IJCAI Awarded Papers CollectingInternational Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials.
IJCAI 2020, pp.2427-2434, (2020)
Our approach synthesizes an explanation by selecting representative sentences from a product’s reviews, contextualizing the opinions based on aspect-level sentiments from a class of compatible explainable recommendation models
Cited by0BibtexViews265DOI
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IJCAI 2020, pp.354-361, (2020)
Various feedback mechanisms are considered for learning a model of negative side effects and their biases are analyzed empirically
Cited by0BibtexViews227DOI
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international joint conference on artificial intelligence, (2019)
We address the problem of classification with noisy triplets that have been obtained in a passive manner: the examples lie in an unknown metric space, not necessarily Euclidean, and
Cited by2BibtexViews212DOI
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IJCAI, pp.4623-4629, (2018)
We present a commonsense knowledge aware conversational model to demonstrate how commonsense knowledge can facilitate language understanding and generation in open-domain conversational systems
Cited by159BibtexViews670DOI
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IJCAI, pp.4446-4452, (2018)
We propose SentiGAN, which can generate a variety of high-quality texts of different sentiment labels
Cited by55BibtexViews410DOI
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IJCAI, pp.2595-2601, (2018)
We defined a general framework for improving the performance of graph comparison algorithms
Cited by28BibtexViews366DOI
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Chun Kai Ling,Fei Fang,J. Zico Kolter
IJCAI, (2018): 396-402
We demonstrate the effectiveness of our approach on several domains: a toy normal-form game where payoffs depend on external context; a one-card poker game; and a security resource allocation game, which is an extensive-form generalization of defender-attacker game in security do...
Cited by28BibtexViews418DOI
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IJCAI, pp.49-55, (2018)
In the paper we addressed a number of questions related to whether consensus can be achieved in settings where opinions of the agents are affected by social influence phenomena
Cited by17BibtexViews314DOI
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IJCAI, pp.1810-1816, (2018)
An important step into this direction has been made by Kikot and Zolin who identify a large class of conjunctive queries that are rewritable into instance queries: a conjunctive queries is rewritable into an ALCI-instance queries if it is connected and every cycle passes through ...
Cited by3BibtexViews299DOI
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Li Xue
IJCAI, pp.2411-2417, (2018)
In practice, the circumstance that training and test data are clean is not always satisfied. The performance of existing methods in the learning using privileged information (LUPI) paradigm may be seriously challenged, due to the lack of clear strategies to address potential nois...
Cited by1BibtexViews219DOI
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IJCAI, (2017): 1123-1130
While certain forms of aggregation can be simulated by iterating over the object domain, as in our examples in Section 3, such a solution may be too cumbersome for practical use, and it relies on the existence of a linear order over the object domain, which is a strong theoretica...
Cited by8BibtexViews315DOI
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IJCAI, pp.3235-3241, (2016)
We have showed that hierarchical Finite State Controllers can be generated in an incremental fashion to address more challenging generalized planning problems
Cited by11BibtexViews241DOI
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J. Artif. Intell. Res., (2015): 127-168
We focused on a specific type of preprocessing techniques, clause elimination procedures that remove clauses from conjunctive normal form and prenex conjunctive normal form formulas based on different polynomial-time checkable redundancy properties
Cited by60BibtexViews265DOI
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IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence, (2015)
This paper proposed a new approach to solving hard nonconvex optimization problems based on recursive decomposition
Cited by33BibtexViews273DOI
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IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence, pp.3605-3611, (2015)
We proposed a framework for query efficient posterior estimation for expensive blackbox likelihood evaluations
Cited by21BibtexViews338DOI
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Artif. Intell., no. C (2014): 78-122
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general ...
Cited by138BibtexViews163DOI
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IJCAI, pp.1778-1784, (2013)
This paper has shown that it is possible to use random embeddings in Bayesian optimization to optimize functions of high extrinsic dimensionality D, provided that they have low intrinsic dimensionality de
Cited by161BibtexViews355DOI
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IJCAI, pp.2422-2428, (2013)
The advantage of the flexibility metric we propose is twofold: First of all, this metric takes into account the correlations between time events unlike previous methods that have been proposed in the literature
Cited by7BibtexViews233DOI
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IJCAI, pp.649-654, (2011)
We have presented Nested Rollout Policy Adaptation, an Monte Carlo tree search algorithm that uses gradient ascent on its rollout policy to navigate search
Cited by92BibtexViews341DOI
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