Fernando Diaz

AAAI Conference 2026 Conference Paper

RankList – a Listwise Preference Learning Framework for Predicting Subjective Preferences

• Abinay Reddy Naini
• Fernando Diaz
• Carlos Busso

Preference learning has gained significant attention in tasks involving subjective human judgments, such as speech emotion recognition (SER) and image aesthetic assessment. While pairwise frameworks such as RankNet offer robust modeling of relative preferences, they are inherently limited to local comparisons and struggle to capture global ranking consistency. To address these limitations, we propose RankList, a novel listwise preference learning framework that generalizes RankNet to structured list-level supervision. Our formulation explicitly models local and non-local ranking constraints within a probabilistic framework. The paper introduces a log-sum-exp approximation to improve training efficiency. We further extend RankList with skip-wise comparisons, enabling progressive exposure to complex list structures and enhancing global ranking fidelity. Extensive experiments demonstrate the superiority of our method across diverse modalities. On benchmark SER datasets (MSP-Podcast, IEMOCAP, BIIC Podcast), RankList achieves consistent improvements in Kendall's Tau and ranking accuracy compared to standard listwise baselines. We also validate our approach on aesthetic image ranking using the Artistic Image Aesthetics dataset, highlighting its broad applicability. Through ablation and cross-domain studies, we show that RankList not only improves in-domain ranking but also generalizes better across datasets. Our framework offers a unified, extensible approach for modeling ordered preferences in subjective learning scenarios.

NeurIPS Conference 2025 Conference Paper

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor

• Alexandra Olteanu
• Su Lin Blodgett
• Agathe Balayn
• Angelina Wang
• Fernando Diaz
• Flavio Calmon
• Margaret Mitchell
• Michael Ekstrand

In AI research and practice, rigor remains largely understood in terms of methodological rigor---such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception---in addition to a more expansive understanding of 1) methodological rigor---should include aspects related to 2) what background knowledge informs what to work on (epistemic rigor); 3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); 4) how clearly articulated the theoretical constructs under use are (conceptual rigor); 5) what is reported and how (reporting rigor); and 6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders.

NeurIPS Conference 2024 Conference Paper

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

• Haolun Wu
• Ofer Meshi
• Masrour Zoghi
• Fernando Diaz
• Xue Liu
• Craig Boutilier
• Maryam Karimzadehgan

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w. r. t. \ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.

NeurIPS Conference 2021 Conference Paper

Artsheets for Art Datasets

• Ramya Srinivasan
• Emily Denton
• Jordan Famularo
• Negar Rostamzadeh
• Fernando Diaz
• Beth Coleman

Machine learning (ML) techniques are increasingly being employed within a variety of creative domains. For example, ML tools are being used to analyze the authenticity of artworks, to simulate artistic styles, and to augment human creative processes. While this progress has opened up new creative avenues, it has also paved the way for adverse downstream effects such as cultural appropriation (e. g. , cultural misrepresentation, offense, and undervaluing) and representational harm. Many such concerning issues stem from the training data in ways that diligent evaluation can uncover, prevent, and mitigate. We posit that, when developing an arts-based dataset, it is essential to consider the social factors that influenced the process of conception and design, and the resulting gaps must be examined in order to maximize understanding of the dataset's meaning and future impact. Each dataset creator's decision produces opportunities, but also omissions. Each choice, moreover, builds on preexisting histories of the data's formation and handling across time by prior actors including, but not limited to, art collectors, galleries, libraries, archives, museums, and digital repositories. To illuminate the aforementioned aspects, we provide a checklist of questions customized for use with art datasets in order to help guide assessment of the ways that dataset design may either perpetuate or shift exclusions found in repositories of art data. The checklist is organized to address the dataset creator's motivation together with dataset provenance, composition, collection, pre-processing, cleaning, labeling, use (including data generation), distribution, and maintenance. Two case studies exemplify the value and application of our questionnaire.

EAAI Journal 2016 Journal Article

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

• Eneko Osaba
• Xin-She Yang
• Fernando Diaz
• Pedro Lopez-Garcia
• Roberto Carballedo

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student׳s t-test, the Holm׳s test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

IJCAI Conference 2016 Conference Paper

Real-Time Web Scale Event Summarization Using Sequential Decision Making

• Chris Kedzie
• Fernando Diaz
• Kathleen McKeown

We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web. Given an event of interest (e. g. Boston Marathon bombing), our system is able to filter the stream for relevance and produce a series of short text updates describing the event as it unfolds over time. Unlike previous work, our approach is able to jointly model the relevance, comprehensiveness, novelty, and timeliness required by time-sensitive queries. We demonstrate a 28. 3% improvement in summary F1 and a 43. 8% improvement in time-sensitive F1 metrics.

IJCAI Conference 2007 Conference Paper

• Fernando Diaz
• Donald Metzler

In machine translation, document alignment refers to finding correspondences between documents which are exact translations of each other. We define pseudo-alignment as the task of finding topical-as opposed to exact-correspondences between documents in different languages. We apply semisupervised methods to pseudo-align multilingual corpora. Specifically, we construct a topic-based graph for each language. Then, given exact correspondences between a subset of documents, we project the unaligned documents into a shared lower-dimensional space. We demonstrate that close documents in this lower-dimensional space tend to share the same topic. This has applications in machine translation and cross-lingual information analysis. Experimental results show that pseudo-alignment of multilingual corpora is feasible and that the document alignments produced are qualitatively sound. Our technique requires no linguistic knowledge of the corpus. On average when 10% of the corpus consists of exact correspondences, an on-topic correspondence occurs within the top 5 foreign neighbors in the lower-dimensional space while the exact correspondence occurs within the top 10 foreign neighbors in this this space. We also show how to substantially improve these results with a novel method for incorporating language-independent information.

YNIMG Journal 1998 Journal Article

Thallium 201 SPECT and F-18 fluorodeoxyglucose PET in the differential diagnosis of brain tumor recurrence vs radiation necrosis after permanent I-125 implantation

• Amgad Matter
• Geoffrey Barger
• Lucia Zamorano
• Laurie Caspar
• Marcelo DiCarli
• Lawrence Davis
• Fernando Diaz