Diffusion models have recently emerged as powerful tools for missing data imputation by modeling the joint distribution of observed and unobserved variables. However, existing methods, typically based on stochastic denoising diffusion probabilistic models (DDPMs), suffer from high inference latency and variable outputs, limiting their applicability in real-world tabular settings. To address these deficiencies, we present in this paper MissDDIM, a conditional diffusion framework that adapts Denoising Diffusion Implicit Models (DDIM) for tabular imputation. While stochastic sampling enables diverse completions, it also introduces output variability that complicates downstream processing. MissDDIM replaces this with a deterministic, non-Markovian sampling path, yielding faster and more consistent imputations. To better leverage incomplete inputs during training, we introduce a self-masking strategy that dynamically constructs imputation targets from observed features-enabling robust conditioning without requiring fully observed data. Experiments on five benchmark datasets demonstrate that MissDDIM matches or exceeds the accuracy of state-of-the-art diffusion models, while significantly improving inference speed and stability. These results highlight the practical value of deterministic diffusion for real-world imputation tasks.

Recommendation systems are now an integral part of our daily lives. We rely on them for tasks such as discovering new movies, finding friends on social media, and connecting job seekers with relevant opportunities. Given their vital role, we must ensure these recommendations are free from societal stereotypes. Therefore, evaluating and addressing such biases in recommendation systems is crucial. Previous work evaluating the fairness of recommended items fails to capture certain nuances as they mainly focus on comparing performance metrics for different sensitive groups. In this paper, we introduce a set of comprehensive metrics for quantifying gender bias in recommendations. Specifically, we show the importance of evaluating fairness on a more granular level, which can be achieved using our metrics to capture gender bias using categories of recommended items like genres for movies. Furthermore, we show that employing a category-aware fairness metric as a regularization term along with the main recommendation loss during training can help effectively minimize bias in the models' output. We experiment on three real-world datasets, using five baseline models alongside two popular fairness-aware models, to show the effectiveness of our metrics in evaluating gender bias. Our metrics help provide an enhanced insight into bias in recommended items compared to previous metrics. Additionally, our results demonstrate how incorporating our regularization term significantly improves the fairness in recommendations for different categories without substantial degradation in overall recommendation performance.

Abstract In many critical machine learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood/tree/kais

In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages, commonly known as distribution shift. This paper outlines different mechanisms for handling two main types of distribution shifts: (i)Covariate shift:where the value of features or covariates change between train and test data, and (ii)Concept/Semantic-shift:where model experiences shift in the concept learned during training due to emergence of novel classes in the test phase. We sum up our contributions in three folds. First, we formalize distribution shifts, recite on how the conventional method fails to handle them adequately and urge for a model that can simultaneously perform better in all types of distribution shifts. Second, we discuss why handling distribution shifts is important and provide an extensive review of the methods and techniques that have been developed to detect, measure, and mitigate the effects of these shifts. Third, we discuss the current state of distribution shift handling mechanisms and propose future research directions in this area. Overall, we provide a retrospective synopsis of the literature in the distribution shift, focusing on OOD data that had been overlooked in the existing surveys.

Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment Classification

In many critical Machine Learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently Outlier Exposure (OE) based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art (S.O.T.A) methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one’s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user’s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized , that recommends any item consistent with the user critiques; and (ii) personalized , which leverages a user’s past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system’s performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems.

Social media platforms such as Twitter or StockTwits are widely used for sharing stock market opinions between investors, traders, and entrepreneurs. Empirically, previous work has shown that the content posted on these social media platforms can be leveraged to predict various aspects of stock market performance. Nonetheless, actors on these social media platforms may not always have altruistic motivations and may instead seek to influence stock trading behavior through the (potentially misleading) information they post. While a lot of previous work has sought to analyze how social media can be used to predict the stock market, there remain many questions regarding the quality of the predictions and the behavior of active users on these platforms. To this end, this article seeks to address a number of open research questions: Which social media platform is more predictive of stock performance? What posted content is actually predictive, and over what time horizon? How does stock market posting behavior vary among different users? Are all users trustworthy or do some user’s predictions consistently mislead about the true stock movement? To answer these questions, we analyzed data from Twitter and StockTwits covering almost 5 years of posted messages spanning 2015 to 2019. The results of this large-scale study provide a number of important insights among which we present the following: (i) StockTwits is a more predictive source of information than Twitter, leading us to focus our analysis on StockTwits; (ii) on StockTwits, users’ self-labeled sentiments are correlated with the stock market but are only slightly predictive in aggregate over the short-term; (iii) there are at least three clear types of temporal predictive behavior for users over a 144 days horizon: short, medium, and long term; and (iv) consistently incorrect users who are reliably wrong tend to exhibit what we conjecture to be “botlike” post content and their removal from the data tends to improve stock market predictions from self-labeled content.

This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network with n disjoint output layers that do not constraint each other, (iii) may output predictions that are often inconsistent with the taxonomy in place, and (iv) have often a fixed value of n. Specifically, we propose a model agnostic output layer that embeds the taxonomy and that can be combined with any model. Our proposed output layer implements a top-down divide-and-conquer strategy through a masking mechanism to enforce that predictions comply with the embedded hierarchy structure. Focusing on image classification, we evaluate the performance of our proposed output layer on three different datasets, each with a three-level hierarchical structure. Experiments on these datasets show that our proposed mask-based output layer allows to improve several multi-level hierarchical classification models using various performance metrics.

This paper presents Marine-tree, a large-scale hierarchical annotated dataset for marine organism classification. Marine-tree contains more than 160k annotated images divided into 60 classes organised in a hierarchy-tree structure using an adapted CATAMI (Collaborative and Automated Tools for the Analysis of Marine Imagery and video) classification scheme. Images were meticulously collected by scuba divers using the RLS (Reef Life Survey) methodology and later annotated by experts in the field. We also propose a hierarchical loss function that can be applied to any multi-level hierarchical classification model, which takes into account the parent-child relationship between predictions and uses it to penalize inconsistent predictions. Experimental results demonstrate thatMarine-tree and the proposed hierarchical loss function are a good contribution for both research in underwater imagery and hierarchical classification.

In this paper we introduce Jarvis, a context-as-a-service mobile tool, which enables context-aware data collection, service discovery, and computer-aided situational awareness through a conversational User Interface (UI). At the core of Jarvis are two main components: (i) a voice-based UI to translate speech to Context Definition and Query Language (Speech-to-CDQL), and (ii) an operational component called Context-as-a-Service (CoaaS), which enables smart things and IoT silos to discover, validate and share relevant and dependable context. The UI is based on two machine learning models: a Speech-to-Text model and a Text-to-CDQL model based on an encoder-decoder architecture. Jarvis is developed as a mobile application that allows people with different backgrounds to interact with various IoT devices. Our demo shows how easy Jarvis can be used for context-aware data collection and to interact with diverse objects in a smart home environment through voice.

Online recommendation systems are prone to create filter bubbles, whereby users are only recommended content narrowly aligned with their historical interests. In the case of media recommendation, this can reinforce political polarization by recommending topical content (e.g., on the economy) at one extreme end of the political spectrum even though this topic has broad coverage from multiple political viewpoints that would provide a more balanced and informed perspective for the user. Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1)~MMR directly sacrifices relevance for diversity, (2)~MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3)~MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). We then modulate the latent embeddings of user preferences in a state-of-the-art VAE-based recommender system to diversify along the targeted dimension while preserving topical relevance across orthogonal dimensions. Our experiments show that our Targeted Diversification VAE-based Collaborative Filtering (TD-VAE-CF) methodology better preserves relevance of content to user preferences across a range of diversification levels in comparison to both untargeted and targeted variations of Maximum Marginal Relevance (MMR); TD-VAE-CF is also much more computationally efficient than the post-hoc re-ranking approach of MMR.

Alzheimer's disease (AD) is an irreversible and progressive disorder where a large number of brain cells and their connections degenerate and die, eventually destroy the memory and other important mental functions that affect memory, thinking, language, judgment, and behavior. Not a single test can effectively determine AD; however, CT and magnetic resonance imaging (MRI) can be used to observe the decrease in size of different areas (mainly temporal and parietal lobes). This paper proposes an integrative deep ensemble learning framework to obtain better predictive performance for AD diagnosis. Unlike DenseNet, we present a multiresolutional ensemble PartialNet tailored to Alzheimer detection using brain MRIs. PartialNet incorporates the properties of identity mappings, diversified depth as well as deep supervision, thus, considers feature reuse that in turn results in better learning. Additionally, the proposed ensemble PartialNet demonstrates better characteristics in terms of vanishing gradient, diminishing forward flow with better training time, and a low number of parameters compared with DenseNet. Experiments performed on benchmark AD neuroimaging initiative data set that showed considerable performance gain (2 + % ↑ $\uparrow $ ) and (1.2 + % ↑ $\uparrow $ ) for multiclass and binary class in AD detection in comparison to state-of-the-art methods.

A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.

In the era of open data, a single data source rarely contains all of the attributes we need for inference in specific applications. For example, a marketing department may aim to integrate retailer-specific purchase data with separate demographic data for purposes of targeted advertising - a capability not possible with either dataset alone. In this work, we address two key desiderata of an automated framework for probabilistic data integration over multiple data sources: (1) we require that each relational data source share at least one attribute with another relational data source, but we do not require these attributes to be foreign keys (e.g., attributes such as gender, age, and postal code are not foreign keys because they do not uniquely identify individuals in a data source) and (2) we require inference to be probabilistic to reflect inherent uncertainty in population-level predictions given the absence of foreign keys. While some frameworks such as Probabilistic Relational Models (PRMs) address point (2), they do not address point (1) since they rely on foreign keys to link tables. To achieve both desiderata simultaneously, we develop an automated framework to construct Bayesian networks for data integration capable of answering any probabilistic query spanning the attributes of multiple relational data sources. We demonstrate that our framework is able to closely approximate the inference of a global Bayesian network over a single relation that has been projected onto multiple local relations and further investigate properties of local relations such as the number of shared attributes and their cardinality to understand how these properties affect the quality of inference.

Geo-temporal visualization of Twitter search results is a challenging task since the simultaneous display of all matching tweets would result in a saturated and unreadable display. In such settings, clustering search results can assist users to scan only a few coherent groups of related tweets rather than many individual tweets. However, in practice, the use of unsupervised clustering methods such as K -Means does not necessarily guarantee that the clusters themselves are relevant. Therefore, we develop a novel method of relevance-driven clustering for visual information retrieval to supply users with highly relevant clusters representing different information perspectives of their queries. We specifically propose a Visual Twitter Information Retrieval (Viz-TIR) tool for relevance-driven clustering and ranking of Twitter search results. At the heart of Viz-TIR is a fast greedy algorithm that optimizes an approximation of an expected F1-Score metric to generate these clusters. We demonstrate its effectiveness w.r.t. K -Means and a baseline method that shows all top matching results on a scenario related to searching natural disasters in US-based Twitter data spanning 2013 and 2014. Our demo shows that Viz-TIR is easy to use and more precise in extracting geo-temporally coherent clusters given search queries in comparison to K-Means, thus aiding the user in visually searching and browsing social network content. Overall, we believe this work enables new opportunities for the synthesis of information retrieval as well as combined relevance and display-aware optimization techniques to support query-adaptive visual information exploration interfaces.

Supervised topic classifiers for Twitter and other media sources are important in a variety of long-term topic tracking tasks. Unfortunately, over long periods of time, features that are predictive during the training period may prove ephemeral and fail to generalize to prediction at future times. For example, if we trained a classifier to identify tweets concerning the topic of “Celebrity Death”, individual celebrity names and terms associated with these celebrities such as “Nelson Mandela” or “South Africa” would prove to be temporally unstable since they would not generalize over long periods of time; in contrast, terms like “RIP” (rest in peace) would prove to be temporally stable predictors of this topic over long periods of time. In this paper, we aim to design supervised learning methods for Twitter topic classifiers that are capable of automatically downweighting temporally unstable features to improve future generalization. To do this, we first begin with an oracular approach that chooses temporally stable features based on knowledge of both train and test data labels. We then search for feature metrics evaluated on only the training data that are capable of recovering the temporally stable features identified by our oracular definition. We next embed the top-performing metric as a temporal stability regularizer in logistic regression with the important property that the overall training objective retains convexity, hence enabling a globally optimal solution. Finally, we train our topic classifiers on 6 Twitter topics over roughly one year of data and evaluate on the following year of data, showing that logistic regression with our temporal stability regularizer generally outperforms logistic regression without such regularization across the full precision-recall continuum. Overall, these results establish a novel regularizer for training long-term temporally stable topic classifiers for Twitter and beyond.

Variational Autoencoder (VAE) based methods for Collaborative Filtering (CF) demonstrate remarkable performance for one-class (implicit negative) recommendation tasks by extending autoencoders with relaxed but tractable latent distributions. Explicitly modeling a latent distribution over user preferences allows VAEs to learn user and item representations that not only reproduce observed interactions, but also generalize them by leveraging learning from similar users and items. Unfortunately, VAE-CF can exhibit suboptimal learning properties; e.g., VAE-CFs will increase their prediction confidence as they receive more preferences per user, even when those preferences may vary widely and create ambiguity in the user representation. To address this issue, we propose a novel Queryable Variational Autoencoder (Q-VAE) variant of the VAE that explicitly models arbitrary conditional relationships between observations. The proposed model appropriately increases uncertainty (rather than reduces it) in cases where a large number of user preferences may lead to an ambiguous user representation. Our experiments on two benchmark datasets show that the Q-VAE generally performs comparably or outperforms VAE-based recommenders as well as other state-of-the-art approaches and is generally competitive across the user preference density spectrum, where other methods peak for certain preference density levels.

We explore in this paper automatic biological sequence type classification for records in biological sequence databases. The sequence type attribute provides important information about the nature of a sequence represented in a record, and is often used in search to filter out irrelevant sequences. However, the sequence type attribute is generally a non-mandatory free-text field, and thus it is subject to many errors including typos, mis-assignment, and non-assignment. In GenBank, this problem concerns roughly 18% of records, an alarming number that should worry the biocuration community. To address this problem of automatic sequence type classification, we propose the use of literature associated to sequence records as an external source of knowledge that can be leveraged for the classification task. We define a set of literature-based features and train a machine learning algorithm to classify a record into one of six primary sequence types. The main intuition behind using the literature for this task is that sequences appear to be discussed differently in scientific articles, depending on their type. The experiments we have conducted on the PubMed Central collection show that the literature is indeed an effective way to address this problem of sequence type classification. Our classification method reached an accuracy of 92.7%, and substantially outperformed two baseline approaches used for comparison.

In this paper, we investigate the influence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search.

In this paper, we introduce LAICOS, a social Web search engine as a contribution to the growing area of Social Information Retrieval (SIR). Social information and personalization are at the heart of LAICOS. On the one hand, the social context of documents is added as a layer to their textual content traditionally used for indexing to provide Personalized Social Document Representations. On the other hand, the social context of users is used for the query expansion process using the Personalized Social Query Expansion framework (PSQE) proposed in our earlier works. We describe the different components of the system while relying on social bookmarking systems as a source of social information for personalizing and enhancing the IR process. We show how the internal structure of indexes as well as the query expansion process operated using social information.

We present in this paper a contribution to IR modeling by proposing a new ranking function called SoPRa that considers the social dimension of the Web. This social dimension is any social information that surrounds documents along with the social context of users. Currently, our approach relies on folksonomies for extracting these social contexts, but it can be extended to use any social meta-data, e.g. comments, ratings, tweets, etc. The evaluation performed on our approach shows its benefits for personalized search.

In this paper, we present a contribution to IR modeling. We propose an approach that computes on the fly, a Personalized Social Document Representation (PSDR) of each document per user based on his social activities. The PSDRs are used to rank documents with respect to a query. This approach has been intensively evaluated on a large public dataset, showing significant benefits for personalized search.

We propose a new approach for social and personalized query expansion using social structures in the Web 2.0. While focusing on social tagging systems, the proposed approach considers (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user profile, and (iii) on the fly, a strategy for expanding user queries. The proposed approach has been evaluated using a large dataset crawled from del.icio.us.

In order to measure and assess the quality of GIS, there exist a sparse offer of tools, providing specific functions with their own interest but are not sufficient to deal with broader user's requirements. Interoperability of these tools remains a technical challenge because of the heterogeneity of their models and access patterns. On the other side, quality analysts require more and more integration facilities that allow them to consolidate and aggregate multiple quality measures acquired from different observations or data sources, in using/combining seamlessly different quality tools. Clearly, there is a gap between users's requirements and the spatial data quality market. This demo paper will illustrate GQBox, a geographic quality (tool)box. GQBox supplies a standards-based generic meta model that supports the definition of quality goals and metrics, and it provides a service-based infrastructure that allows interoperability among several quality tools.