OpenRaiser/Intern-Atlas huggingface dataset:
Learning from scarce experience
Authors: Leonid Peshkin, Christian R. Shelton
Venue: Conference ICML 2002
Year: 2002

Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each change of the target policy, its value is estimated from the results of following that very policy. This requires a large number of interactions with the environment as different polices are considered. We present a family of algorithms based on likelihood ratio estimation that use data gathered when executing one policy (or collection of policies) to estimate the value of a different policy. The algorithms combine estimation and optimization stages. The former utilizes experience to build a non-parametric representation of an optimized function. The latter performs optimization on this estimate. We show positive empirical results and provide the sample complexity bound.
Gaussian process factorization machines for context-aware recommendations
Authors: Trung V. Nguyen, Alexandros Karatzoglou, Linas Baltrunas
Venue: Conference SIGIR 2014
Year: 2014

Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity.
A robust shape model for multi-view car alignment
Authors: Yan Li, Leon Gu, Takeo Kanade
Venue: Conference CVPR 2009
Year: 2009

We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate object shape and pose hypotheses from randomly sampled partial shapes - subsets of feature points. The hypotheses are then evaluated to find the one that minimizes the shape prediction error. The proposed model can effectively handle outliers and recover the object shape. We evaluate our approach on a challenging dataset which contains over 2,000 multi-view car images and spans a wide variety of types, lightings, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.
Estimating motion and size of moving non-line-of-sight objects in cluttered environments
Authors: Rohit Pandharkar, Andreas Velten, Andrew Bardagjy, Everett Lawson, Moungi Bawendi, Ramesh Raskar
Venue: Conference CVPR 2011
Year: 2011

We present a technique for motion and size estimation of non-line-of-sight (NLOS) moving objects in cluttered environments using a time of flight camera and multipath analysis. We exploit relative times of arrival after reflection from a grid of points on a diffuse surface and create a virtual phased-array. By subtracting space-time impulse responses for successive frames, we separate responses of NLOS moving objects from those resulting from the cluttered environment. After reconstructing the line-of-sight scene geometry, we analyze the space of wavefronts using the phased array and solve a constrained least squares problem to recover the NLOS target location. Importantly, we can recover target's motion vector even in presence of uncalibrated time and pose bias common in time of flight systems. In addition, we compute the upper bound on the size of the target by backprojecting the extremas of the time profiles. Ability to track targets inside rooms despite opaque occluders and multipath responses has numerous applications in search and rescue, medicine and defense. We show centimeter accurate results by making appropriate modifications to a time of flight system.
Syntf: Synthetic and differentially private term frequency vectors for privacy-preserving text mining
Authors: Benjamin Weggenmann, Florian Kerschbaum
Venue: Conference SIGIR 2018
Year: 2018

Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anonymous: Search logs can reveal sensitive details about a user, critical articles or messages about a company or government might have severe or fatal consequences for a critic, and negative feedback in customer surveys might negatively impact business relations if they are identified. Simply removing personally identifying information from a document is, however, insufficient to protect the writer's identity: Given some reference texts of suspect authors, so-called authorship attribution methods can reidentfy the author from the text itself. One of the most prominent models to represent documents in many common text mining and information retrieval tasks is the vector space model where each document is represented as a vector, typically containing its term frequencies or related quantities. We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors. We evaluate our method on an exemplary text classification task and demonstrate that it only has a low impact on its accuracy. In contrast, we show that our method strongly affects authorship attribution techniques to the level that they become infeasible with a much stronger decline in accuracy. Other than previous authorship obfuscation methods, our approach is the first that fulfills differential privacy and hence comes with a provable plausible deniability guarantee.
Machine learning and causal inference for policy evaluation
Authors: Susan Athey
Venue: Conference KDD 2015
Year: 2015

A large literature on causal inference in statistics, econometrics, biostatistics, and epidemiology (see, e.g., Imbens and Rubin [2015] for a recent survey) has focused on methods for statistical estimation and inference in a setting where the researcher wishes to answer a question about the (counterfactual) impact of a change in a policy, or "treatment" in the terminology of the literature. The policy change has not necessarily been observed before, or may have been observed only for a subset of the population; examples include a change in minimum wage law or a change in a firm's price. The goal is then to estimate the impact of small set of "treatments" using data from randomized experiments or, more commonly, "observational" studies (that is, non-experimental data). The literature identifies a variety of assumptions that, when satisfied, allow the researcher to draw the same types of conclusions that would be available from a randomized experiment. To estimate causal effects given non-random assignment of individuals to alternative policies in observational studies, popular techniques include propensity score weighting, matching, and regression analysis; all of these methods adjust for differences in observed attributes of individuals. Another strand of literature in econometrics, referred to as "structural modeling," fully specifies the preferences of actors as well as a behavioral model, and estimates those parameters from data (for applications to auction-based electronic commerce, see Athey and Haile [2007] and Athey and Nekipelov [2012]). In both cases, parameter estimates are interpreted as "causal," and they are used to make predictions about the effect of policy changes. In contrast, the supervised machine learning literature has traditionally focused on prediction, providing data-driven approaches to building rich models and relying on cross-validation as a powerful tool for model selection. These methods have been highly successful in practice. This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, where the papers are connected by three themes.
Means in spaces of tree-like shapes
Authors: Aasa Feragen, Søren Hauberg, Mads Nielsen, François Lauze
Venue: Conference ICCV 2011
Year: 2011

The mean is often the most important statistic of a dataset as it provides a single point that summarizes the entire set. While the mean is readily defined and computed in Euclidean spaces, no commonly accepted solutions are currently available in more complicated spaces, such as spaces of tree-structured data. In this paper we study the notion of means, both generally in Gromov's CAT(0)-spaces (metric spaces of non-positive curvature), but also specifically in the space of tree-like shapes. We prove local existence and uniqueness of means in such spaces and discuss three different algorithms for computing means. We make an experimental evaluation of the three algorithms through experiments on three different sets of data with tree-like structure: a synthetic dataset, a leaf morphology dataset from images, and a set of human airway subtrees from medical CT scans. This experimental study provides great insight into the behavior of the different methods and how they relate to each other. More importantly, it also provides mathematically well-founded, tractable and robust “average trees”. This statistic is of utmost importance due to the ever-presence of tree-like structures in human anatomy, e.g., airways and vascularization systems.
Intriguing properties of adversarial examples
Authors: Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le
Venue: Conference ICLR 2018
Year: 2018

It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we argue that the origin of adversarial examples is primarily due to an inherent uncertainty that neural networks have about their predictions. We show that the functional form of this uncertainty is independent of architecture, dataset, and training protocol; and depends only on the statistics of the logit differences of the network, which do not change significantly during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white \emph{and} black box attacks compared to previous attempts.
Shape-based recognition of 3d point clouds in urban environments
Authors: Aleksey Golovinskiy, Vladimir G. Kim, Thomas A. Funkhouser
Venue: Conference ICCV 2009
Year: 2009

This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potential object locations (with hierarchical clustering). Then, we segment points near those locations into foreground and background sets (with a graph-cut algorithm). Next, we build a feature vector for each point cluster (based on both its shape and its context). Finally, we label the feature vectors using a classifier trained on a set of manually labeled objects. The paper presents several alternative methods for each step. We quantitatively evaluate the system and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest. Then, we use this truth data as a training set to recognize objects amidst approximately 1 billion points of the remainder of the Ottawa scan.
Unsupervised discovery of drug side-effects from heterogeneous data sources
Authors: Fenglong Ma, Chuishi Meng, Houping Xiao, Qi Li, Jing Gao, Lu Su, Aidong Zhang
Venue: Conference KDD 2017
Year: 2017

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.
Labelme video: Building a video database with human annotations
Authors: Jenny Yuen, Bryan C. Russell, Ce Liu 0001, Antonio Torralba 0001
Venue: Conference ICCV 2009
Year: 2009

Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well as from missing comprehensive annotated video databases for benchmarking. We designed an online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos. The annotations are also complemented with knowledge from static image databases to infer occlusion and depth information. Using this system, we have built a scalable video database composed of diverse video samples and paired with human-guided annotations. We complement this paper demonstrating potential uses of this database by studying motion statistics as well as cause-effect motion relationships between objects.
Variational distance-dependent image restoration
Authors: Ran Kaftory, Yoav Y. Schechner, Yehoshua Y. Zeevi
Venue: Conference CVPR 2007
Year: 2007

There is a need to restore color images that suffer from distance-dependent degradation during acquisition. This occurs, for example, when imaging through scattering media. There, signal attenuation worsens with the distance of an object from the camera. A 'naive' restoration may attempt to restore the image by amplifying the signal in each pixel according to the distance of its corresponding object. This, however, would amplify the noise in a nonuniform manner. Moreover, standard space-invariant de-noising over-blurs close by objects (which have low noise), or insufficiently smoothes distant objects (which are very noisy). We present a variational method to overcome this problem. It uses a regularization operator which is distance dependent, in addition to being edge-preserving and color-channel coupled. Minimizing this functional results in a scheme of reconstruction-while-denoising. It preserves important features, such as the texture of close by objects and edges of distant ones. A restoration algorithm is presented for reconstructing color images taken through haze. The algorithm also restores the path radiance, which is equivalent to the distance map. We demonstrate the approach experimentally.
Chaotic Invariants for Human Action Recognition
Authors: Saad Ali, Arslan Basharat, Mubarak Shah
Venue: Conference ICCV 2007
Year: 2007

The paper introduces an action recognition framework that uses concepts from the theory of chaotic systems to model and analyze nonlinear dynamics of human actions. Trajectories of reference joints are used as the representation of the non-linear dynamical system that is generating the action. Each trajectory is then used to reconstruct a phase space of appropriate dimension by employing a delay-embedding scheme. The properties of the reconstructed phase space are captured in terms of dynamical and metric invariants that include Lyapunov exponent, correlation integral and correlation dimension. Finally, the action is represented by a feature vector which is a combination of these invariants over all the reference trajectories. Our contributions in this paper include :1) investigation of the appropriateness of theory of chaotic systems for human action modelling and recognition, 2) a new set of features to characterize nonlinear dynamics of human actions, 3) experimental validation of the feasibility and potential merits of carrying out action recognition using methods from theory of chaotic systems.
Experimental comparisons of online and batch versions of bagging and boosting
Authors: Nikunj C. Oza, Stuart Russell
Venue: Conference KDD 2001
Year: 2001

Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, i.e., they require multiple passes through the training data. In previous work, we presented online bagging and boosting algorithms that only require one pass through the training data and presented experimental results on some relatively small datasets. Through additional experiments on a variety of larger synthetic and real datasets, this paper demonstrates that our online versions perform comparably to their batch counterparts in terms of classification accuracy. We also demonstrate the substantial reduction in running time we obtain with our online algorithms because they require fewer passes through the training data.
Personalized employee training course recommendation with career development awareness
Authors: Chao Wang, Hengshu Zhu, Chen Zhu, Xi Zhang, Enhong Chen, Hui Xiong
Venue: Conference WWW 2020
Year: 2020

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this paper, we propose an explainable personalized online course recommender system for enhancing employee training and development. A unique perspective of our system is to jointly model both the employees’ current competencies and their career development preferences in an explainable way. Specifically, the recommender system is based on a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN). In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Finally, extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of DCBVN, as well as its robustness on sparse and cold-start scenarios.
Interpretable deep graph generation with node-edge co-disentanglement
Authors: Xiaojie Guo 0002, Liang Zhao 0002, Zhao Qin, Lingfei Wu 0001, Amarda Shehu, Yanfang Ye 0001
Venue: Conference KDD 2020
Year: 2020

Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the disentangled representations behind a graph remains largely unexplored, especially for the attributed graph with both node and edge features. Disentanglement learning for graph generation has substantial new challenges including 1) the lack of graph deconvolution operations to jointly decode node and edge attributes; and 2) the difficulty in enforcing the disentanglement among latent factors that respectively influence: i) only nodes, ii) only edges, and iii) joint patterns between them. To address these challenges, we propose a new disentanglement enhancement framework for deep generative models for attributed graphs. In particular, a novel variational objective is proposed to disentangle the above three types of latent factors, with novel architecture for node and edge deconvolutions. Moreover, within each type, individual-factor-wise disentanglement is further enhanced, which is shown to be a generalization of the existing framework for images. Qualitative and quantitative experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed model and its extensions.
Improved nyström low-rank approximation and error analysis
Authors: Kai Zhang 0001, Ivor W. Tsang, James T. Kwok
Venue: Conference ICML 2008
Year: 2008

Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and sampling, as the mainstream of such algorithms, has drawn considerable attention in both theory and practice. This paper presents detailed studies on the Nyström sampling scheme and in particular, an error analysis that directly relates the Nyström approximation quality with the encoding powers of the landmark points in summarizing the data. The resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for Nyström low-rank approximation. We compare it with state-of-the-art approaches that range from greedy schemes to probabilistic sampling. Our algorithm achieves significant performance gains in a number of supervised/unsupervised learning tasks including kernel PCA and least squares SVM.
Geometric blur for template matching
Authors: Alexander C. Berg, Jitendra Malik
Venue: Conference CVPR 2001
Year: 2001

We address the problem of finding point correspondences in images by way of an approach to template matching that is robust under affine distortions. This is achieved by applying "geometric blur" to both the template and the image, resulting in a fall-off in similarity that is close to linear in the norm of the distortion between the template and the image. Results in wide baseline stereo correspondence, face detection, and feature correspondence are included.