Our paper was accepted for Machine Learning.(Springer)

◼︎Information Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo. “Information-theoretic regularization for learning global features by sequential VAE”, Mach Learn (2021). https://doi.org/10.1007/s10994-021-06032-4 ◼︎Overview Sequential variational autoencoders (VAEs) with a global latent variable z have been studied for disentangling the global features of data, which is useful for several downstream tasks. To further assist the sequential VAEs in…

Our paper was accepted for UAI2021.

◼︎Information Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano. “Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces”, 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). ◼︎Overview Numerous invariant (or equivariant) neural networks have succeeded in handling the invariant data such as point clouds and graphs. However, a generalization theory for…

Our paper was accepted for ICML2021.

【Information】 Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu. “Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning”, International Conference on Machine Learning 2021 (ICML2021). July 2021. 【Overview】 Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing…

Our paper was accepted for ACL-IJCNLP 2021 (Findings).

【NEWS】Our paper was accepted to ACL-IJCNLP 2021 (Findings) 【Title】LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer 【Authors】Machel Reid and Victor Zhong (University of Washington) 【Overview】Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from “I had a terrible time…” to “I had a great time…”). We propose…

Our paper was accepted for L4DC.

【Information】 Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo. Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning. Learning for Dynamics and Control (L4DC) Conference. June 2021. 【Overview】 There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In meta-RL, the…

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.