Our paper was accepted for ICLR2021.

Our paper was accepted for presentation at ICLR2021. 【Information】Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Shane Gu. “Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization”, International Conference on Learning Representations 2021 (ICLR2021). May 2021. 【Overview】Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to…

Our paper was accepted for ICLR2021.

Our paper was accepted for presentation at ICLR2021. 【Information】Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, and Yutaka Matsuo. “Group Equivariant Conditional Neural Processes”, International Conference on Learning Representations 2021 (ICLR2021). May 2021. 【Overview】We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional…

Our paper was accepted for AAAI 2021.

Our paper was accepted for presentation at AAAI 2021. 【Information】Edison Marrese-Taylor, Machel Reid and Yutaka Matsuo. Variational Inference for Learning Representations of Natural Language Edits. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). February 2021. 【Title】Variational Inference for Learning Representations of Natural Language Edits 【Authors】Edison Marrese-Taylor, Machel Reid, Yutaka Matsuo 【Overview】Document editing has become a pervasive…

Our paper was accepted for Frontiers in Robotics & AI.

Our paper was accepted for Frontiers in Robotics & AI. 【Information】Tatsuya Matsushima, Naruya Kondo, Yusuke Iwasawa, Kaoru Nasuno, Yutaka Matsuo: Modeling Task Uncertainty for Safe Meta-imitation Learning, Frontiers in Robotics and AI, Vol. 7, pp.189, https://www.frontiersin.org/article/10.3389/frobt.2020.606361 (2020) 【Overview】To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their…

当研究室の研究がキオクシア株式会社による「2020年度 キオクシア奨励研究」に採択されました

 

予算名:2020年度キオクシア奨励研究
代表者:松尾豊(松嶋達也)
課題題目:ロボット学習におけるオフラインデータの活用と実機転用に関する研究
課題概要:オフラインデータを用いて一種のシミュレータとしてロボットの環境のモデル(世界モデル)を構築し,実ロボット制御への応用するための研究を行う。とくに,本研究期間では,申請者らのこれまでの研究成果を実ロボットに応用することで,手法の実用性の評価と実用に向けた課題の解決を目指す.

Our paper was accepted for EMNLP2020.

Our paper was accepted to the main conference at EMNLP 2020. 【Information】Our paper was accepted to the main conference at EMNLP 2020 【Title】VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling 【Authors】Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo 【Overview】In this paper, we tackle the task of definition modeling, where the goal is…

当研究室の論文が人工知能学会論文誌に採録されました

当研究室の論文が人工知能学会論文誌に採録されました。 【Information】 田村 浩一郎,松尾 豊: ソーシャルメディアにおける影響関係から金融市場に対する作用のモデル化と分析, 人工知能学会論文誌, Vol.35, No.6(2020)   【Overview】 The data of social media has received much attention to observe and predict real-world events. For example, It is used to predict financial markets, products demand, and voter turnout. While these works regards social media as a sensor of real world, as social media…

当研究室の論文がIJCAI2020に採択されました

Our paper for IJCAI2020 were accepted. 【タイトル】Stabilizing Adversarial Invariance Induction from Divergence Minimization Perspective 【概要】 Adversarial invariance induction (AII) is a generic and powerful framework for enforcing an invariance to nuisance attributes into neural network representations. However, its optimization is often unstable and little is known about its practical behavior. This paper presents an analysis of…

当研究室の論文がWI2019のBest Student Paper Awardを受賞しました

当研究室博士1年の中川大海くんの発表が、WI2019のBest Student Paper Awardを受賞しました。

 

【タイトル】Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network
【概要】
オンライン教育サービス上における受講者の学習ログを元に各受講者の習熟状況を予測する”knowledge tracing”のタスクの改善を目的とした研究。コンテンツ間の関係性をグラフ構造と見なした上でGraph Neural Networksを用いて受講者の習熟を定式化する手法を提案し、従来手法に比べて予測の精度と解釈性を改善した。

【著者】中川大海,岩澤有祐,松尾豊

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Our paper for ICLR2019 Workshop (Limited Label Data), ECML PKDD were accepted.

Our paper for ICLR2019 Workshop (Limited Label Data), ECML PKDD were accepted. 【Information】 Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo: “Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization”, in Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. 【Overview】 Learning domain-invariant representation is a dominant approach…