paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i7.16807 | On Exploiting Hitting Sets for Model Reconciliation | https://ojs.aaai.org/index.php/AAAI/article/view/16807 | https://ojs.aaai.org/index.php/AAAI/article/download/16807/16614 | [
"Stylianos Loukas Vasileiou",
"Alessandro Previti",
"William Yeoh"
] | In human-aware planning, a planning agent may need to provide an explanation to a human user on why its plan is optimal. A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model such that the plan is also optimal in the human's m... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16807 | 35 | 7 | 6514-6521 | official | 2012.09274 | title_snapshot |
10.1609/aaai.v35i7.16806 | On the Tractability of SHAP Explanations | https://ojs.aaai.org/index.php/AAAI/article/view/16806 | https://ojs.aaai.org/index.php/AAAI/article/download/16806/16613 | [
"Guy Van den Broeck",
"Anton Lykov",
"Maximilian Schleich",
"Dan Suciu"
] | SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction of a machine learning model. Despite a lot of recent interest from both academia and industry, it is not known whether SHAP explanations of ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16806 | 35 | 7 | 6505-6513 | official | 2009.08634 | title_snapshot |
10.1609/aaai.v35i7.16805 | Strong Explanations in Abstract Argumentation | https://ojs.aaai.org/index.php/AAAI/article/view/16805 | https://ojs.aaai.org/index.php/AAAI/article/download/16805/16612 | [
"Markus Ulbricht",
"Johannes P. Wallner"
] | argumentation constitutes both a major research strand and a key approach that provides the core reasoning engine for a multitude of formalisms in computational argumentation in AI. Reasoning in abstract argumentation is carried out by viewing arguments and their relationships as abstract entities, with argumentation f... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16805 | 35 | 7 | 6496-6504 | official | null | null |
10.1609/aaai.v35i7.16804 | Stratified Negation in Datalog with Metric Temporal Operators | https://ojs.aaai.org/index.php/AAAI/article/view/16804 | https://ojs.aaai.org/index.php/AAAI/article/download/16804/16611 | [
"David J Tena Cucala",
"Przemysław A Wałęga",
"Bernardo Cuenca Grau",
"Egor Kostylev"
] | We extend DatalogMTL—Datalog with operators from metric temporal logic—by adding stratified negation as failure. The new language provides additional expressive power for representing and reasoning about temporal data and knowledge in a wide range of applications. We consider models over the rational timeline, study th... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16804 | 35 | 7 | 6488-6495 | official | null | null |
10.1609/aaai.v35i7.16803 | Quantification of Resource Production Incompleteness | https://ojs.aaai.org/index.php/AAAI/article/view/16803 | https://ojs.aaai.org/index.php/AAAI/article/download/16803/16610 | [
"Yakoub Salhi"
] | In a situation where an agent has to produce specific resources using the available ones, it may not be possible to achieve the complete goal, but only obtaining some of its parts.This incompleteness problem calls for reasoning models to make rational decisions. In this paper, we introduce a logic-based framework for m... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16803 | 35 | 7 | 6480-6487 | official | null | null |
10.1609/aaai.v35i7.16790 | Mining EL Bases with Adaptable Role Depth | https://ojs.aaai.org/index.php/AAAI/article/view/16790 | https://ojs.aaai.org/index.php/AAAI/article/download/16790/16597 | [
"Ricardo Guimarães",
"Ana Ozaki",
"Cosimo Persia",
"Baris Sertkaya"
] | In Formal Concept Analysis, a base for a finite structure is a set of implications that characterizes all valid implications of the structure. This notion can be adapted to the context of Description Logic, where the base consists of a set of concept inclusions instead of implications. In this setting, concept expressi... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16790 | 35 | 7 | 6367-6374 | official | 2102.10689 | title_snapshot |
10.1609/aaai.v35i7.16789 | Constraint Logic Programming for Real-World Test Laboratory Scheduling | https://ojs.aaai.org/index.php/AAAI/article/view/16789 | https://ojs.aaai.org/index.php/AAAI/article/download/16789/16596 | [
"Tobias Geibinger",
"Florian Mischek",
"Nysret Musliu"
] | The Test Laboratory Scheduling Problem (TLSP) and its subproblem TLSP-S are real-world industrial scheduling problems that are extensions of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, TLSP includes a grouping phase where the jobs to be scheduled have to be assem... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16789 | 35 | 7 | 6358-6366 | official | null | null |
10.1609/aaai.v35i7.16791 | REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement | https://ojs.aaai.org/index.php/AAAI/article/view/16791 | https://ojs.aaai.org/index.php/AAAI/article/download/16791/16598 | [
"Yinya Huang",
"Meng Fang",
"Xunlin Zhan",
"Qingxing Cao",
"Xiaodan Liang"
] | When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commonsense knowledge bases to supply additional information to each question, there is still ample opportunity to advance it on the quality of t... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16791 | 35 | 7 | 6375-6383 | official | 2012.13185 | title_snapshot |
10.1609/aaai.v35i7.16792 | (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/16792 | https://ojs.aaai.org/index.php/AAAI/article/download/16792/16599 | [
"Jena D. Hwang",
"Chandra Bhagavatula",
"Ronan Le Bras",
"Jeff Da",
"Keisuke Sakaguchi",
"Antoine Bosselut",
"Yejin Choi"
] | Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for ta... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16792 | 35 | 7 | 6384-6392 | official | 2010.05953 | title_snapshot |
10.1609/aaai.v35i7.16793 | Commonsense Knowledge Augmentation for Low-Resource Languages via Adversarial Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16793 | https://ojs.aaai.org/index.php/AAAI/article/download/16793/16600 | [
"Bosung Kim",
"Juae Kim",
"Youngjoong Ko",
"Jungyun Seo"
] | Commonsense reasoning is one of the ultimate goals of artificial intelligence research because it simulates the human thinking process. However, most commonsense reasoning studies have focused on English because available commonsense knowledge for low-resource languages is scarce due to high construction costs. Transla... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16793 | 35 | 7 | 6393-6401 | official | null | null |
10.1609/aaai.v35i7.16794 | Parameterized Logical Theories | https://ojs.aaai.org/index.php/AAAI/article/view/16794 | https://ojs.aaai.org/index.php/AAAI/article/download/16794/16601 | [
"Fangzhen Lin"
] | A theory in first-order logic is a set of sentences. A parameterized theory is a first-order theory with some of its predicates and functions identified as parameters, together with some import statements that call other parameterized theories. A KB is then a collection of these interconnected parameterised theories, s... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16794 | 35 | 7 | 6402-6409 | official | null | null |
10.1609/aaai.v35i7.16795 | Learning Term Embeddings for Lexical Taxonomies | https://ojs.aaai.org/index.php/AAAI/article/view/16795 | https://ojs.aaai.org/index.php/AAAI/article/download/16795/16602 | [
"Jingping Liu",
"Menghui Wang",
"Chao Wang",
"Jiaqing Liang",
"Lihan Chen",
"Haiyun Jiang",
"Yanghua Xiao",
"Yunwen Chen"
] | Lexical taxonomies, a special kind of knowledge graph, are essential for natural language understanding. This paper studies the problem of lexical taxonomy embedding. Most existing graph embedding methods are difficult to apply to lexical taxonomies since 1) they ignore implicit but important information, namely, sibli... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16795 | 35 | 7 | 6410-6417 | official | null | null |
10.1609/aaai.v35i7.16796 | KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/16796 | https://ojs.aaai.org/index.php/AAAI/article/download/16796/16603 | [
"Ye Liu",
"Yao Wan",
"Lifang He",
"Hao Peng",
"Philip S. Yu"
] | Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sen... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16796 | 35 | 7 | 6418-6425 | official | 2009.12677 | title_snapshot |
10.1609/aaai.v35i7.16797 | Parameterized Complexity of Logic-Based Argumentation in Schaefer's Framework | https://ojs.aaai.org/index.php/AAAI/article/view/16797 | https://ojs.aaai.org/index.php/AAAI/article/download/16797/16604 | [
"Yasir Mahmood",
"Arne Meier",
"Johannes Schmidt"
] | Logic-based argumentation is a well-established formalism modeling nonmonotonic reasoning. It has been playing a major role in AI for decades, now. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the c... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16797 | 35 | 7 | 6426-6434 | official | 2102.11782 | title_snapshot |
10.1609/aaai.v35i7.16798 | Ranking Sets of Defeasible Elements in Preferential Approaches to Structured Argumentation: Postulates, Relations, and Characterizations | https://ojs.aaai.org/index.php/AAAI/article/view/16798 | https://ojs.aaai.org/index.php/AAAI/article/download/16798/16605 | [
"Jan Maly",
"Johannes P. Wallner"
] | Preferences play a key role in computational argumentation in AI, as they reflect various notions of argument strength vital for the representation of argumentation. Within central formal approaches to structured argumentation, preferential approaches are applied by lifting preferences over defeasible elements to ranki... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16798 | 35 | 7 | 6435-6443 | official | null | null |
10.1609/aaai.v35i7.16799 | GENSYNTH: Synthesizing Datalog Programs without Language Bias | https://ojs.aaai.org/index.php/AAAI/article/view/16799 | https://ojs.aaai.org/index.php/AAAI/article/download/16799/16606 | [
"Jonathan Mendelson",
"Aaditya Naik",
"Mukund Raghothaman",
"Mayur Naik"
] | Techniques for learning logic programs from data typically rely on language bias mechanisms to restrict the hypothesis space. These methods are therefore limited by the user's ability to tune them such that the hypothesis space is simultaneously large enough to include the target program but small enough to admit a tra... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16799 | 35 | 7 | 6444-6453 | official | null | null |
10.1609/aaai.v35i7.16800 | Parameterized Complexity of Small Decision Tree Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16800 | https://ojs.aaai.org/index.php/AAAI/article/download/16800/16607 | [
"Sebastian Ordyniak",
"Stefan Szeider"
] | We study the NP-hard problem of learning a decision tree (DT) of smallest depth or size from data. We provide the first parameterized complexity analysis of the problem and draw a detailed parameterized complexity map for the natural parameters: size or depth of the DT, maximum domain size of all features, and the maxi... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16800 | 35 | 7 | 6454-6462 | official | null | null |
10.1609/aaai.v35i7.16801 | Interpreting Neural Networks as Quantitative Argumentation Frameworks | https://ojs.aaai.org/index.php/AAAI/article/view/16801 | https://ojs.aaai.org/index.php/AAAI/article/download/16801/16608 | [
"Nico Potyka"
] | We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. This connection creates a bridge between research in Formal Argumentation and Machine Learning. We generalize the semantics of feed-forward neural networks to acyclic graphs and study the result... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16801 | 35 | 7 | 6463-6470 | official | null | null |
10.1609/aaai.v35i7.16802 | ChronoR: Rotation Based Temporal Knowledge Graph Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/16802 | https://ojs.aaai.org/index.php/AAAI/article/download/16802/16609 | [
"Ali Sadeghian",
"Mohammadreza Armandpour",
"Anthony Colas",
"Daisy Zhe Wang"
] | Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficul... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16802 | 35 | 7 | 6471-6479 | official | 2103.10379 | title_snapshot |
10.1609/aaai.v35i7.16788 | Knowledge-Base Degrees of Inconsistency: Complexity and Counting | https://ojs.aaai.org/index.php/AAAI/article/view/16788 | https://ojs.aaai.org/index.php/AAAI/article/download/16788/16595 | [
"Johannes K. Fichte",
"Markus Hecher",
"Arne Meier"
] | Description logics (DLs) are knowledge representation languages that are used in the field of artificial intelligence (AI). A common technique is to query DL knowledge bases, e.g., by Boolean Datalog queries, and ask for entailment. But real world knowledge-bases are often obtained by combining data from various source... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16788 | 35 | 7 | 6349-6357 | official | null | null |
10.1609/aaai.v35i7.16787 | Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics | https://ojs.aaai.org/index.php/AAAI/article/view/16787 | https://ojs.aaai.org/index.php/AAAI/article/download/16787/16594 | [
"Oliver Fernández Gil",
"Anni-Yasmin Turhan"
] | Classical regular path queries (RPQs) can be too restrictive for some applications and answering such queries under approximate semantics to relax the query is desirable. While for answering regular path queries over graph databases under approximate semantics algorithms are available, such algorithms are scarce for th... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16787 | 35 | 7 | 6340-6348 | official | null | null |
10.1609/aaai.v35i7.16786 | A Simple Framework for Cognitive Planning | https://ojs.aaai.org/index.php/AAAI/article/view/16786 | https://ojs.aaai.org/index.php/AAAI/article/download/16786/16593 | [
"Jorge Luis Fernandez Davila",
"Dominique Longin",
"Emiliano Lorini",
"Frédéric Maris"
] | We present a novel approach to cognitive planning, i.e., an agent's planning aimed at changing the cognitive attitudes of another agent including her beliefs and intentions. We encode the cognitive planning problem in an epistemic logic with a semantics exploiting belief bases. We study a NP-fragment of the logic whose... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16786 | 35 | 7 | 6331-6339 | official | null | null |
10.1609/aaai.v35i7.16785 | SMT-based Safety Checking of Parameterized Multi-Agent Systems | https://ojs.aaai.org/index.php/AAAI/article/view/16785 | https://ojs.aaai.org/index.php/AAAI/article/download/16785/16592 | [
"Paolo Felli",
"Alessandro Gianola",
"Marco Montali"
] | We study the problem of verifying whether a given parameterized multi-agent system (PMAS) is safe, namely whether none of its possible executions can lead to bad states. These are captured by a state formula existentially quantifying over agents. As the MAS is parameterized, it only describes the finite set of possible... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16785 | 35 | 7 | 6321-6330 | official | null | null |
10.1609/aaai.v35i7.16784 | Treewidth-Aware Complexity in ASP: Not all Positive Cycles are Equally Hard | https://ojs.aaai.org/index.php/AAAI/article/view/16784 | https://ojs.aaai.org/index.php/AAAI/article/download/16784/16591 | [
"Jorge Fandinno",
"Markus Hecher"
] | It is well-known that deciding consistency for normal answer set programs (ASP) is NP-complete, thus, as hard as the satisfaction problem for propositional logic (SAT). The exponential time hypothesis (ETH) implies that the best algorithms to solve these problems take exponential time in the worst case. However, accoun... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16784 | 35 | 7 | 6312-6320 | official | 2007.04620 | title_snapshot |
10.1609/aaai.v35i7.16783 | On the Complexity of Sum-of-Products Problems over Semirings | https://ojs.aaai.org/index.php/AAAI/article/view/16783 | https://ojs.aaai.org/index.php/AAAI/article/download/16783/16590 | [
"Thomas Eiter",
"Rafael Kiesel"
] | Many important problems in AI, among them SAT, #SAT, and probabilistic inference, amount to Sum-of-Products Problems, i.e. evaluating a sum of products of values from some semiring R. While efficiently solvable cases are known, a systematic study of the complexity of this problem is missing. We characterize the latter ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16783 | 35 | 7 | 6304-6311 | official | null | null |
10.1609/aaai.v35i7.16769 | A General Setting for Gradual Semantics Dealing with Similarity | https://ojs.aaai.org/index.php/AAAI/article/view/16769 | https://ojs.aaai.org/index.php/AAAI/article/download/16769/16576 | [
"Leila Amgoud",
"Victor David"
] | The paper discusses theoretical foundations that describe principles and processes involved in defining semantics that deal with similarity between arguments. Such semantics compute the strength of an argument on the basis of the strengths of its attackers, similarities between those attackers, and an initial weight as... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16769 | 35 | 7 | 6185-6192 | official | null | null |
10.1609/aaai.v35i7.16768 | Argumentation Frameworks with Strong and Weak Constraints: Semantics and Complexity | https://ojs.aaai.org/index.php/AAAI/article/view/16768 | https://ojs.aaai.org/index.php/AAAI/article/download/16768/16575 | [
"Gianvincenzo Alfano",
"Sergio Greco",
"Francesco Parisi",
"Irina Trubitsyna"
] | Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in formal argumentation. Key aspects of the success and popularity of Dung's framework include its simplicity and expressiveness. Integrity constraints help to express domain knowledge in a compact and natural way, thus keeping easy the mod... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16768 | 35 | 7 | 6175-6184 | official | null | null |
10.1609/aaai.v35i7.16770 | Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions | https://ojs.aaai.org/index.php/AAAI/article/view/16770 | https://ojs.aaai.org/index.php/AAAI/article/download/16770/16577 | [
"Alessandro Artale",
"Jean Christoph Jung",
"Andrea Mazzullo",
"Ana Ozaki",
"Frank Wolter"
] | The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP transform potentially hard existence proble... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16770 | 35 | 7 | 6193-6201 | official | 2007.02736 | title_judge |
10.1609/aaai.v35i7.16771 | Equivalent Causal Models | https://ojs.aaai.org/index.php/AAAI/article/view/16771 | https://ojs.aaai.org/index.php/AAAI/article/download/16771/16578 | [
"Sander Beckers"
] | The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16771 | 35 | 7 | 6202-6209 | official | 2012.05603 | title_snapshot |
10.1609/aaai.v35i7.16772 | The Counterfactual NESS Definition of Causation | https://ojs.aaai.org/index.php/AAAI/article/view/16772 | https://ojs.aaai.org/index.php/AAAI/article/download/16772/16579 | [
"Sander Beckers"
] | Beckers & Vennekens recently proposed a definition of actual causation that is based on certain plausible principles, thereby allowing the debate on causation to shift away from its heavy focus on examples towards a more systematic analysis. This paper contributes to that analysis in two ways. First, I show that their ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16772 | 35 | 7 | 6210-6217 | official | 2012.05123 | title_snapshot |
10.1609/aaai.v35i7.16773 | Network Satisfaction for Symmetric Relation Algebras with a Flexible Atom | https://ojs.aaai.org/index.php/AAAI/article/view/16773 | https://ojs.aaai.org/index.php/AAAI/article/download/16773/16580 | [
"Manuel Bodirsky",
"Simon Knäuer"
] | Robin Hirsch posed in 1996 the Really Big Complexity Problem: classify the computational complexity of the network satisfaction problem for all finite relation algebras A. We provide a complete classification for the case that A is symmetric and has a flexible atom; the problem is in this case NP-complete or in P. If a... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16773 | 35 | 7 | 6218-6226 | official | 2008.11943 | title_judge |
10.1609/aaai.v35i7.16774 | Conditional Inference under Disjunctive Rationality | https://ojs.aaai.org/index.php/AAAI/article/view/16774 | https://ojs.aaai.org/index.php/AAAI/article/download/16774/16581 | [
"Richard Booth",
"Ivan Varzinczak"
] | The question of conditional inference, i.e., of which conditional sentences of the form ``if A then, normally, B'' should follow from a set KB of such sentences, has been one of the classic questions of AI, with several well-known solutions proposed. Perhaps the most notable is the rational closure construction of Lehm... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16774 | 35 | 7 | 6227-6234 | official | null | null |
10.1609/aaai.v35i7.16775 | Algebra of Modular Systems: Containment and Equivalence | https://ojs.aaai.org/index.php/AAAI/article/view/16775 | https://ojs.aaai.org/index.php/AAAI/article/download/16775/16582 | [
"Andrei Bulatov",
"Eugenia Ternovska"
] | The Algebra of Modular System is a KR formalism that allows for combinations of modules written in multiple languages. Informally, a module represents a piece of knowledge. It can be given by a knowledge base, be an agent, an ASP, ILP, CP program, etc. Formally, a module is a class of structures over the same vocabular... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16775 | 35 | 7 | 6235-6243 | official | null | null |
10.1609/aaai.v35i7.16776 | Certifying Top-Down Decision-DNNF Compilers | https://ojs.aaai.org/index.php/AAAI/article/view/16776 | https://ojs.aaai.org/index.php/AAAI/article/download/16776/16583 | [
"Florent Capelli",
"Jean-Marie Lagniez",
"Pierre Marquis"
] | Certifying the output of tools solving complex problems so as to ensure the correctness of the results they provide is of tremendous importance. Despite being widespread for SAT-solvers, this level of exigence has not yet percolated for tools solving more complex tasks, such as model counting or knowledge compilation. ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16776 | 35 | 7 | 6244-6253 | official | null | null |
10.1609/aaai.v35i7.16777 | Contextual Conditional Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/16777 | https://ojs.aaai.org/index.php/AAAI/article/download/16777/16584 | [
"Giovanni Casini",
"Thomas Meyer",
"Ivan Varzinczak"
] | We extend the expressivity of classical conditional reasoning by introducing context as a new parameter. The enriched conditional logic generalises the defeasible setting in the style of Kraus, Lehmann and Magidor, and allows for a more refined representation of an agent’s epistemic state, distinguishing, for example, ... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16777 | 35 | 7 | 6254-6261 | official | null | null |
10.1609/aaai.v35i7.16778 | Preferred Explanations for Ontology-Mediated Queries under Existential Rules | https://ojs.aaai.org/index.php/AAAI/article/view/16778 | https://ojs.aaai.org/index.php/AAAI/article/download/16778/16585 | [
"İsmail İlkan Ceylan",
"Thomas Lukasiewicz",
"Enrico Malizia",
"Cristian Molinaro",
"Andrius Vaicenavičius"
] | Recently, explanations for query answers under existential rules have been investigated, where an explanation is an inclusion-minimal subset of a given database that, together with the ontology, entails the query. In this paper, we take a step further and study explanations under different minimality criteria. In parti... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16778 | 35 | 7 | 6262-6270 | official | null | null |
10.1609/aaai.v35i7.16779 | Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/16779 | https://ojs.aaai.org/index.php/AAAI/article/download/16779/16586 | [
"Jiajun Chen",
"Huarui He",
"Feng Wu",
"Jie Wang"
] | Inductive link prediction---where entities during training and inference stages can be different---has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing appro... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16779 | 35 | 7 | 6271-6278 | official | 2103.03642 | title_snapshot |
10.1609/aaai.v35i7.16780 | A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving | https://ojs.aaai.org/index.php/AAAI/article/view/16780 | https://ojs.aaai.org/index.php/AAAI/article/download/16780/16587 | [
"Maxwell Crouse",
"Ibrahim Abdelaziz",
"Bassem Makni",
"Spencer Whitehead",
"Cristina Cornelio",
"Pavan Kapanipathi",
"Kavitha Srinivas",
"Veronika Thost",
"Michael Witbrock",
"Achille Fokoue"
] | Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to obviate the need for such heuristics, however, its deployment in automated theorem proving remains a challenge. In this paper we introduce TR... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16780 | 35 | 7 | 6279-6287 | official | 1911.02065 | title_snapshot |
10.1609/aaai.v35i7.16781 | Recursion in Abstract Argumentation is Hard --- On the Complexity of Semantics Based on Weak Admissibility | https://ojs.aaai.org/index.php/AAAI/article/view/16781 | https://ojs.aaai.org/index.php/AAAI/article/download/16781/16588 | [
"Wolfgang Dvořák",
"Markus Ulbricht",
"Stefan Woltran"
] | We study the computational complexity of abstract argumentation semantics based on weak admissibility, a recently introduced concept to deal with arguments of self-defeating nature. Our results reveal that semantics based on weak admissibility are of much higher complexity (under typical assumptions) compared to all ar... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16781 | 35 | 7 | 6288-6295 | official | null | null |
10.1609/aaai.v35i7.16782 | The Complexity Landscape of Claim-Augmented Argumentation Frameworks | https://ojs.aaai.org/index.php/AAAI/article/view/16782 | https://ojs.aaai.org/index.php/AAAI/article/download/16782/16589 | [
"Wolfgang Dvořák",
"Alexander Greßler",
"Anna Rapberger",
"Stefan Woltran"
] | Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to general... | main | Knowledge Representation and Reasoning | 10.1609/aaai.v35i7.16782 | 35 | 7 | 6296-6303 | official | null | null |
10.1609/aaai.v35i8.16903 | Deep Graph Spectral Evolution Networks for Graph Topological Evolution | https://ojs.aaai.org/index.php/AAAI/article/view/16903 | https://ojs.aaai.org/index.php/AAAI/article/download/16903/16710 | [
"Negar Etemadyrad",
"Qingzhe Li",
"Liang Zhao"
] | Characterizing the underlying mechanism of graph topological evolution from a source graph to a target graph has attracted fast increasing attention in the deep graph learning domain. However, it is very challenging to build expressive and efficient models that can handle global and local evolution patterns between sou... | main | Machine Learning | 10.1609/aaai.v35i8.16903 | 35 | 8 | 7358-7366 | official | null | null |
10.1609/aaai.v35i8.16904 | Adversarial Training and Provable Robustness: A Tale of Two Objectives | https://ojs.aaai.org/index.php/AAAI/article/view/16904 | https://ojs.aaai.org/index.php/AAAI/article/download/16904/16711 | [
"Jiameng Fan",
"Wenchao Li"
] | We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. We formulate the training problem as a joint optimization problem with both empirical and provable robustness objectives and develop a novel gradient-descent techniq... | main | Machine Learning | 10.1609/aaai.v35i8.16904 | 35 | 8 | 7367-7376 | official | 2008.06081 | title_snapshot |
10.1609/aaai.v35i8.16905 | Learning a Gradient-free Riemannian Optimizer on Tangent Spaces | https://ojs.aaai.org/index.php/AAAI/article/view/16905 | https://ojs.aaai.org/index.php/AAAI/article/download/16905/16712 | [
"Xiaomeng Fan",
"Zhi Gao",
"Yuwei Wu",
"Yunde Jia",
"Mehrtash Harandi"
] | A principal way of addressing constrained optimization problems is to model them as problems on Riemannian manifolds. Recently, Riemannian meta-optimization provides a promising way for solving constrained optimization problems by learning optimizers on Riemannian manifolds in a data-driven fashion, making it possible ... | main | Machine Learning | 10.1609/aaai.v35i8.16905 | 35 | 8 | 7377-7384 | official | null | null |
10.1609/aaai.v35i8.16906 | Learning to Reweight with Deep Interactions | https://ojs.aaai.org/index.php/AAAI/article/view/16906 | https://ojs.aaai.org/index.php/AAAI/article/download/16906/16713 | [
"Yang Fan",
"Yingce Xia",
"Lijun Wu",
"Shufang Xie",
"Weiqing Liu",
"Jiang Bian",
"Tao Qin",
"Xiang-Yang Li"
] | Recently the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training d... | main | Machine Learning | 10.1609/aaai.v35i8.16906 | 35 | 8 | 7385-7393 | official | 2007.04649 | title_snapshot |
10.1609/aaai.v35i8.16907 | Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/16907 | https://ojs.aaai.org/index.php/AAAI/article/download/16907/16714 | [
"Amirreza Farnoosh",
"Bahar Azari",
"Sarah Ostadabbas"
] | We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dimensional data by a p... | main | Machine Learning | 10.1609/aaai.v35i8.16907 | 35 | 8 | 7394-7403 | official | 2009.05135 | title_snapshot |
10.1609/aaai.v35i8.16908 | UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/16908 | https://ojs.aaai.org/index.php/AAAI/article/download/16908/16715 | [
"Boyuan Feng",
"Yuke Wang",
"Yufei Ding"
] | With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs. However, unlike the conventional CNNs that have been extensively explored and exhaustively tested, people are still worrying about the GNNs' robustness under the critical s... | main | Machine Learning | 10.1609/aaai.v35i8.16908 | 35 | 8 | 7404-7412 | official | 2009.10235 | title_judge |
10.1609/aaai.v35i8.16909 | SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations | https://ojs.aaai.org/index.php/AAAI/article/view/16909 | https://ojs.aaai.org/index.php/AAAI/article/download/16909/16716 | [
"Hao-Zhe Feng",
"Kezhi Kong",
"Minghao Chen",
"Tianye Zhang",
"Minfeng Zhu",
"Wei Chen"
] | Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information dir... | main | Machine Learning | 10.1609/aaai.v35i8.16909 | 35 | 8 | 7413-7421 | official | 2011.10684 | title_snapshot |
10.1609/aaai.v35i8.16910 | Learning to Augment for Data-scarce Domain BERT Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/16910 | https://ojs.aaai.org/index.php/AAAI/article/download/16910/16717 | [
"Lingyun Feng",
"Minghui Qiu",
"Yaliang Li",
"Hai-Tao Zheng",
"Ying Shen"
] | Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (te... | main | Machine Learning | 10.1609/aaai.v35i8.16910 | 35 | 8 | 7422-7430 | official | 2101.08106 | title_snapshot |
10.1609/aaai.v35i8.16911 | Collaborative Group Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16911 | https://ojs.aaai.org/index.php/AAAI/article/download/16911/16718 | [
"Shaoxiong Feng",
"Hongshen Chen",
"Xuancheng Ren",
"Zhuoye Ding",
"Kan Li",
"Xu Sun"
] | Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Le... | main | Machine Learning | 10.1609/aaai.v35i8.16911 | 35 | 8 | 7431-7438 | official | 2009.07712 | title_snapshot |
10.1609/aaai.v35i8.16912 | Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression | https://ojs.aaai.org/index.php/AAAI/article/view/16912 | https://ojs.aaai.org/index.php/AAAI/article/download/16912/16719 | [
"Christian Fiedler",
"Carsten W. Scherer",
"Sebastian Trimpe"
] | Gaussian Process regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncer... | main | Machine Learning | 10.1609/aaai.v35i8.16912 | 35 | 8 | 7439-7447 | official | 2105.02796 | title_snapshot |
10.1609/aaai.v35i8.16913 | Few-Shot One-Class Classification via Meta-Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16913 | https://ojs.aaai.org/index.php/AAAI/article/download/16913/16720 | [
"Ahmed Frikha",
"Denis Krompaß",
"Hans-Georg Köpken",
"Volker Tresp"
] | Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strateg... | main | Machine Learning | 10.1609/aaai.v35i8.16913 | 35 | 8 | 7448-7456 | official | 2007.04146 | title_snapshot |
10.1609/aaai.v35i8.16914 | Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16914 | https://ojs.aaai.org/index.php/AAAI/article/download/16914/16721 | [
"Haotian Fu",
"Hongyao Tang",
"Jianye Hao",
"Chen Chen",
"Xidong Feng",
"Dong Li",
"Wulong Liu"
] | Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Meta-RL policies can easily generalize to new tasks within a few adaptation steps. We argue that improving the quality of context involves ans... | main | Machine Learning | 10.1609/aaai.v35i8.16914 | 35 | 8 | 7457-7465 | official | 2009.13891 | title_snapshot |
10.1609/aaai.v35i8.16915 | Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16915 | https://ojs.aaai.org/index.php/AAAI/article/download/16915/16722 | [
"Mengying Fu",
"Tianning Yuan",
"Fang Wan",
"Songcen Xu",
"Qixiang Ye"
] | In active learning, the ignorance of aligning unlabeled samples' distribution with that of labeled samples hinders the model trained upon labeled samples from selecting informative unlabeled samples. In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignme... | main | Machine Learning | 10.1609/aaai.v35i8.16915 | 35 | 8 | 7466-7473 | official | null | null |
10.1609/aaai.v35i8.16916 | Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances | https://ojs.aaai.org/index.php/AAAI/article/view/16916 | https://ojs.aaai.org/index.php/AAAI/article/download/16916/16723 | [
"Zhang-Hua Fu",
"Kai-Bin Qiu",
"Hongyuan Zha"
] | For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of ... | main | Machine Learning | 10.1609/aaai.v35i8.16916 | 35 | 8 | 7474-7482 | official | 2012.10658 | title_snapshot |
10.1609/aaai.v35i8.16883 | Generalized Adversarially Learned Inference | https://ojs.aaai.org/index.php/AAAI/article/view/16883 | https://ojs.aaai.org/index.php/AAAI/article/download/16883/16690 | [
"Yatin Dandi",
"Homanga Bharadhwaj",
"Abhishek Kumar",
"Piyush Rai"
] | Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to mat... | main | Machine Learning | 10.1609/aaai.v35i8.16883 | 35 | 8 | 7185-7192 | official | 2006.08089 | title_snapshot |
10.1609/aaai.v35i8.16884 | Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models | https://ojs.aaai.org/index.php/AAAI/article/view/16884 | https://ojs.aaai.org/index.php/AAAI/article/download/16884/16691 | [
"Antoine Dedieu",
"Miguel Lázaro-Gredilla",
"Dileep George"
] | We consider the problem of learning the underlying graph of a sparse Ising model with p nodes from n i.i.d. samples. The most recent and best performing approaches combine an empirical loss (the logistic regression loss or the interaction screening loss) with a regularizer (an L1 penalty or an L1 constraint). This resu... | main | Machine Learning | 10.1609/aaai.v35i8.16884 | 35 | 8 | 7193-7200 | official | 2012.01744 | title_snapshot |
10.1609/aaai.v35i8.16885 | Learning with Retrospection | https://ojs.aaai.org/index.php/AAAI/article/view/16885 | https://ojs.aaai.org/index.php/AAAI/article/download/16885/16692 | [
"Xiang Deng",
"Zhongfei Zhang"
] | Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An i... | main | Machine Learning | 10.1609/aaai.v35i8.16885 | 35 | 8 | 7201-7209 | official | 2012.13098 | title_snapshot |
10.1609/aaai.v35i8.16886 | Mercer Features for Efficient Combinatorial Bayesian Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/16886 | https://ojs.aaai.org/index.php/AAAI/article/download/16886/16693 | [
"Aryan Deshwal",
"Syrine Belakaria",
"Janardhan Rao Doppa"
] | Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting for combinatorial spaces (e.g., sequences and graphs) that occurs naturally in science and engineering applications. A prototypical example is... | main | Machine Learning | 10.1609/aaai.v35i8.16886 | 35 | 8 | 7210-7218 | official | 2012.07762 | title_snapshot |
10.1609/aaai.v35i8.16887 | Differentially Private and Communication Efficient Collaborative Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16887 | https://ojs.aaai.org/index.php/AAAI/article/download/16887/16694 | [
"Jiahao Ding",
"Guannan Liang",
"Jinbo Bi",
"Miao Pan"
] | Collaborative learning has received huge interests due to its capability of exploiting the collective computing power of the wireless edge devices. However, during the learning process, model updates using local private samples and large-scale parameter exchanges among agents impose severe privacy concerns and communic... | main | Machine Learning | 10.1609/aaai.v35i8.16887 | 35 | 8 | 7219-7227 | official | null | null |
10.1609/aaai.v35i8.16888 | Knowledge Refinery: Learning from Decoupled Label | https://ojs.aaai.org/index.php/AAAI/article/view/16888 | https://ojs.aaai.org/index.php/AAAI/article/download/16888/16695 | [
"Qianggang Ding",
"Sifan Wu",
"Tao Dai",
"Hao Sun",
"Jiadong Guo",
"Zhang-Hua Fu",
"Shutao Xia"
] | Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels while neglecting the rel... | main | Machine Learning | 10.1609/aaai.v35i8.16888 | 35 | 8 | 7228-7235 | official | null | null |
10.1609/aaai.v35i8.16889 | Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization | https://ojs.aaai.org/index.php/AAAI/article/view/16889 | https://ojs.aaai.org/index.php/AAAI/article/download/16889/16696 | [
"Kien Do",
"Truyen Tran",
"Svetha Venkatesh"
] | We propose two generic methods for improving semi-supervised learning (SSL). The first integrates weight perturbation (WP) into existing “consistency regularization” (CR) based methods. We implement WP by leveraging variational Bayesian inference (VBI). The second method proposes a novel consistency loss called “maximu... | main | Machine Learning | 10.1609/aaai.v35i8.16889 | 35 | 8 | 7236-7244 | official | 2012.01793 | title_snapshot |
10.1609/aaai.v35i8.16890 | Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences | https://ojs.aaai.org/index.php/AAAI/article/view/16890 | https://ojs.aaai.org/index.php/AAAI/article/download/16890/16697 | [
"Andis Draguns",
"Emīls Ozoliņš",
"Agris Šostaks",
"Matīss Apinis",
"Karlis Freivalds"
] | Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuffle-Exchange network offers a computation-efficient alternative, enabling the modelling of long-range dependencies in O(n log n) time. The mo... | main | Machine Learning | 10.1609/aaai.v35i8.16890 | 35 | 8 | 7245-7253 | official | 2004.04662 | title_snapshot |
10.1609/aaai.v35i8.16891 | A One-Size-Fits-All Solution to Conservative Bandit Problems | https://ojs.aaai.org/index.php/AAAI/article/view/16891 | https://ojs.aaai.org/index.php/AAAI/article/download/16891/16698 | [
"Yihan Du",
"Siwei Wang",
"Longbo Huang"
] | In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a given baseline at any time. We propose a One-Size-Fits-All solution to CBPs and present its applications to three encompassed problems, i.e. c... | main | Machine Learning | 10.1609/aaai.v35i8.16891 | 35 | 8 | 7254-7261 | official | 2012.07341 | title_snapshot |
10.1609/aaai.v35i8.16892 | Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/16892 | https://ojs.aaai.org/index.php/AAAI/article/download/16892/16699 | [
"Yihan Du",
"Yuko Kuroki",
"Wei Chen"
] | In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space X \subseteq {0,1}^d, and in each round the learner pulls an action x \in X and receives a random reward with expectation x^T \theta, with \theta \in \R^d ... | main | Machine Learning | 10.1609/aaai.v35i8.16892 | 35 | 8 | 7262-7270 | official | 2006.07905 | title_snapshot |
10.1609/aaai.v35i8.16893 | Knowledge Refactoring for Inductive Program Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/16893 | https://ojs.aaai.org/index.php/AAAI/article/download/16893/16700 | [
"Sebastijan Dumancic",
"Tias Guns",
"Andrew Cropper"
] | Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redund... | main | Machine Learning | 10.1609/aaai.v35i8.16893 | 35 | 8 | 7271-7278 | official | 2004.09931 | title_snapshot |
10.1609/aaai.v35i8.16894 | Semi-Supervised Metric Learning: A Deep Resurrection | https://ojs.aaai.org/index.php/AAAI/article/view/16894 | https://ojs.aaai.org/index.php/AAAI/article/download/16894/16701 | [
"Ujjal Kr Dutta",
"Mehrtash Harandi",
"C Chandra Shekhar"
] | Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are closer, and dissimilar examples are apart. In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples. SSDM... | main | Machine Learning | 10.1609/aaai.v35i8.16894 | 35 | 8 | 7279-7287 | official | 2105.05061 | title_snapshot |
10.1609/aaai.v35i8.16895 | Reinforcement Learning with Trajectory Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/16895 | https://ojs.aaai.org/index.php/AAAI/article/download/16895/16702 | [
"Yonathan Efroni",
"Nadav Merlis",
"Shie Mannor"
] | The standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair. However, in practice, it is often the case that such frequent feedback is not available. In this work, we take a first step towards relaxing this assumption and require a weaker form of feedback, whic... | main | Machine Learning | 10.1609/aaai.v35i8.16895 | 35 | 8 | 7288-7295 | official | 2008.06036 | title_snapshot |
10.1609/aaai.v35i8.16896 | The Parameterized Complexity of Clustering Incomplete Data | https://ojs.aaai.org/index.php/AAAI/article/view/16896 | https://ojs.aaai.org/index.php/AAAI/article/download/16896/16703 | [
"Eduard Eiben",
"Robert Ganian",
"Iyad Kanj",
"Sebastian Ordyniak",
"Stefan Szeider"
] | We study fundamental clustering problems for incomplete data. Specifically, given a set of incomplete d-dimensional vectors (representing rows of a matrix), the goal is to complete the missing vector entries in a way that admits a partitioning of the vectors into at most k clusters with radius or diameter at most r. We... | main | Machine Learning | 10.1609/aaai.v35i8.16896 | 35 | 8 | 7296-7304 | official | 1911.01465 | title_snapshot |
10.1609/aaai.v35i8.16897 | Learning Prediction Intervals for Model Performance | https://ojs.aaai.org/index.php/AAAI/article/view/16897 | https://ojs.aaai.org/index.php/AAAI/article/download/16897/16704 | [
"Benjamin Elder",
"Matthew Arnold",
"Anupama Murthi",
"Jiří Navrátil"
] | Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using test sets or periodic manual quality assessments, both of which require laborious manual data labeling. Automated performance prediction techn... | main | Machine Learning | 10.1609/aaai.v35i8.16897 | 35 | 8 | 7305-7313 | official | 2012.08625 | title_snapshot |
10.1609/aaai.v35i8.16898 | Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities | https://ojs.aaai.org/index.php/AAAI/article/view/16898 | https://ojs.aaai.org/index.php/AAAI/article/download/16898/16705 | [
"Alina Ene",
"Huy L. Nguyen",
"Adrian Vladu"
] | We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense that they achieve nearly-optimal convergence rates for both smooth and non-smooth functions, even when they only have access to stochastic gr... | main | Machine Learning | 10.1609/aaai.v35i8.16898 | 35 | 8 | 7314-7321 | official | 2007.08840 | title_snapshot |
10.1609/aaai.v35i8.16899 | Projection-Free Bandit Optimization with Privacy Guarantees | https://ojs.aaai.org/index.php/AAAI/article/view/16899 | https://ojs.aaai.org/index.php/AAAI/article/download/16899/16706 | [
"Alina Ene",
"Huy L. Nguyen",
"Adrian Vladu"
] | We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a complex geometry, and access to it is done efficiently only through a linear optimization oracle, hence Euclidean projections are unavailable (... | main | Machine Learning | 10.1609/aaai.v35i8.16899 | 35 | 8 | 7322-7330 | official | 2012.12138 | title_snapshot |
10.1609/aaai.v35i8.16900 | Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems | https://ojs.aaai.org/index.php/AAAI/article/view/16900 | https://ojs.aaai.org/index.php/AAAI/article/download/16900/16707 | [
"Shohei Enomoro",
"Takeharu Eda"
] | Recently, deep neural networks have become to be used in a variety of applications. While the accuracy of deep neural networks is increasing, the confidence score, which indicates the reliability of the prediction results, is becoming more important. Deep neural networks are seen as highly accurate but known to be over... | main | Machine Learning | 10.1609/aaai.v35i8.16900 | 35 | 8 | 7331-7339 | official | 2104.09286 | title_snapshot |
10.1609/aaai.v35i8.16901 | Regret Bounds for Batched Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/16901 | https://ojs.aaai.org/index.php/AAAI/article/download/16901/16708 | [
"Hossein Esfandiari",
"Amin Karbasi",
"Abbas Mehrabian",
"Vahab Mirrokni"
] | We present simple algorithms for batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets that improve and extend the best known regret bounds of Gao, Han, Ren, and Zhou (NeurIPS 2019), for any number of batches. In particular, our algorithms in bot... | main | Machine Learning | 10.1609/aaai.v35i8.16901 | 35 | 8 | 7340-7348 | official | 1910.04959 | title_snapshot |
10.1609/aaai.v35i8.16902 | Almost Linear Time Density Level Set Estimation via DBSCAN | https://ojs.aaai.org/index.php/AAAI/article/view/16902 | https://ojs.aaai.org/index.php/AAAI/article/download/16902/16709 | [
"Hossein Esfandiari",
"Vahab Mirrokni",
"Peilin Zhong"
] | In this work we focus on designing a fast algorithm for lambda-density level set estimation via DBSCAN clustering. Previous work (Jiang ICML’17, and Jang and Jiang ICML’19) shows that under some natural assumptions DBSCAN and its variant DBSCAN++ can be used to estimate the lambda-density level set with near-optimal Ha... | main | Machine Learning | 10.1609/aaai.v35i8.16902 | 35 | 8 | 7349-7357 | official | null | null |
10.1609/aaai.v35i8.16863 | Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16863 | https://ojs.aaai.org/index.php/AAAI/article/download/16863/16670 | [
"Chao Chen",
"Dongsheng Li",
"Junchi Yan",
"Hanchi Huang",
"Xiaokang Yang"
] | One-bit matrix completion is an important class of positive-unlabeled (PU) learning problems where the observations consist of only positive examples, e.g., in top-N recommender systems. For the first time, we show that 1-bit matrix completion can be formulated as the problem of recovering clean graph signals from nois... | main | Machine Learning | 10.1609/aaai.v35i8.16863 | 35 | 8 | 7011-7019 | official | null | null |
10.1609/aaai.v35i8.16864 | Addressing Action Oscillations through Learning Policy Inertia | https://ojs.aaai.org/index.php/AAAI/article/view/16864 | https://ojs.aaai.org/index.php/AAAI/article/download/16864/16671 | [
"Chen Chen",
"Hongyao Tang",
"Jianye Hao",
"Wulong Liu",
"Zhaopeng Meng"
] | Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective on a wide range of challenging decision making and control tasks. However, these methods typically suffer from severe action oscillations in particular in discrete action setting, which means that agents select different actions within ... | main | Machine Learning | 10.1609/aaai.v35i8.16864 | 35 | 8 | 7020-7027 | official | 2103.02287 | title_snapshot |
10.1609/aaai.v35i8.16865 | Cross-Layer Distillation with Semantic Calibration | https://ojs.aaai.org/index.php/AAAI/article/view/16865 | https://ojs.aaai.org/index.php/AAAI/article/download/16865/16672 | [
"Defang Chen",
"Jian-Ping Mei",
"Yuan Zhang",
"Can Wang",
"Zhe Wang",
"Yan Feng",
"Chun Chen"
] | Recently proposed knowledge distillation approaches based on feature-map transfer validate that intermediate layers of a teacher model can serve as effective targets for training a student model to obtain better generalization ability. Existing studies mainly focus on particular representation forms for knowledge trans... | main | Machine Learning | 10.1609/aaai.v35i8.16865 | 35 | 8 | 7028-7036 | official | 2012.03236 | title_snapshot |
10.1609/aaai.v35i8.16866 | Distributed Ranking with Communications: Approximation Analysis and Applications | https://ojs.aaai.org/index.php/AAAI/article/view/16866 | https://ojs.aaai.org/index.php/AAAI/article/download/16866/16673 | [
"Hong Chen",
"Yingjie Wang",
"Yulong Wang",
"Feng Zheng"
] | Learning theory of distributed algorithms has recently attracted enormous attention in the machine learning community. However, most of existing works focus on learning problem with pointwise loss and does not consider the communication among local processors. In this paper, we propose a new distributed pairwise rankin... | main | Machine Learning | 10.1609/aaai.v35i8.16866 | 35 | 8 | 7037-7045 | official | null | null |
10.1609/aaai.v35i8.16867 | THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation | https://ojs.aaai.org/index.php/AAAI/article/view/16867 | https://ojs.aaai.org/index.php/AAAI/article/download/16867/16674 | [
"Mengyun Chen",
"Kaixin Gao",
"Xiaolei Liu",
"Zidong Wang",
"Ningxi Ni",
"Qian Zhang",
"Lei Chen",
"Chao Ding",
"Zhenghai Huang",
"Min Wang",
"Shuangling Wang",
"Fan Yu",
"Xinyuan Zhao",
"Dachuan Xu"
] | It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order matrix. Inspired by... | main | Machine Learning | 10.1609/aaai.v35i8.16867 | 35 | 8 | 7046-7054 | official | null | null |
10.1609/aaai.v35i8.16868 | Neural Relational Inference with Efficient Message Passing Mechanisms | https://ojs.aaai.org/index.php/AAAI/article/view/16868 | https://ojs.aaai.org/index.php/AAAI/article/download/16868/16675 | [
"Siyuan Chen",
"Jiahai Wang",
"Guoqing Li"
] | Many complex processes can be viewed as dynamical systems of interacting agents. In many cases, only the state sequences of individual agents are observed, while the interacting relations and the dynamical rules are unknown. The neural relational inference (NRI) model adopts graph neural networks that pass messages ove... | main | Machine Learning | 10.1609/aaai.v35i8.16868 | 35 | 8 | 7055-7063 | official | 2101.09486 | title_snapshot |
10.1609/aaai.v35i8.16869 | Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16869 | https://ojs.aaai.org/index.php/AAAI/article/download/16869/16676 | [
"Xin Chen",
"Lingxi Xie",
"Jun Wu",
"Longhui Wei",
"Yuhui Xu",
"Qi Tian"
] | Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. The... | main | Machine Learning | 10.1609/aaai.v35i8.16869 | 35 | 8 | 7064-7072 | official | 2004.08423 | title_snapshot |
10.1609/aaai.v35i8.16870 | Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information | https://ojs.aaai.org/index.php/AAAI/article/view/16870 | https://ojs.aaai.org/index.php/AAAI/article/download/16870/16677 | [
"Yi Chen",
"Hong Qu",
"Malu Zhang",
"Yuchen Wang"
] | Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respect... | main | Machine Learning | 10.1609/aaai.v35i8.16870 | 35 | 8 | 7073-7080 | official | null | null |
10.1609/aaai.v35i8.16871 | HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16871 | https://ojs.aaai.org/index.php/AAAI/article/download/16871/16678 | [
"Yuanyuan Chen",
"Boyang Li",
"Han Yu",
"Pengcheng Wu",
"Chunyan Miao"
] | The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HyDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around th... | main | Machine Learning | 10.1609/aaai.v35i8.16871 | 35 | 8 | 7081-7089 | official | 2102.02515 | title_snapshot |
10.1609/aaai.v35i8.16872 | NASGEM: Neural Architecture Search via Graph Embedding Method | https://ojs.aaai.org/index.php/AAAI/article/view/16872 | https://ojs.aaai.org/index.php/AAAI/article/download/16872/16679 | [
"Hsin-Pai Cheng",
"Tunhou Zhang",
"Yixing Zhang",
"Shiyu Li",
"Feng Liang",
"Feng Yan",
"Meng Li",
"Vikas Chandra",
"Hai Li",
"Yiran Chen"
] | Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a lat... | main | Machine Learning | 10.1609/aaai.v35i8.16872 | 35 | 8 | 7090-7098 | official | 2007.04452 | title_snapshot |
10.1609/aaai.v35i8.16873 | Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16873 | https://ojs.aaai.org/index.php/AAAI/article/download/16873/16680 | [
"Li Cheng",
"Yijie Wang",
"Xinwang Liu"
] | Multi-view outlier detection recently attracted rapidly growing attention with the development of multi-view learning. Although promising performance demonstrated, we observe that identifying outliers in multi-view data is still a challenging task due to the complicated characteristics of multi-view data. Specifically,... | main | Machine Learning | 10.1609/aaai.v35i8.16873 | 35 | 8 | 7099-7106 | official | null | null |
10.1609/aaai.v35i8.16874 | Self-Progressing Robust Training | https://ojs.aaai.org/index.php/AAAI/article/view/16874 | https://ojs.aaai.org/index.php/AAAI/article/download/16874/16681 | [
"Minhao Cheng",
"Pin-Yu Chen",
"Sijia Liu",
"Shiyu Chang",
"Cho-Jui Hsieh",
"Payel Das"
] | Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods such as adversarial training explicitly uses an ``attack'' (e.g., l_infty-norm bounded perturbation) to generate adversarial examples during... | main | Machine Learning | 10.1609/aaai.v35i8.16874 | 35 | 8 | 7107-7115 | official | 2012.11769 | title_snapshot |
10.1609/aaai.v35i8.16875 | Continuous-Time Attention for Sequential Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16875 | https://ojs.aaai.org/index.php/AAAI/article/download/16875/16682 | [
"Jen-Tzung Chien",
"Yi-Hsiang Chen"
] | Attention mechanism is crucial for sequential learning where a wide range of applications have been successfully developed. This mechanism is basically trained to spotlight on the region of interest in hidden states of sequence data. Most of the attention methods compute the attention score through relating between a q... | main | Machine Learning | 10.1609/aaai.v35i8.16875 | 35 | 8 | 7116-7124 | official | null | null |
10.1609/aaai.v35i8.16876 | Transfer Learning for Efficient Iterative Safety Validation | https://ojs.aaai.org/index.php/AAAI/article/view/16876 | https://ojs.aaai.org/index.php/AAAI/article/download/16876/16683 | [
"Anthony Corso",
"Mykel J. Kochenderfer"
] | Safety validation is important during the development of safety-critical autonomous systems but can require significant computational effort. Existing algorithms often start from scratch each time the system under test changes. We apply transfer learning to improve the efficiency of reinforcement learning based safety ... | main | Machine Learning | 10.1609/aaai.v35i8.16876 | 35 | 8 | 7125-7132 | official | 2012.05336 | title_snapshot |
10.1609/aaai.v35i8.16877 | Computationally Tractable Riemannian Manifolds for Graph Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/16877 | https://ojs.aaai.org/index.php/AAAI/article/download/16877/16684 | [
"Calin Cruceru",
"Gary Becigneul",
"Octavian-Eugen Ganea"
] | Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases (e.g., hierarchical structures benefit from hyperbolic geometry). However, going beyond embedding spaces of constant sectional curvatur... | main | Machine Learning | 10.1609/aaai.v35i8.16877 | 35 | 8 | 7133-7141 | official | 2002.08665 | title_snapshot |
10.1609/aaai.v35i8.16878 | Cost-aware Graph Generation: A Deep Bayesian Optimization Approach | https://ojs.aaai.org/index.php/AAAI/article/view/16878 | https://ojs.aaai.org/index.php/AAAI/article/download/16878/16685 | [
"Jiaxu Cui",
"Bo Yang",
"Bingyi Sun",
"Jiming Liu"
] | Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e.g., drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottl... | main | Machine Learning | 10.1609/aaai.v35i8.16878 | 35 | 8 | 7142-7150 | official | null | null |
10.1609/aaai.v35i8.16879 | Type-augmented Relation Prediction in Knowledge Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/16879 | https://ojs.aaai.org/index.php/AAAI/article/download/16879/16686 | [
"Zijun Cui",
"Pavan Kapanipathi",
"Kartik Talamadupula",
"Tian Gao",
"Qiang Ji"
] | Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entities. Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones. Most of the ... | main | Machine Learning | 10.1609/aaai.v35i8.16879 | 35 | 8 | 7151-7159 | official | 2009.07938 | title_snapshot |
10.1609/aaai.v35i8.16880 | The Value-Improvement Path: Towards Better Representations for Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16880 | https://ojs.aaai.org/index.php/AAAI/article/download/16880/16687 | [
"Will Dabney",
"André Barreto",
"Mark Rowland",
"Robert Dadashi",
"John Quan",
"Marc G. Bellemare",
"David Silver"
] | In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a single, stationary, approximation problem, but a sequence of value prediction problems. Each time the policy improves, the nature of the problem changes, shifting both the distribution of states and their values. In this pa... | main | Machine Learning | 10.1609/aaai.v35i8.16880 | 35 | 8 | 7160-7168 | official | 2006.02243 | title_snapshot |
10.1609/aaai.v35i8.16881 | Loop Estimator for Discounted Values in Markov Reward Processes | https://ojs.aaai.org/index.php/AAAI/article/view/16881 | https://ojs.aaai.org/index.php/AAAI/article/download/16881/16688 | [
"Falcon Z. Dai",
"Matthew R. Walter"
] | At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and... | main | Machine Learning | 10.1609/aaai.v35i8.16881 | 35 | 8 | 7169-7175 | official | 2002.06299 | title_snapshot |
10.1609/aaai.v35i8.16882 | Differentially Private Stochastic Coordinate Descent | https://ojs.aaai.org/index.php/AAAI/article/view/16882 | https://ojs.aaai.org/index.php/AAAI/article/download/16882/16689 | [
"Georgios Damaskinos",
"Celestine Mendler-Dünner",
"Rachid Guerraoui",
"Nikolaos Papandreou",
"Thomas Parnell"
] | In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this vector suffices to hide critical information about individuals, st... | main | Machine Learning | 10.1609/aaai.v35i8.16882 | 35 | 8 | 7176-7184 | official | 2006.07272 | title_snapshot |
10.1609/aaai.v35i8.16843 | Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness | https://ojs.aaai.org/index.php/AAAI/article/view/16843 | https://ojs.aaai.org/index.php/AAAI/article/download/16843/16650 | [
"Anh Tuan Bui",
"Trung Le",
"He Zhao",
"Paul Montague",
"Olivier DeVel",
"Tamas Abraham",
"Dinh Phung"
] | Ensemble-based Adversarial Training is a principled approach to achieve robustness against adversarial attacks. An important technicality of this approach is to control the transferability of adversarial examples between ensemble members. We propose in this work a simple, but effective strategy to collaborate among com... | main | Machine Learning | 10.1609/aaai.v35i8.16843 | 35 | 8 | 6831-6839 | official | 2009.09612 | title_snapshot |
10.1609/aaai.v35i8.16844 | Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently | https://ojs.aaai.org/index.php/AAAI/article/view/16844 | https://ojs.aaai.org/index.php/AAAI/article/download/16844/16651 | [
"Rebekka Burkholz",
"John Quackenbush"
] | Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or... | main | Machine Learning | 10.1609/aaai.v35i8.16844 | 35 | 8 | 6840-6849 | official | 1909.05416 | title_snapshot |
10.1609/aaai.v35i8.16845 | Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16845 | https://ojs.aaai.org/index.php/AAAI/article/download/16845/16652 | [
"Bowen Cai",
"Huan Fu",
"Rongfei Jia",
"Binqiang Zhao",
"Hua Li",
"Yinghui Xu"
] | Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods is unsatisfactory on real datasets where the new target domain comprises of heterogeneous sub-domains (e.g. diverse weather characteristics)... | main | Machine Learning | 10.1609/aaai.v35i8.16845 | 35 | 8 | 6850-6858 | official | 2012.05608 | title_snapshot |
10.1609/aaai.v35i8.16846 | Time Series Domain Adaptation via Sparse Associative Structure Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16846 | https://ojs.aaai.org/index.php/AAAI/article/download/16846/16653 | [
"Ruichu Cai",
"Jiawei Chen",
"Zijian Li",
"Wei Chen",
"Keli Zhang",
"Junjian Ye",
"Zhuozhang Li",
"Xiaoyan Yang",
"Zhenjie Zhang"
] | Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task f... | main | Machine Learning | 10.1609/aaai.v35i8.16846 | 35 | 8 | 6859-6867 | official | 2012.11797 | title_snapshot |
10.1609/aaai.v35i8.16847 | A Blind Block Term Decomposition of High Order Tensors | https://ojs.aaai.org/index.php/AAAI/article/view/16847 | https://ojs.aaai.org/index.php/AAAI/article/download/16847/16654 | [
"Yunfeng Cai",
"Ping Li"
] | Tensor decompositions have found many applications in signal processing, data mining, machine learning, etc. In particular, the block term decomposition (BTD), which is a generalization of CP decomposition and Tucker decomposition/HOSVD, has been successfully used for the compression and acceleration of neural networks... | main | Machine Learning | 10.1609/aaai.v35i8.16847 | 35 | 8 | 6868-6876 | official | null | null |
10.1609/aaai.v35i8.16848 | Open-Set Recognition with Gaussian Mixture Variational Autoencoders | https://ojs.aaai.org/index.php/AAAI/article/view/16848 | https://ojs.aaai.org/index.php/AAAI/article/download/16848/16655 | [
"Alexander Cao",
"Yuan Luo",
"Diego Klabjan"
] | In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability t... | main | Machine Learning | 10.1609/aaai.v35i8.16848 | 35 | 8 | 6877-6884 | official | 2006.02003 | title_snapshot |
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