OpenRaiser/Intern-Atlas huggingface dataset:
Extreme meta-classification for large-scale zero-shot retrieval
Authors: Sachin Yadav 0002, Deepak Saini, Anirudh Buvanesh, Bhawna Paliwal, Kunal Dahiya, Siddarth Asokan, Yashoteja Prabhu, Jian Jiao 0007, Manik Varma
Venue: Conference KDD 2024
Year: 2024

We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the items lying closest to the query. While this approach allows efficient addition and retrieval of novel items, the small encoder lacks sufficient capacity for the necessary world knowledge in complex retrieval tasks. The extreme classification approaches have addressed this by learning a separate classifier for each item observed in the training set which significantly increases the representation capacity of the model. Such classifiers outperform Siamese approaches on observed items, but cannot be trained for novel items due to data and latency constraints. To bridge these gaps, this paper develops: (1) A new algorithmic framework, EMMETT, which efficiently synthesizes classifiers on-the-fly for novel items, by relying on the readily available classifiers for observed items; (2) A new algorithm, IRENE, which is a simple and effective instance of EMMETT that is specifically suited for large-scale deployments, and (3) A new theoretical framework for analyzing the generalization performance in large-scale zero-shot retrieval which guides our algorithm and training related design decisions. Comprehensive experiments are conducted on a wide range of retrieval tasks which demonstrate that IRENE improves the zero-shot retrieval accuracy by up to 15% points in Recall@10 when added on top of leading encoders. Additionally, on an online A/B test in a large-scale ad retrieval task in a major search engine, IRENE improved the ad click-through rate by 4.2%. Lastly, we validate our design choices through extensive ablative experiments. The source code for IRENE is available at https://aka.ms/irene.
Sparse probabilistic regression for activity-independent human pose inference
Authors: Raquel Urtasun, Trevor Darrell
Venue: Conference CVPR 2008
Year: 2008

Discriminative approaches to human pose inference involve mapping visual observations to articulated body configurations. Current probabilistic approaches to learn this mapping have been limited in their ability to handle domains with a large number of activities that require very large training sets. We propose an online probabilistic regression scheme for efficient inference of complex, high- dimensional, and multimodal mappings. Our technique is based on a local mixture of Gaussian processes, where locality is defined based on both appearance and pose, and where the mapping hyperparameters can vary across local neighborhoods to better adapt to specific regions in the pose space. The mixture components are defined online in very small neighborhoods, so learning and inference is extremely efficient. When the mapping is one-to-one, we derive a bound on the approximation error of local regression (vs. global regression) for monotonically decreasing co- variance functions. Our method can determine when training examples are redundant given the rest of the database, and use this criteria for pruning. We report results on synthetic (Poser) and real (Humaneva) pose databases, obtaining fast and accurate pose estimates using training set sizes up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> .
ReARTeR: Retrieval-augmented reasoning with trustworthy process rewarding
Authors: Zhongxiang Sun, Qipeng Wang, Weijie Yu, Xiaoxue Zang, Kai Zheng, Jun Xu, Xiao Zhang, Yang Song, Han Li
Venue: Conference SIGIR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs) have shown promise in knowledge-intensive tasks, yet their reasoning capabilities, particularly for complex multi-step reasoning, remain limited. Although recent approaches have explored integrating RAG with chain-of-thought reasoning or incorporating test-time search with process reward model (PRM), these methods face several untrustworthy challenges, including lack of explanations, bias in PRM training data, early-step bias in PRM scores, and ignoring post-training that fails to fully optimize reasoning potential. To address these issues, we propose Retrieval-Augmented Reasoning through Trustworthy Process Rewarding (ReARTeR), a framework that enhances RAG systems' reasoning capabilities through both post-training and test-time scaling. At test time, ReARTeR introduces Trustworthy Process Rewarding via a Process Reward Model for accurate scalar scoring and a Process Explanation Model (PEM) for generating natural language explanations, enabling step refinement. During post-training, we leverage Monte Carlo Tree Search guided by Trustworthy Process Rewarding to collect high-quality step-level preference data, which is used to optimize the model through Iterative Preference Optimization. ReARTeR tackles three key challenges: (1) misalignment between PRM and PEM, addressed through off-policy preference learning; (2) bias in PRM training data, mitigated by a balanced annotation method and incorporating stronger annotations for difficult examples; and (3) early-step bias in PRM, resolved via a temporal-difference-based look-ahead search strategy. Experimental results on multi-step reasoning benchmarks demonstrate that ReARTeR significantly improves reasoning performance, highlighting its potential to advance the reasoning capability of RAG systems.
External evaluation measures for subspace clustering
Authors: Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl
Venue: Conference CIKM 2011
Year: 2011

Knowledge discovery in databases requires not only development of novel mining techniques but also fair and comparable quality assessment based on objective evaluation measures. Especially in young research areas where no common measures are available, researchers are unable to provide a fair evaluation. Typically, publications glorify the high quality of one approach only justified by an arbitrary evaluation measure. However, such conclusions can only be drawn if the evaluation measures themselves are fully understood. In this paper, we provide the basis for systematic evaluation in the emerging research area of subspace clustering. We formalize general quality criteria for subspace clustering measures not yet addressed in the literature. We compare the existing external evaluation methods based on these criteria and pinpoint limitations. We propose a novel external evaluation measure which meets the requirements in form of quality properties. In thorough experiments we empirically show characteristic properties of evaluation measures. Overall, we provide a set of evaluation measures that fulfill the general quality criteria as recommendation for future evaluations. All measures and datasets are provided on our website and are integrated in our evaluation framework.
NCDawareRank: A novel ranking method that exploits the decomposable structure of the web
Authors: Athanasios N. Nikolakopoulos, John D. Garofalakis
Venue: Conference WSDM 2013
Year: 2013

Research about the topological characteristics of the hyperlink graph has shown that Web possesses a nested block structure, indicative of its innate hierarchical organization. This crucial observation opens the way for new approaches that can usefully regard Web as a Nearly Completely Decomposable(NCD) system; In recent years, such approaches gave birth to various efficient methods and algorithms that exploit NCD from a computational point of view and manage to considerably accelerate the extraction of the PageRank vector. However, very little have been done towards the qualitative exploitation of NCD.
A scalable modular convex solver for regularized risk minimization
Authors: Choon Hui Teo, Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le
Venue: Conference KDD 2007
Year: 2007

A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as l1 and l2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox.
Automatic social circle detection using multi-view clustering
Authors: Yuhao Yang, Chao Lan, Xiaoli Li, Bo Luo, Jun Huan
Venue: Conference CIKM 2014
Year: 2014

With the development of information technology, online social networks grow dramatically. They now play a significant role in people's social life, especially for the younger generation. While huge amount of information is available in online social networks, privacy concerns arise. Among various privacy protection proposals, the notions of privacy as control and information boundary have been introduced. Commercial social networking sites have adopted the concept to implement mechanisms such as Google circles and Facebook custom lists. However, the functions are not widely accepted by the users, partly because it is tedious and labor-intensive to manually assign friends into circles.
Scalable hands-free transfer learning for online advertising
Authors: Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, Foster J. Provost
Venue: Conference KDD 2014
Year: 2014

Internet display advertising is a critical revenue source for publishers and online content providers, and is supported by massive amounts of user and publisher data. Targeting display ads can be improved substantially with machine learning methods, but building many models on massive data becomes prohibitively expensive computationally. This paper presents a combination of strategies, deployed by the online advertising firm Dstillery, for learning many models from extremely high-dimensional data efficiently and without human intervention. This combination includes: (i)~A method for simple-yet-effective transfer learning where a model learned from data that is relatively abundant and cheap is taken as a prior for Bayesian logistic regression trained with stochastic gradient descent (SGD) from the more expensive target data. (ii)~A new update rule for automatic learning rate adaptation, to support learning from sparse, high-dimensional data, as well as the integration with adaptive regularization. We present an experimental analysis across 100 different ad campaigns, showing that the transfer learning indeed improves performance across a large number of them, especially at the start of the campaigns. The combined "hands-free" method needs no fiddling with the SGD learning rate, and we show that it is just as effective as using expensive grid search to set the regularization parameter for each campaign.
Letor methods for unsupervised rank aggregation
Authors: Avradeep Bhowmik, Joydeep Ghosh
Venue: Conference WWW 2017
Year: 2017

Learning the true rank ordering among objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to recommendation systems and search aggregation. We study the problem of unsupervised rank aggregation where no ground truth ordering information in available, neither about the true preference ordering among any set of objects nor about the quality of individual rank lists. Aggregating the often inconsistent and poor quality rank lists in such an unsupervised manner is a highly challenging problem, and standard consensus-based methods fall short in terms of both quality and scalability. In this manuscript we propose a novel framework to bypass these issues by using object attributes to augment the standard rank aggregation framework. We design algorithms that learn joint models on both rank lists and object features to obtain an aggregated rank ordering that is more accurate and robust, and also helps weed out rank lists of dubious validity. We validate our techniques on synthetic datasets where our algorithm is able to estimate the true rank ordering even when the rank lists are corrupted. Experiments on three real datasets, MQ2008, MQ2007 and OHSUMED, show that using object features can result in significant improvement in performance over existing rank aggregation methods that do not use object information. Furthermore, when at least some of the rank lists are of high quality, our methods are able to effectively exploit such information to output an aggregated rank ordering of high accuracy.
Combining top-n recommenders with metasearch algorithms
Authors: Daniel Valcarce, Javier Parapar, Álvaro Barreiro
Venue: Conference SIGIR 2017
Year: 2017

Given the diversity of recommendation algorithms, choosing one technique is becoming increasingly difficult. In this paper, we explore methods for combining multiple recommendation approaches. We studied rank aggregation methods that have been proposed for the metasearch task (i.e., fusing the outputs of different search engines) but have never been applied to merge top-N recommender systems. These methods require no training data nor parameter tuning. We analysed two families of methods: voting-based and score-based approaches. These rank aggregation techniques yield significant improvements over state-of-the-art top-N recommenders. In particular, score-based methods yielded good results; however, some voting techniques were also competitive without using score information, which may be unavailable in some recommendation scenarios. The studied methods not only improve the state of the art of recommendation algorithms but they are also simple and efficient.
Segmenting images on the tensor manifold
Authors: Yogesh Rathi, Allen R. Tannenbaum, Oleg V. Michailovich
Venue: Conference CVPR 2007
Year: 2007

In this note, we propose a method to perform segmentation on the tensor manifold, that is, the space of positive definite matrices of given dimension. In this work, we explicitly use the Riemannian structure of the tensor space in designing our algorithm. This structure has already been utilized in several approaches based on active contour models which separate the mean and/or variance inside and outside the evolving contour. We generalize these methods by proposing a new technique for performing segmentation by separating the entire probability distributions of the regions inside and outside the contour using the Bhattacharyya metric. In particular, this allows for segmenting objects with multimodal probability distributions (on the space of tensors). We demonstrate the effectiveness of our algorithm by segmenting various textured images using the structure tensor. A level set based scheme is proposed to implement the curve flow evolution equation.
Shapley meets uniform: An axiomatic framework for attribution in online advertising
Authors: Omar Besbes, Antoine Désir, Vineet Goyal, Garud Iyengar, Raghav Singal
Venue: Conference WWW 2019
Year: 2019

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions including emails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, there is no formal justification for them and many of these fail even in simple canonical settings. The main contribution in this work is to develop an axiomatic framework for attribution in online advertising. In particular, we consider a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We propose a novel attribution metric, that we refer to as counterfactual adjusted Shapley value, which inherits the desirable properties of the traditional Shapley value. Furthermore, we establish that this metric coincides with an adjusted “unique-uniform” attribution scheme. This scheme is efficiently computable and implementable and can be interpreted as a correction to the commonly used uniform attribution scheme.
Reciprocity in social networks with capacity constraints
Authors: Bo Jiang, Zhi-Li Zhang, Don Towsley
Venue: Conference KDD 2015
Year: 2015

Directed links -- representing asymmetric social ties or interactions (e.g., "follower-followee") -- arise naturally in many social networks and other complex networks, giving rise to directed graphs (or digraphs) as basic topological models for these networks. Reciprocity, defined for a digraph as the percentage of edges with a reciprocal edge, is a key metric that has been used in the literature to compare different directed networks and provide "hints" about their structural properties: for example, are reciprocal edges generated randomly by chance or are there other processes driving their generation? In this paper we study the problem of maximizing achievable reciprocity for an ensemble of digraphs with the same prescribed in- and out-degree sequences. We show that the maximum reciprocity hinges crucially on the in- and out-degree sequences, which may be intuitively interpreted as constraints on some "social capacities" of nodes and impose fundamental limits on achievable reciprocity. We show that it is NP-complete to decide the achievability of a simple upper bound on maximum reciprocity, and provide conditions for achieving it. We demonstrate that many real networks exhibit reciprocities surprisingly close to the upper bound, which implies that users in these social networks are in a sense more "social" than suggested by the empirical reciprocity alone in that they are more willing to reciprocate, subject to their "social capacity" constraints. We find some surprising linear relationships between empirical reciprocity and the bound. We also show that a particular type of small network motifs that we call 3-paths are the major source of loss in reciprocity for real networks.
Affine- Invariant Contour Tracking with Automatic Control of Spatiotemporal Scale
Authors: Andrew Blake 0001, Rupert W. Curwen, Andrew Zisserman
Venue: Conference ICCV 1993
Year: 1993

A framework for visual tracking of contours is presented, based on a synthesis of elastic models, stochastic filtering, and geometric invariance. Flexibly coupled curve templates implement soft prior assumptions about shape. Affine invariance, built into the flexible coupling, ensures that the affine deformations that arise naturally from image projection are favored. The stochastic basis of the framework is shown to result naturally in an automatic mechanism for the control of spatiotemporal scale.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Integration of Quantitative and Qualitative Techniques for Deformable Model Fitting from Orthographic, Perspective, and Stereo Projections
Authors: Dimitris N. Metaxas, Sven J. Dickinson
Venue: Conference ICCV 1993
Year: 1993

The authors synthesize a new approach to 3-D object shape recovery by integrating qualitative shape recovery techniques and quantitative physics-based shape estimation techniques. They first use qualitative shape recovery and recognition techniques to provide strong fitting constraints on physics-based deformable model recovery techniques. Previously developed techniques of fitting deformable models to occluding image contours are then extended to the case of image data captured under general orthographic, perspective, and stereo projections. Experimental results are presented to illustrate the shape recovery approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Experiments on using semantic distances between words in image caption retrieval
Authors: Alan F. Smeaton, Ian Quigley
Venue: Conference SIGIR 1996
Year: 1996

Traditional approaches to information retrieval are based upon representing a user&apos;s query as a bag of query terms and a document as a bag of index terms and computing a degree of similarity between the two based on the overlap or number of query terms in common between them. Our long-term approach to IR applications is based upon precomputing semantically-based word-word similarities, work which is described elsewhere, and using these as part of the document-query similarity measure. A basic premise of our word-to-word similarity measure is that the input to this computation is the correct or intended word sense but in information retrieval applications, automatic and accurate word sense disambiguation remains an unsolved problem. In this paper we describe our first successful application of these ideas to an information retrieval application, specifically the indexing and retrieval of captions describing the content of images. We have hand-captioned 2714 images and to circumvent, fo...
Gait recognition from time-normalized joint-angle trajectories in the walking plane
Authors: Rawesak Tanawongsuwan, Aaron F. Bobick
Venue: Conference CVPR 2001
Year: 2001

This paper demonstrates gait recognition using only the trajectories of lower body joint angles projected into the walking plane. For this work, we begin with the position of 3D markers as projected into the sagittal or walking plane. We show a simple method for estimating the planar offsets between the markers and the underlying skeleton and joints; given these offsets we compute the joint angle trajectories. To compensate for systematic temporal variations from one instance to the next-predominantly distance and speed of walk-we fix the number of footsteps and time-normalize the trajectories by a variance compensated time warping. We perform recognition on two walking databases of 18 people (over 150 walk instances) using simple nearest neighbor algorithm with Euclidean distance as a measurement criteria. We also use the expected confusion metric as a means to estimate how well joint-angle signals will perform in a larger population.
Price prediction and insurance for online auctions
Authors: Rayid Ghani
Venue: Conference KDD 2005
Year: 2005

Online auctions are generating a new class of fine-grained data about online transactions. This data lends itself to a variety of applications and services that can be provided to both buyers and sellers in online marketplaces. We collect data from online auctions and use several classification algorithms to predict the probable-end prices of online auction items. This paper describes the feature extraction and selection process, and several machine learning formulations of the price prediction problem. As a prototype application, we developed Auction Price Insurance that uses the predicted end-price to offer price insurance to sellers in online auctions. We define Price Insurance as a service that offers insurance to auction sellers that guarantees a price for their goods, for an appropriate premium. If the item sells for less than the insured price, the seller is reimbursed for the difference. We show that our price prediction techniques are accurate enough to offer price insurance as a profitable business. While this paper deals specifically with online auctions, we believe that this is an interesting case study that applies to dynamic markets where the price of the goods is variable and is affected by both internal and external factors that change over time.