OpenRaiser/Intern-Atlas huggingface dataset:
Uncertainty sampling and transductive experimental design for active dual supervision
Authors: Vikas Sindhwani, Prem Melville, Richard D. Lawrence
Venue: ICML
Year: 2009

Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text classification where it is frequently possible to provide domain knowledge in the form of words that associate strongly with a class. In this paper, we consider the novel problem of active dual supervision, or, how to optimally query an example and feature labeling oracle to simultaneously collect two different forms of supervision, with the objective of building the best classifier in the most cost effective manner. We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models. Empirical studies confirm the potential of these schemes to significantly reduce the cost of acquiring labeled data for training high-quality models.
Multi-assignment clustering for Boolean data
Authors: Andreas P. Streich, Mario Frank 0001, David A. Basin, Joachim M. Buhmann
Venue: ICML
Year: 2009

Conventional clustering methods typically assume that each data item belongs to a single cluster. This assumption does not hold in general. In order to overcome this limitation, we propose a generative method for clustering vectorial data, where each object can be assigned to multiple clusters. Using a deterministic annealing scheme, our method decomposes the observed data into the contributions of individual clusters and infers their parameters. Experiments on synthetic Boolean data show that our method achieves higher accuracy in the source parameter estimation and superior cluster stability compared to state-of-the-art approaches. We also apply our method to an important problem in computer security known as role mining. Experiments on real-world access control data show performance gains in generalization to new employees against other multi-assignment methods. In challenging situations with high noise levels, our approach maintains its good performance, while alternative state-of-the-art techniques lack robustness.
A simpler unified analysis of budget perceptrons
Authors: Ilya Sutskever
Venue: ICML
Year: 2009

The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing if the number of errors is large. The Forgetron and the Randomized Budget Perceptron algorithms overcome this problem by restricting the number of support vectors the Perceptron is allowed to have. These algorithms have regret bounds whose proofs are dissimilar. In this paper we propose a unified analysis of both of these algorithms by observing that the way in which they remove support vectors can be seen as types of L2-regularization. By casting these algorithms as instances of online convex optimization problems and applying a variant of Zinkevich’s theorem for noisy and incorrect gradient, we can bound the regret of these algorithms more easily than before. Our bounds are similar to the existing ones, but the proofs are less technical. 1.
Fast gradient-descent methods for temporal-difference learning with linear function approximation
Authors: Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver 0001, Csaba Szepesvári, Eric Wiewiora
Venue: ICML
Year: 2009

Sutton, Szepesvári and Maei (2009) recently introduced the first temporal-difference learning algorithm compatible with both linear function approximation and off-policy training, and whose complexity scales only linearly in the size of the function approximator. Although their gradient temporal difference (GTD) algorithm converges reliably, it can be very slow compared to conventional linear TD (on on-policy problems where TD is convergent), calling into question its practical utility. In this paper we introduce two new related algorithms with better convergence rates. The first algorithm, GTD2, is derived and proved convergent just as GTD was, but uses a different objective function and converges significantly faster (but still not as fast as conventional TD). The second new algorithm, linear TD with gradient correction, or TDC, uses the same update rule as conventional TD except for an additional term which is initially zero. In our experiments on small test problems and in a Computer Go application with a million features, the learning rate of this algorithm was comparable to that of conventional TD. This algorithm appears to extend linear TD to off-policy learning with no penalty in performance while only doubling computational requirements.
Kernelized value function approximation for reinforcement learning
Authors: Gavin Taylor, Ronald Parr
Venue: ICML
Year: 2009

A recent surge in research in kernelized approaches to reinforcement learning has sought to bring the benefits of kernelized machine learning techniques to reinforcement learning. Kernelized reinforcement learning techniques are fairly new and different authors have approached the topic with different assumptions and goals. Neither a unifying view nor an understanding of the pros and cons of different approaches has yet emerged. In this paper, we offer a unifying view of the different approaches to kernelized value function approximation for reinforcement learning. We show that, except for different approaches to regularization, Kernelized LSTD (KLSTD) is equivalent to a modelbased approach that uses kernelized regression to find an approximate reward and transition model, and that Gaussian Process Temporal Difference learning (GPTD) returns a mean value function that is equivalent to these other approaches. We also discuss the relationship between our modelbased approach and the earlier Gaussian Processes in Reinforcement Learning (GPRL). Finally, we decompose the Bellman error into the sum of transition error and reward error terms, and demonstrate through experiments that this decomposition can be helpful in choosing regularization parameters.
More generality in efficient multiple kernel learning
Authors: Manik Varma, Bodla Rakesh Babu
Venue: ICML
Year: 2009

Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels.
K-means in space: a radiation sensitivity evaluation
Authors: Kiri L. Wagstaff, Benjamin J. Bornstein
Venue: ICML
Year: 2009

Spacecraft increasingly employ onboard data analysis to inform further data collection and prioritization decisions. However, many spacecraft operate in high-radiation environments in which the reliability of dataintensive computation is not known. This paper presents the first study of radiation sensitivity for k-means clustering. Our key findings are 1) k-means data structures differ in sensitivity, which is not determined solely by the amount of memory exposed; 2) no special radiation protection is needed below a data-set-dependent radiation threshold, enabling the use of faster, smaller, and cheaper onboard memory; and 3) subsampling improves radiation tolerance slightly, but the use of kd-trees unfortunately reduces tolerance. Our conclusions can help tailor k-means for use in future high-radiation environments.
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Authors: Linli Xu, Martha White, Dale Schuurmans
Venue: ICML
Year: 2009

Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unification of several supervised and unsupervised training principles through the concept of optimal reverse prediction: predict the inputs from the target labels, optimizing both over model parameters and any missing labels. In particular, we show how supervised least squares, principal components analysis, k-means clustering and normalized graph-cut can all be expressed as instances of the same training principle. Natural forms of semi-supervised regression and classification are then automatically derived, yielding semi-supervised learning algorithms for regression and classification that, surprisingly, are novel and refine the state of the art. These algorithms can all be combined with standard regularizers and made non-linear via kernels.
Robust feature extraction via information theoretic learning
Authors: Xiaotong Yuan, Bao-Gang Hu
Venue: ICML
Year: 2009

In this paper, we present a robust feature extraction framework based on information-theoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi's quadratic information potential of features and the Renyi's cross information potential between features and class labels. This objective function reaps the advantages in robustness from both redescending M-estimator and manifold regularization, and can be efficiently optimized via half-quadratic optimization in an iterative manner. In addition, the popular algorithms LPP, SRDA and LapRLS for feature extraction are all justified to be the special cases within this framework. Extensive comparison experiments on several real-world data sets, with contaminated features or labels, well validate the encouraging gain in algorithmic robustness from this proposed framework.
Learning instance specific distances using metric propagation
Authors: De-Chuan Zhan, Ming Li 0005, Yufeng Li 0008, Zhi-Hua Zhou
Venue: ICML
Year: 2009

In many real-world applications, such as image retrieval, it would be natural to measure the distances from one instance to others using instance specific distance which captures the distinctions from the perspective of the concerned instance. However, there is no complete framework for learning instance specific distances since existing methods are incapable of learning such distances for test instance and unlabeled data. In this paper, we propose the Isd method to address this issue. The key of Isd is metric propagation, that is, propagating and adapting metrics of individual labeled examples to individual unlabeled instances. We formulate the problem into a convex optimization framework and derive efficient solutions. Experiments show that Isd can effectively learn instance specific distances for labeled as well as unlabeled instances. The metric propagation scheme can also be used in other scenarios.
MedLDA: maximum margin supervised topic models for regression and classification
Authors: Jun Zhu 0001, Amr Ahmed 0001, Eric P. Xing
Venue: ICML
Year: 2009

Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.
On primal and dual sparsity of Markov networks
Authors: Jun Zhu 0001, Eric P. Xing
Venue: ICML
Year: 2009

Sparsity is a desirable property in high dimensional learning. The l1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining these two methods, an l1-norm max-margin Markov network (l1-M3N) can achieve both types of sparsity. This paper analyzes its connections to the Laplace max-margin Markov network (LapM3N), which inherits the dual sparsity of max-margin models but is pseudo-primal sparse, and to a novel adaptive M3N (AdapM3N). We show that the l1-M3N is an extreme case of the LapM3N, and the l1-M3N is equivalent to an AdapM3N. Based on this equivalence we develop a robust EM-style algorithm for learning an l1-M3N. We demonstrate the advantages of the simultaneously (pseudo-) primal and dual sparse models over the ones which enjoy either primal or dual sparsity on both synthetic and real data sets.
SimpleNPKL: simple non-parametric kernel learning
Authors: Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Venue: ICML
Year: 2009

Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such intensive computation cost prohibits NPK learning applicable to real applications, even for data sets of moderate size. In this paper, we propose an efficient approach to NPK learning from side information, referred to as SimpleNPKL, which can efficiently learn non-parametric kernels from large sets of pairwise constraints. In particular, we show that the proposed SimpleNPKL with linear loss has a closed-form solution that can be simply computed by the Lanczos algorithm. Moreover, we show that the SimpleNPKL with square hinge loss can be re-formulated as a saddle-point optimization task, which can be further solved by a fast iterative algorithm. In contrast to the previous approaches, our empirical results show that our new technique achieves the same accuracy, but is significantly more efficient and scalable.
A Distributed Cooperative Agents Architecture for Software Development
Authors: Jeffrey J. P. Tsai, Ray-Yuan Sheu
Venue: IJCAI
Year: 1991

This paper presents a distributed cooperative agents architecture for software development using a frame-and-rule oriented requirements language (FRORL). FRORL is a logic-based requirements specification language and serves as multiple roles for representing knowledge, for interface language between cooperative agents and knowledge and data bases, and for knowledge communications among distributed cooperative intelligent agents. The distributed cooperative agents environment includes a frame-and-rule oriented requirements specifications language, an object-oriented specification acquisition methodology, the knowledge bases for software development using the paradigm of intelligent and cooperative information systems, and intelligent agents for all phases of software life cycle. All the software agents for software development are either logically or geographically distributed over network environment. The specifications acquisition methodology is used to guide software developers in performing object-oriented modeling of application problem domains. The system is designed to support rapid prototyping for validation of specifications in multiple abstraction layers through frame hierarchies. The specifications represented in FRORL can be transformed into various kinds of traditional or object-oriented programming languages.
Learning information extraction patterns from examples
Authors: Scott B. Huffman
Venue: IJCAI
Year: 1995

A growing population of users want to extract a growing variety of information from on-line texts. Unfortunately, current information extraction systems typically require experts to hand-build dictionaries of extraction patterns for each new type of information to be extracted. This paper presents a system that can learn dictionaries of extraction patterns directly from user-provided examples of texts and events to be extracted from them. The system, called LIEP, learns patterns that recognize relationships between key constituents based on local syntax. Sets of patterns learned by LIEP for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns. 1 Introduction Although significant progress has been made on information extraction systems in recent years (for instance through the MUC conferences [MUC, 1992; MUC, 1993]), coding the knowledge these systems need to extract new kinds of information and events is an arduous and time-consuming process [Ril...
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
Authors: Gary Geunbae Lee, Jong-Hyeok Lee
Venue: IJCAI
Year: 1995

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for novel processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, emphasizing that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/ syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. Phoneme recognition, speech morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for spoken Korean processing.
The Traits of the Personable
Authors: Naren Ramakrishnan
Venue: IJCAI
Year: 2003

Information personalization is fertile ground for application of AI techniques. In this article I relate personalization to the ability to capture partial information in an information-seeking interaction. The specific focus is on personalizing interactions at web sites. Using ideas from partial evaluation and explanation-based generalization, I present a modeling methodology for reasoning about personalization. This approach helps identify seven tiers of ‘personable traits' in web sites.
An Interactive, Generative Punch and Judy Show Using Institutions, ASP and Emotional Agents
Authors: Matthew Thompson 0001, Julian A. Padget, Steve Battle
Venue: IJCAI
Year: 2015

Using Punch and Judy as a story domain, we describe an interactive puppet show, where the flow and content of the story can be influenced by the actions of the audience. As the puppet show is acted out, the audience reacts to events by cheering or booing the characters. This affects the agents' emotional state, potentially causing them to change their actions, altering the course of the narrative. An institutional normative model is used to constrain the narrative so that it remains consistent with the Punch and Judy canon. Through this vignette of a sociotechnical system (STS), comprising human and software actors, an institutional model - derived from narrative theory - and (simplistic) technological interaction artifacts, we begin to be able to explore some of the issues that can arise in STS through the prism of the World-Institution-Technology (WIT) model.
Quantified Degrees of Group Responsibility
Authors: Vahid Yazdanpanah, Mehdi Dastani
Venue: IJCAI
Year: 2015

This paper builds on an existing notion of group responsibility and proposes two ways to define the degree of group responsibility: structural and functional degrees of responsibility. These notions measure the potential responsibilities of (agent) groups for avoiding a state of affairs. According to these notions, a degree of responsibility for a state of affairs can be assigned to a group of agents if, and to the extent that, the group has the potential to preclude the state of affairs.
User Profiling in Personalization Applications Through Rule Discovery and Validation
Authors: Gediminas Adomavicius, Alexander Tuzhilin
Venue: KDD
Year: 1999

Gediminas Adomavicius New York University adomavic@cs.nyu.edu In many applications, ranging from recommender systems to one-to-one marketing to Web browsing, it is important to build personalized profiles of individual users from their transactional histories. These profiles describe individual behavior of users and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate customer profiles by separating “good” rules from the “bad.” This paper presents a method for validating such rules with an explicit participation of a human expert
On the Merits of Building Categorization Systems by Supervised Clustering
Authors: Charu C. Aggarwal, Stephen C. Gates, Philip S. Yu
Venue: KDD
Year: 1999

This paper investigates the use of supervised clustering in order to create sets of categories for classification of documents.
Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering
Authors: Charu C. Aggarwal, Joel L. Wolf, Kun-Lung Wu, Philip S. Yu
Venue: KDD
Year: 1999

Article Free Access Share on Horting hatches an egg: a new graph-theoretic approach to collaborative filtering Authors: Charu C. Aggarwal IBM T. J. Watson Research Center, Yorktown Heights, NY IBM T. J. Watson Research Center, Yorktown Heights, NYView Profile , Joel L. Wolf IBM T. J. Watson Research Center, Yorktown Heights, NY IBM T. J. Watson Research Center, Yorktown Heights, NYView Profile , Kun-Lung Wu IBM T. J. Watson Research Center, Yorktown Heights, NY IBM T. J. Watson Research Center, Yorktown Heights, NYView Profile , Philip S. Yu IBM T. J. Watson Research Center, Yorktown Heights, NY IBM T. J. Watson Research Center, Yorktown Heights, NYView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 201–212https://doi.org/10.1145/312129.312230Published:01 August 1999Publication History 218citation2,375DownloadsMetricsTotal Citations218Total Downloads2,375Last 12 Months144Last 6 weeks44 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Data Mining: Crossing the Chasm (Invited talk, Abstract only)
Authors: Rakesh Agrawal
Venue: KDD
Year: 1999

Data mining has attracted tremendous interest in the research community as well as commercial market place. The last few years have witnessed a flurry of technical innovations and introduction of comercial products. The next major challenge facing data mining is to make a transition from a niche technology to a main stream technology. I will present key technical and environmental issues that must be addressed for a successful transition.
An Efficient Algorithm to Update Large Itemsets with Early Pruning
Authors: Necip Fazil Ayan, Abdullah Uz Tansel, M. Erol Arkun
Venue: KDD
Year: 1999

We present an efficient algorithm (UWEP) for updating large itemsets when new transactions are added to the set of old transactions. UWEP employs a dynamic lookahead strategy in updating the existing large itemsets by detecting and removing those that will no longer remain large after the contribution of the new set of transactions. It differs from the other update algorithms by scanning the existing database at most once and the new database exactly once. Moreover, it generates and counts the minimum number of candidates in the new database. The experiments on synthetic data show that UWEP outperforms the existing algorithms in terms of the candidates generated and counted.