Machine teaching (MT) studies the task of designing a training set. Specifically, given a learner (e.g., an artificial neural network or a human) and a target model, a teacher aims to create a training set which results in the target model being learned. MT applications include optimal education design for human learners and computer security where adversaries aim to attack learning-based systems. In this work, we formulate pool-based MT as a state space search problem. We discuss the properties and challenges of the resulting problem and highlight opportunities for novel search techniques. In our preliminary study we use a beam search approach, and find that training and evaluating empirical risk of models dominate the run time of the search. Toward the goal of better search techniques for future work, we develop optimizations ranging from implementation details for specific learners to algorithm changes applicable to general blackbox learners. We conclude with a discussion of open problems and research directions.

The evolution of the electronic sources connected through wide area networks like Internet has encouraged the development of new information gathering techniques that go beyond traditional information retrieval and WEB search methods. They use advanced techniques, like planning or constraint programming, to integrate and reason about hetereogeneous information sources. In this paper we describe MAPWEB. MAPWEB is a multiagent framework that integrates planning agents and WEB information retrieval agents. The goal of this framework is to deal with problems that require planning with information to be gathered from the WEB.

In this paper, we are considering advanced pathplanning problems that feature finding paths for multiple units subject to rich path constraints. Examples of richer constraints are the following of other units or to stay out of sight of a specific unit. Little attention has so far been given to richer pathplanning problem where the objective is more than reaching a specific destination from a starting point such that the path length is minimized. Richer pathplanning problems occur in many complex real-world scenarios, ranging from computer games to military movement planning. In this paper, a novel way to formally specify such problems and a new local-search strategy to solve such problems are proposed and demonstrated by a prototype implementation. Among the design goals are real-time computability as well as extendibility for new constraints and search heuristics.

Is your search engine marketing strategy based on industry-wide best practices? Confused because you're not getting the results you want? You may need personalized SEO recommendations that just aren't applicable to everyone. AI-based search engine modeling can improve your SEO results with personalized solutions. Search engines are constantly evolving.

Many, if not most, search engines in use today are based on keywords, in which the search engine attempts to find the best match for a word or set of words used as input. It's a tried-and-true method that has been deployed millions of times over decades of use. But new search approaches based on deep learning, including vector search and neural search, have emerged recently, and early backers say they have the potential to shake up the search market. Vector search uses a fundamentally different approach to finding the best fit between a term provided as input to the engine and the result that is presented to the user. Instead of powering the search by doing a direct one-to-one matching of keywords, in vector search, the engine attempts to match the input term to a vector, which is an array of features generated from objects in the catalog.

Bing, previously known as Microsoft Live Search, is a search engine with over 40% market share in the US. Bing's SEO guide is aimed at helping small business owners to better optimize their websites for traffic and leads. If you're already using Google's SEO solutions (Analytics, Search Console, etc.) then Bing's guide will give you an edge over your competitors by showing you what additional steps to take and tools to use in order to increase your website traffic. The free SEO analysis tool is an extension for Chrome that automatically analyzes any page that it loads and tells you how well optimized the page is for Bing. Based on its results, you can then use this Bing's SEO guide to optimize your website further.

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In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.

TF-IDF (Term Frequency-Inverse Document Frequency) is a way of measuring how relevant a word is to a document in a collection of documents. TF-IDF has many uses, such as in information retrieval, text analysis, keyword extraction, and as a way of obtaining numeric features from text for machine learning algorithms. TF-IDF was first designed for document search and information retrieval, where a query is run and the system has to find the most relevant documents. Suppose the query is the text "The bug". The system would give each document a higher score proportionally to the frequencies of the query words found in the document, weighting more rare words like "bug" with respect to common words like "the".

It is not a coincidence that search engine optimization is the'holy cow' of internet traffic. It is responsible for more than half of it. Every second, Google alone processes nearly 100,000 search queries. Therefore, content creators do their best to exploit SEO tricks and gimmicks to their advantage and push their websites to the top of the search. People with deep knowledge of search engine optimization can easily find jobs all around the world.