Approximating Pareto frontier using a hybrid line search approach

This is the post-print version of the final paper published in Information Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.CNCSIS IDEI 2412, Romani

Joint Embedding Predictive Architectures Focus on Slow Features

Many common methods for learning a world model for pixel-based environments use generative architectures trained with pixel-level reconstruction objectives. Recently proposed Joint Embedding Predictive Architectures (JEPA) offer a reconstruction-free alternative. In this work, we analyze performance of JEPA trained with VICReg and SimCLR objectives in the fully offline setting without access to rewards, and compare the results to the performance of the generative architecture. We test the methods in a simple environment with a moving dot with various background distractors, and probe learned representations for the dot's location. We find that JEPA methods perform on par or better than reconstruction when distractor noise changes every time step, but fail when the noise is fixed. Furthermore, we provide a theoretical explanation for the poor performance of JEPA-based methods with fixed noise, highlighting an important limitation.Comment: 4 pages (3 figures) short paper for SSL Theory and Practice workshop at NeurIPS 2022. Code is available at https://github.com/vladisai/JEPA_SSL_NeurIPS_202

Multi-Document Summarization with Centroid-Based Pretraining

In multi-document summarization (MDS), the input is a cluster of documents, and the output is the cluster summary. In this paper, we focus on pretraining objectives for MDS. Specifically, we introduce a simple pretraining objective of choosing the ROUGE-based centroid of each document cluster as a proxy for its summary. Our objective thus does not require human written summaries and can be used for pretraining on a dataset containing only clusters of documents. Through zero-shot and fully supervised experiments on multiple MDS datasets, we show that our model Centrum is better or comparable to a state-of-the-art model. We release our pretrained and finetuned models at https://github.com/ratishsp/centrum.Comment: 4 pages, work-in-progres

Adapting to climate change : Assessing the vulnerability of ecosystem services in Europe in the context of rural development

This document is the Accepted Manuscript version. The final publication is available at Springer via https://doi.org/10.1007/s11027-013-9507-6.Over the past decade, efforts to move towards a low carbon economy have been increasingly coupled with the acknowledgement that we also need to develop climate resilient economies, capable of adapting and responding to changes in climate. To shift society in these directions we need to quantify impacts in relation to these objectives and develop cost-effective interventions. Techniques for quantifying greenhouse gas emissions are relatively well established and enable identification of hotspots where there is emissions reduction potential. However, there are no established techniques to assess and quantify adaptation vulnerability issues and identify hotspots for intervention. This paper presents work undertaken at a European level with the objective of identifying potential hotspots where ecosystem services may be vulnerable to climate change and thus where intervention may be required under the European Rural Development Programme. A pragmatic and relatively simple approach is presented, based on data that is readily available across Europe. The vulnerability assessments cover: Water (quality: dilution and filtration, regulation: flooding and provision); soils (erosion and organic matter); and biodiversity (forest fires, migration and pollination). The framework and assessments presented are considered fit for purpose (at a basic level) and they are potentially valuable tools for targeting limited resources to achieve desirable outcomes. They also contribute towards providing a better understanding of the climate change challenges we face and support the formulation of solutions to optimally address those challenges. There is scope to further improvement and a number of options are discussed and explored within this paperPeer reviewe

CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control

Intrinsic motivation is a promising exploration technique for solving reinforcement learning tasks with sparse or absent extrinsic rewards. There exist two technical challenges in implementing intrinsic motivation: 1) how to design a proper intrinsic objective to facilitate efficient exploration; and 2) how to combine the intrinsic objective with the extrinsic objective to help find better solutions. In the current literature, the intrinsic objectives are all designed in a task-agnostic manner and combined with the extrinsic objective via simple addition (or used by itself for reward-free pre-training). In this work, we show that these designs would fail in typical sparse-reward continuous control tasks. To address the problem, we propose Constrained Intrinsic Motivation (CIM) to leverage readily attainable task priors to construct a constrained intrinsic objective, and at the same time, exploit the Lagrangian method to adaptively balance the intrinsic and extrinsic objectives via a simultaneous-maximization framework. We empirically show, on multiple sparse-reward continuous control tasks, that our CIM approach achieves greatly improved performance and sample efficiency over state-of-the-art methods. Moreover, the key techniques of our CIM can also be plugged into existing methods to boost their performances

Finding common ground A pragmatic budgetary instrument for the euro area Bertelsmann Stiftung Policy Paper 8 February 2019

We outline a pragmatic proposal for a budgetary instrument for the euro area in line with the decision of the December 2018 Euro Summit. It is based on a very simple principle: any new instrument should make the euro area function better as a currency union. This is the only way to justify a euro area instrument in the first place. This principle has two implications. First, duplication of existing tools needs to be avoided at all cost. In the current situation, we see a looming risk of layering a new instrument onto existing programmes such as EU structural funds, to which the new instrument would add no real value. Second, the two objectives set out in the Euro Summit decision – competitiveness and convergence – ought to be operationalized strictly in terms of their contribution to a better functioning of the euro area as a currency union