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no code implementations • EACL 2021 • Danushka Bollegala, Huda Hakami, Yuichi Yoshida, Ken-ichi Kawarabayashi

Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.

1 code implementation • 25 Jan 2021 • Danushka Bollegala, Huda Hakami, Yuichi Yoshida, Ken-ichi Kawarabayashi

Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.

no code implementations • 20 Jan 2021 • Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

However, there is a gap of $\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic factors.

no code implementations • NeurIPS 2020 • Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

This paper offers a nearly optimal algorithm for online linear optimization with delayed bandit feedback.

3 code implementations • ICLR 2021 • Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka

Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e. g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.

no code implementations • 4 Aug 2020 • Ken-ichi Kawarabayashi, Bojan Mohar, Roman Nedela, Peter Zeman

The automorphism group of the original map can be reconstructed from the automorphism group of the uniform map in linear time.

Combinatorics Data Structures and Algorithms

no code implementations • NeurIPS 2019 • Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles.

no code implementations • NeurIPS 2019 • Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

\textit{Bandit combinatorial optimization} is a bandit framework in which a player chooses an action within a given finite set $\mathcal{A} \subseteq \{ 0, 1 \}^d$ and incurs a loss that is the inner product of the chosen action and an unobservable loss vector in $\mathbb{R} ^ d$ in each round.

no code implementations • 12 Sep 2019 • Danushka Bollegala, Tomoya Machide, Ken-ichi Kawarabayashi

Next, we reconstruct the search results relevant to the original query term by aggregating the search results retrieved for the decomposed query terms.

no code implementations • ACL 2019 • Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan Boyd-Graber

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.

1 code implementation • 4 Jun 2019 • Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan Boyd-Graber

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.

2 code implementations • ICLR 2020 • Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka

Neural networks have succeeded in many reasoning tasks.

no code implementations • ICLR 2019 • Danushka Bollegala, Huda Hakami, Yuichi Yoshida, Ken-ichi Kawarabayashi

Existing methods for learning KGEs can be seen as a two-stage process where (a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and (b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.

no code implementations • NeurIPS 2018 • Shinji Ito, Daisuke Hatano, Sumita Hanna, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return.

2 code implementations • ICML 2018 • Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka

Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

Ranked #11 on Node Classification on PPI

no code implementations • ICML 2018 • Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi

In this setting, the arms are identified with interventions on a given causal graph, and the effect of an intervention propagates throughout all over the causal graph.

no code implementations • 14 Apr 2018 • Danushka Bollegala, Vincent Atanasov, Takanori Maehara, Ken-ichi Kawarabayashi

We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem.

no code implementations • NeurIPS 2017 • Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

Under these assumptions, we present polynomial-time sublinear-regret algorithms for the online sparse linear regression.

1 code implementation • 19 Sep 2017 • Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi

Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks.

no code implementations • 5 Sep 2017 • Danushka Bollegala, Yuichi Yoshida, Ken-ichi Kawarabayashi

Co-occurrences between two words provide useful insights into the semantics of those words.

1 code implementation • 19 Nov 2015 • Danushka Bollegala, Alsuhaibani Mohammed, Takanori Maehara, Ken-ichi Kawarabayashi

For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.

no code implementations • IJCNLP 2015 • Danushka Bollegala, Takanori Maehara, Ken-ichi Kawarabayashi

Given a pair of \emph{source}-\emph{target} domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domain-specific aspects of word semantics.

no code implementations • 1 May 2015 • Danushka Bollegala, Takanori Maehara, Ken-ichi Kawarabayashi

We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words.

no code implementations • 7 Dec 2014 • Danushka Bollegala, Takanori Maehara, Yuichi Yoshida, Ken-ichi Kawarabayashi

To evaluate the accuracy of the word representations learnt using the proposed method, we use the learnt word representations to solve semantic word analogy problems.

no code implementations • 23 Jan 2014 • Richard Hoshino, Ken-ichi Kawarabayashi

For larger n, we propose a novel "expander construction" that generates an approximate solution to the LD-TTP.

no code implementations • 16 Jan 2014 • Richard Hoshino, Ken-ichi Kawarabayashi

This is an example of a bipartite tournament.

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