Spain

ECAI 2020

Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings

dynamic cost constraints

greedy algorithm

submodular maximization

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Extended Markov Games to Learn Complex Specifications in Multi-Agent Reinforcement Learning

Efficient Neural Network Verification via Adaptive Refinement and Adversarial Search

Conference Presentation Recording for ECAI2020 paper 'Adaptive Graph Reasoning Network for Fashion Landmark Detection'

Feature-level Ensemble Knowledge Distillation for Aggregating Knowledge from Multiple Networks

Crowd Counting via Hierarchical Scale Recalibration Network

Learning a Latent Space of Style-Aware Symbolic Music Representations by Adversarial Autoencoders

Adaptive Local Neighbors for Transfer Discriminative Feature Learning

Analysing Irresolute Multiwinner Voting Rules via SAT solving

DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners

Learning Deterministic Automata on Infinite Words

https://d3smihljt9218e.cloudfront.net/transcript/984/subtitle/c0924b403782b5f299da5da341532e6a.vtt

English (automatic)

Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings

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Next from ECAI 2020

Extended Markov Games to Learn Complex Specifications in Multi-Agent Reinforcement Learning

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DASH: A Distributed and Parallelizable Algorithm for Size-Constrained Submodular Maximization

Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings

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Next from ECAI 2020

Extended Markov Games to Learn Complex Specifications in Multi-Agent Reinforcement Learning

Similar lecture

DASH: A Distributed and Parallelizable Algorithm for Size-Constrained Submodular Maximization

Downloads