Lihong Li

Bio: Lihong Li is an academic researcher from Microsoft. The author has contributed to research in topics: Cognitive models of information retrieval & Information seeking. The author has an hindex of 1, co-authored 1 publications receiving 88 citations.

Online Evaluation for Information Retrieval

Abstract: Online evaluation is one of the most common approaches to measure the effectiveness of an information retrieval system. It involves fielding the information retrieval system to real users, and observing these users' interactions in-situ while they engage with the system. This allows actual users with real world information needs to play an important part in assessing retrieval quality. As such, online evaluation complements the common alternative offline evaluation approaches which may provide more easily interpretable outcomes, yet are often less realistic when measuring of quality and actual user experience.In this survey, we provide an overview of online evaluation techniques for information retrieval. We show how online evaluation is used for controlled experiments, segmenting them into experiment designs that allow absolute or relative quality assessments. Our presentation of different metrics further partitions online evaluation based on different sized experimental units commonly of interest: documents, lists and sessions. Additionally, we include an extensive discussion of recent work on data re-use, and experiment estimation based on historical data.A substantial part of this work focuses on practical issues: How to run evaluations in practice, how to select experimental parameters, how to take into account ethical considerations inherent in online evaluations, and limitations that experimenters should be aware of. While most published work on online experimentation today is at large scale in systems with millions of users, we also emphasize that the same techniques can be applied at small scale. To this end, we emphasize recent work that makes it easier to use at smaller scales and encourage studying real-world information seeking in a wide range of scenarios. Finally, we present a summary of the most recent work in the area, and describe open problems, as well as postulating future directions.

Experimental And Quasi Experimental Designs For Research

Abstract: Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Neural Approaches to Conversational AI

Abstract: The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.

Neural Approaches to Conversational AI

Abstract: This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.

Statistical Rules-of-Thumb.

Abstract: Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this statistical rules of thumb tends to be the representative book in this website.

Introduction to Multi-Armed Bandits

Abstract: Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments; many of the chapters conclude with exercises. The book is structured as follows. The first four chapters are on IID rewards, from the basic model to impossibility results to Bayesian priors to Lipschitz rewards. The next three chapters cover adversarial rewards, from the full-feedback version to adversarial bandits to extensions with linear rewards and combinatorially structured actions. Chapter 8 is on contextual bandits, a middle ground between IID and adversarial bandits in which the change in reward distributions is completely explained by observable contexts. The last three chapters cover connections to economics, from learning in repeated games to bandits with supply/budget constraints to exploration in the presence of incentives. The appendix provides sufficient background on concentration and KL-divergence. The chapters on "bandits with similarity information", "bandits with knapsacks" and "bandits and agents" can also be consumed as standalone surveys on the respective topics.