pwc-archive/papers-with-abstracts huggingface dataset:
Correlated Bigram LSA for Unsupervised Language Model Adaptation
Authors: Yik-Cheung Tam, Tanja Schultz
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

We propose using correlated bigram LSA for unsupervised LM adaptation for automatic speech recognition. The model is trained using efficient variational EM and smoothed using the proposed fractional Kneser-Ney smoothing which handles fractional counts. Our approach can be scalable to large training corpora via bootstrapping of bigram LSA from unigram LSA. For LM adaptation, unigram and bigram LSA are integrated into the background N-gram LM via marginal adaptation and linear interpolation respectively. Experimental results show that applying unigram and bigram LSA together yields 6%--8% relative perplexity reduction and 0.6% absolute character error rates (CER) reduction compared to applying only unigram LSA on the Mandarin RT04 test set. Comparing with the unadapted baseline, our approach reduces the absolute CER by 1.2%.
Playing Pinball with non-invasive BCI
Authors: Matthias Krauledat, Konrad Grzeska, Max Sagebaum, Benjamin Blankertz, Carmen Vidaurre, Klaus-Robert Müller, Michael Schröder
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: EEG, Electroencephalogram (EEG)

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for complex control tasks. In the present study, however, we demonstrate this is possible and report on the interaction of a human subject with a complex real device: a pinball machine. First results in this single subject study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires complex predictive behavior. Using machine learning methods for mental state decoding, BCI-based pinball control is possible within the first session without the necessity to employ lengthy subject training. While the current study is still of anecdotal nature, it clearly shows that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.
Bounding Performance Loss in Approximate MDP Homomorphisms
Authors: Jonathan Taylor, Doina Precup, Prakash Panagaden
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

We define a metric for measuring behavior similarity between states in a Markov decision process (MDP), in which action similarity is taken into account. We show that the kernel of our metric corresponds exactly to the classes of states defined by MDP homomorphisms (Ravindran \& Barto, 2003). We prove that the difference in the optimal value function of different states can be upper-bounded by the value of this metric, and that the bound is tighter than that provided by bisimulation metrics (Ferns et al. 2004, 2005). Our results hold both for discrete and for continuous actions. We provide an algorithm for constructing approximate homomorphisms, by using this metric to identify states that can be grouped together, as well as actions that can be matched. Previous research on this topic is based mainly on heuristics.
Integrating Locally Learned Causal Structures with Overlapping Variables
Authors: David Danks, Clark Glymour, Robert E. Tillman
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Missing Values

In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset. There are several asymptotically correct, informative algorithms that search for causal information given a single dataset, even with missing values and hidden variables. There are, however, no such reliable procedures for distributed data with overlapping variables, and only a single heuristic procedure (Structural EM). This paper describes an asymptotically correct procedure, ION, that provides all the information about structure obtainable from the marginal independence relations. Using simulated and real data, the accuracy of ION is compared with that of Structural EM, and with inference on complete, unified data.
Bayesian Kernel Shaping for Learning Control
Authors: Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakumar, Stefan Schaal
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Gaussian Processes, regression, Reinforcement Learning

In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of output noise (i.e., heteroscedasticity) varies spatially. Unfortunately, it presents a complex computational problem as the danger of overfitting is high and the individual optimization of every kernel in a learning system may be overly expensive due to the introduction of too many open learning parameters. Previous work has suggested gradient descent techniques or complex statistical hypothesis methods for local kernel shaping, typically requiring some amount of manual tuning of meta parameters. In this paper, we focus on nonparametric regression and introduce a Bayesian formulation that, with the help of variational approximations, results in an EM-like algorithm for simultaneous estimation of regression and kernel parameters. The algorithm is computationally efficient (suitable for large data sets), requires no sampling, automatically rejects outliers and has only one prior to be specified. It can be used for nonparametric regression with local polynomials or as a novel method to achieve nonstationary regression with Gaussian Processes. Our methods are particularly useful for learning control, where reliable estimation of local tangent planes is essential for adaptive controllers and reinforcement learning. We evaluate our methods on several synthetic data sets and on an actual robot which learns a task-level control law.
Efficient Sampling for Gaussian Process Inference using Control Variables
Authors: Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: General Classification, regression

Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algorithm for sampling from the posterior process of the GP model. This algorithm uses control variables which are auxiliary function values that provide a low dimensional representation of the function. At each iteration, the algorithm proposes new values for the control variables and generates the function from the conditional GP prior. The control variable input locations are found by continuously minimizing an objective function. We demonstrate the algorithm on regression and classification problems and we use it to estimate the parameters of a differential equation model of gene regulation.
Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement
Authors: Michael T. Todd, Yael Niv, Jonathan D. Cohen
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

Working memory is a central topic of cognitive neuroscience because it is critical for solving real world problems in which information from multiple temporally distant sources must be combined to generate appropriate behavior. However, an often neglected fact is that learning to use working memory effectively is itself a difficult problem. The Gating" framework is a collection of psychological models that show how dopamine can train the basal ganglia and prefrontal cortex to form useful working memory representations in certain types of problems. We bring together gating with ideas from machine learning about using finite memory systems in more general problems. Thus we present a normative Gating model that learns, by online temporal difference methods, to use working memory to maximize discounted future rewards in general partially observable settings. The model successfully solves a benchmark working memory problem, and exhibits limitations similar to those observed in human experiments. Moreover, the model introduces a concise, normative definition of high level cognitive concepts such as working memory and cognitive control in terms of maximizing discounted future rewards."
The Infinite Factorial Hidden Markov Model
Authors: Jurgen V. Gael, Yee W. Teh, Zoubin Ghahramani
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: blind source separation, model

We introduces a new probability distribution over a potentially infinite number of binary Markov chains which we call the Markov Indian buffet process. This process extends the IBP to allow temporal dependencies in the hidden variables. We use this stochastic process to build a nonparametric extension of the factorial hidden Markov model. After working out an inference scheme which combines slice sampling and dynamic programming we demonstrate how the infinite factorial hidden Markov model can be used for blind source separation.
Multi-Level Active Prediction of Useful Image Annotations for Recognition
Authors: Sudheendra Vijayanarasimhan, Kristen Grauman
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the category-learner to strategically choose what annotations it receives---based on both the expected reduction in uncertainty as well as the relative costs of obtaining each annotation. We construct a multiple-instance discriminative classifier based on the initial training data. Then all remaining unlabeled and weakly labeled examples are surveyed to actively determine which annotation ought to be requested next. After each request, the current classifier is incrementally updated. Unlike previous work, our approach accounts for the fact that the optimal use of manual annotation may call for a combination of labels at multiple levels of granularity (e.g., a full segmentation on some images and a present/absent flag on others). As a result, it is possible to learn more accurate category models with a lower total expenditure of manual annotation effort.
Diffeomorphic Dimensionality Reduction
Authors: Christian Walder, Bernhard Schölkopf
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Dimensionality Reduction

This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high and low dimensional spaces to be a diffeomorphism is a natural way of ensuring that pairwise distances are approximately preserved. Accordingly we develop an algorithm which diffeomorphically maps the data near to a lower dimensional subspace and then projects onto that subspace. The problem of solving for the mapping is transformed into one of solving for an Eulerian flow field which we compute using ideas from kernel methods. We demonstrate the efficacy of our approach on various real world data sets.
Learning a discriminative hidden part model for human action recognition
Authors: Yang Wang, Greg Mori
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Action Recognition, Object, Object Recognition, Temporal Action Localization

We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field~(hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
Algorithms for Infinitely Many-Armed Bandits
Authors: Yizao Wang, Jean-Yves Audibert, Rémi Munos
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matchs (up to logarithmic factors) the upper-bound in some cases.
Large Margin Taxonomy Embedding for Document Categorization
Authors: Kilian Q. Weinberger, Olivier Chapelle
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Classification, General Classification, Multi-class Classification

Applications of multi-class classification, such as document categorization, often appear in cost-sensitive settings. Recent work has significantly improved the state of the art by moving beyond ``flat'' classification through incorporation of class hierarchies [Cai and Hoffman 04]. We present a novel algorithm that goes beyond hierarchical classification and estimates the latent semantic space that underlies the class hierarchy. In this space, each class is represented by a prototype and classification is done with the simple nearest neighbor rule. The optimization of the semantic space incorporates large margin constraints that ensure that for each instance the correct class prototype is closer than any other. We show that our optimization is convex and can be solved efficiently for large data sets. Experiments on the OHSUMED medical journal data base yield state-of-the-art results on topic categorization.
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
Authors: Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie Luo, Holly Jimison, Frank Ohl, Misha Pavel
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Novelty Detection, Object Recognition, speech-recognition, Speech Recognition

Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on 'incongruent events' - when 'general level' and 'specific level' classifiers give conflicting predictions. We define a formal framework for the representation and processing of incongruent events: starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies. For each event, we compute its probability in different ways, based on adjacent levels (according to the partial order) in the label hierarchy . An incongruent event is an event where the probability computed based on some more specific level (in accordance with the partial order) is much smaller than the probability computed based on some more general level, leading to conflicting predictions. We derive algorithms to detect incongruent events from different types of hierarchies, corresponding to class membership or part membership. Respectively, we show promising results with real data on two specific problems: Out Of Vocabulary words in speech recognition, and the identification of a new sub-class (e.g., the face of a new individual) in audio-visual facial object recognition.
Spectral Hashing
Authors: Yair Weiss, Antonio Torralba, Rob Fergus
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: graph partitioning, Semantic Similarity, Semantic Textual Similarity

Semantic hashing seeks compact binary codes of datapoints so that the Hamming distance between codewords correlates with semantic similarity. Hinton et al. used a clever implementation of autoencoders to find such codes. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresh- olded eigenvectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigen- functions of manifolds, we show how to efficiently calculate the code of a novel datapoint. Taken together, both learning the code and applying it to a novel point are extremely simple. Our experiments show that our codes significantly outperform the state-of-the art.
MAS: a multiplicative approximation scheme for probabilistic inference
Authors: Ydo Wexler, Christopher Meek
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

We propose a multiplicative approximation scheme (MAS) for inference problems in graphical models, which can be applied to various inference algorithms. The method uses $\epsilon$-decompositions which decompose functions used throughout the inference procedure into functions over smaller sets of variables with a known error $\epsilon$. MAS translates these local approximations into bounds on the accuracy of the results. We show how to optimize $\epsilon$-decompositions and provide a fast closed-form solution for an $L_2$ approximation. Applying MAS to the Variable Elimination inference algorithm, we introduce an algorithm we call DynaDecomp which is extremely fast in practice and provides guaranteed error bounds on the result. The superior accuracy and efficiency of DynaDecomp is demonstrated.
Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1
Authors: Klaus Wimmer, Marcel Stimberg, Robert Martin, Lars Schwabe, Jorge Mariño, James Schummers, David C. Lyon, Mriganka Sur, Klaus Obermayer
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12

One major role of primary visual cortex (V1) in vision is the encoding of the orientation of lines and contours. The role of the local recurrent network in these computations is, however, still a matter of debate. To address this issue, we analyze intracellular recording data of cat V1, which combine measuring the tuning of a range of neuronal properties with a precise localization of the recording sites in the orientation preference map. For the analysis, we consider a network model of Hodgkin-Huxley type neurons arranged according to a biologically plausible two-dimensional topographic orientation preference map. We then systematically vary the strength of the recurrent excitation and inhibition relative to the strength of the afferent input. Each parametrization gives rise to a different model instance for which the tuning of model neurons at different locations of the orientation map is compared to the experimentally measured orientation tuning of membrane potential, spike output, excitatory, and inhibitory conductances. A quantitative analysis shows that the data provides strong evidence for a network model in which the afferent input is dominated by strong, balanced contributions of recurrent excitation and inhibition. This recurrent regime is close to a regime of 'instability', where strong, self-sustained activity of the network occurs. The firing rate of neurons in the best-fitting network is particularly sensitive to small modulations of model parameters, which could be one of the functional benefits of a network operating in this particular regime.
Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG
Authors: Julia Owen, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan, David P. Wipf
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: EEG, Electroencephalogram (EEG)

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and orientation of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to have theoretically zero bias estimating both the location and orientation of multi-component dipoles even in the presence of correlations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.
Localized Sliced Inverse Regression
Authors: Qiang Wu, Sayan Mukherjee, Feng Liang
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Dimensionality Reduction, General Classification, regression

We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classification problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.
Model selection and velocity estimation using novel priors for motion patterns
Authors: Shuang Wu, Hongjing Lu, Alan L. Yuille
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: Model Selection, Translation

Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation. These findings are inconsistent with standard models of motion integration which predict best performance for translation [6]. To explain this discrepancy, our theory formulates motion perception at two levels of inference: we first perform model selection between the competing models (e.g. translation, rotation, and expansion) and then estimate the velocity using the selected model. We define novel prior models for smooth rotation and expansion using techniques similar to those in the slow-and-smooth model [17] (e.g. Green functions of differential operators). The theory gives good agreement with the trends observed in human experiments.
Robust Regression and Lasso
Authors: Huan Xu, Constantine Caramanis, Shie Mannor
Date: 2008-12-01 00:00:00
Proceeding: NeurIPS 2008 12
Tasks: regression

We consider robust least-squares regression with feature-wise disturbance. We show that this formulation leads to tractable convex optimization problems, and we exhibit a particular uncertainty set for which the robust problem is equivalent to $\ell_1$ regularized regression (Lasso). This provides an interpretation of Lasso from a robust optimization perspective. We generalize this robust formulation to consider more general uncertainty sets, which all lead to tractable convex optimization problems. Therefore, we provide a new methodology for designing regression algorithms, which generalize known formulations. The advantage is that robustness to disturbance is a physical property that can be exploited: in addition to obtaining new formulations, we use it directly to show sparsity properties of Lasso, as well as to prove a general consistency result for robust regression problems, including Lasso, from a unified robustness perspective.