Igor Labutov

LAER AI, Founder

I am a Machine Learning and Natural Language Processing researcher interested in building teachable personal assistants. Broadly, I am interested in the related problems of interactive semantic parsing, question answering and knowledge base construction.

I obtained my PhD from Cornell University, working with Christoph Studer and Hod Lipson

I spent two years as a Postdoc in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on building an instructable (teachable) personal assistant.

In 2018, together with Bishan Yang, I co-founded a startup called LAER.AI, building next-generation enterprise intelligence. We are actively hiring!

Publications

Learning to Learn Semantic Parsers from Natural Language Supervision

I Labutov, Bishan Yang and Tom Mitchell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

I Labutov and Bishan Yang
Proceedings of the Association for Computational Linguistics Vo. 1. 2018

Joint concept learning and semantic parsing from natural language explanations

S Srivastava, I Labutov, Tom Mitchell
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Zero-shot learning of classifiers from natural language quantification

S Srivastava, I Labutov, Tom Mitchell
Proceedings of the Association for Computational Linguistics Vo. 1. 2018

LIA: A Natural Language Programmable Personal Assistant

I Labutov, Shashank Srivastava and Tom Mitchell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Demo Paper)

APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Natural Language Instructions

Toby Jia-Jun Li, I Labutov, Xiaohan Nancy Li, Xiaoyi Zhang, Wenze Shi, Wanling Ding, Tom Mitchell and Brad A. Myers
2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2018

Designing a conversational interface for a multimodal smartphone programming-by-demonstration agent

Toby Jia-Jun Li, I Labutov, Brad A. Myers, Amos Azaria, Alexander I. Rudnicky and Tom Mitchell
Conversational UX Design CHI 2017 Workshop, Denver CO

An End User Development Approach for Failure Handling in Goal-oriented Conversational Agents

Toby Jia-Jun Li, I Labutov, Brad A. Myers, Amos Azaria, Alexander I. Rudnicky and Tom Mitchell
Book Chapter. Studies in Conversational UX Design, Springer (2018)

Evaluation methods for unsupervised word embeddings

T Schnabel, I Labutov, David Mimno, Thorsten Joachims
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Prrocessing

Semi-supervised Techniques for Mining Learning Outcomes and Prerequisites

I Labutov, Yun Huang, Peter Brusilovsky, Daqing He
Proceedings of the 23rd ACM SIGKDD international conference on Knowledge Discovery and Data Mining

Unbounded human learning: Optimal scheduling for spaced repetition

S Reddy, I Labutov, S Banerjee, Thorsten Joachims
Proceedings of the 22nd ACM SIGKDD international conference on Knowledge Discovery and Data Mining

Learning student and content embeddings for personalized lesson sequence recommendation

S Reddy, I Labutov, Thorsten Joachims
Proceedings of the Third (2016) ACM Conference on Learning@ Scale, 93-96

Re-embedding words

I Labutov, H Lipson
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics

Deep questions without deep understanding

I Labutov, Sumit Basu, Lucy Vanderwende
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics

Humor as circuits in semantic networks

I Labutov, H Lipson
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics

Calibrated Self-Assessment

I Labutov, C Studer
Proceedings for Educational Data Mining (EDM) Conference 2016

Generating code-switched text for lexical learning

I Labutov, H Lipson
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics