioinformatics 21

Fast projection onto the capped simplex with applications to...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Andersen Ang Jianzhu Ma Nianjun Liu Kun Huang Yijie Wang Dept. of Combinatorics and Optimization University of Waterloo Institute for Artificial Intelligence Peking University Dept. of Epidemiology and Biostatistics Indiana University Bloomington Dept. of Biostatistics and Health Data Science Indiana University Dept. of Computer Science Indiana University Bloomington

ISBN: (纸本)9781713845393

We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane. For an n-dimensional input vector with bounded elements, we found that a simple algorithm based on Newton's method is able to solve the projection problem to high precision with a complexity roughly about O(n), which has a much lower computational cost compared with the existing sorting-based methods proposed in the literature. We provide a theory for partial explanation and justification of the *** demonstrate that the proposed algorithm can produce a solution of the projection problem with high precision on large scale datasets, and the algorithm is able to significantly outperform the state-of-the-art methods in terms of runtime (about 6-8 times faster than a commercial software with respect to CPU time for input vector with 1 million variables or more).We further illustrate the effectiveness of the proposed algorithm on solving sparse regression in a bioinformatics problem. Empirical results on the GWAS dataset (with 1,500,000 single-nucleotide polymorphisms) show that, when using the proposed method to accelerate the Projected Quasi-Newton (PQN) method, the accelerated PQN algorithm is able to handle huge-scale regression problem and it is more efficient (about 3-6 times faster) than the current state-of-the-art methods.

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Labeling trick: a theory of using graph neural networks for multi-node representation learning 21

Labeling trick: a theory of using graph neural networks for ...

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Proceedings of the 35th International conference on Neural Information Processing Systems

作者： Muhan Zhang Pan Li Yinglong Xia Kai Wang Long Jin Institute for Artificial Intelligence Peking University and Beijing Institute for General Artificial Intelligence Department of Computer Science Purdue University Facebook AI

ISBN: (纸本)9781713845393

In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN into a joint node set representation. In this paper, we show a fundamental constraint of such an approach, namely the inability to capture the dependence between nodes in the node set, and argue that directly aggregating individual node representations does not lead to an effective joint representation for multiple nodes. then, we notice that a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, and ID-GNN, all used node labeling. these methods first label nodes in the graph according to their relationships with the target node set before applying a GNN. then, the node representations obtained in the labeled graph are aggregated into a node set representation. By investigating their inner mechanisms, we unify these node labeling techniques into a single and most general form—labeling trick. We prove that with labeling trick a sufficiently expressive GNN learns the most expressive node set representations, thus in principle solves any joint learning tasks over node sets. Experiments on one important two-node representation learning task, link prediction, verified our theory. Our work explains the superior performance of previous node-labeling-based methods, and establishes a theoretical foundation of using GNNs for multi-node representation learning.

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