Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects

We present experiences and lessons learned from increasing data readiness of heterogeneous data for artificial intelligence projects using visual analysis methods. Increasing the data readiness level involves understanding both the data as well as the context in which it is used, which are challenges well suitable to visual analysis. For this purpose, we contribute a mapping between data readiness aspects and visual analysis techniques suitable for different data types. We use the defined mapping to increase data readiness levels in use cases involving time-varying data, including numerical, categorical, and text. In addition to the mapping, we extend the data readiness concept to better take aspects of the task and solution into account and explicitly address distribution shifts during data collection time. We report on our experiences in using the presented visual analysis techniques to aid future artificial intelligence projects in raising the data readiness level.

Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation

Data Fairness is a crucial topic due to the recent wide usage of AI powered applications. Most of the real-world data is filled with human or machine biases and when those data are being used to train AI models, there is a chance that the model will reflect the bias in the training data. Existing bias-mitigating generative methods based on GANs, Diffusion models need in-processing fairness objectives and fail to consider computational overhead while choosing computationally-heavy architectures, which may lead to high computational demands, instability and poor optimization performance. To mitigate this issue, in this work, we present a fair data generation technique based on knowledge distillation, where we use a small architecture to distill the fair representation in the latent space. The idea of fair latent space distillation enables more flexible and stable training of Fair Generative Models (FGMs). We first learn a syntax-agnostic (for any data type) fair representation of the data, followed by distillation in the latent space into a smaller model. After distillation, we use the distilled fair latent space to generate high-fidelity fair synthetic data. While distilling, we employ quality loss (for fair distillation) and utility loss (for data utility) to ensure that the fairness and data utility characteristics remain in the distilled latent space. Our approaches show a 5%, 5% and 10% rise in performance in fairness, synthetic sample quality and data utility, respectively, than the state-of-the-art fair generative model.

Improving Relational Database Interactions with Large Language Models: Column Descriptions and Their Impact on Text-to-SQL Performance

Relational databases often suffer from uninformative descriptors of table contents, such as ambiguous columns and hard-to-interpret values, impacting both human users and Text-to-SQL models. This paper explores the use of large language models (LLMs) to generate informative column descriptions as a semantic layer for relational databases. Using the BIRD-Bench development set, we created \textsc{ColSQL}, a dataset with gold-standard column descriptions generated and refined by LLMs and human annotators. We evaluated several instruction-tuned models, finding that GPT-4o and Command R+ excelled in generating high-quality descriptions. Additionally, we applied an LLM-as-a-judge to evaluate model performance. Although this method does not align well with human evaluations, we included it to explore its potential and to identify areas for improvement. More work is needed to improve the reliability of automatic evaluations for this task. We also find that detailed column descriptions significantly improve Text-to-SQL execution accuracy, especially when columns are uninformative. This study establishes LLMs as effective tools for generating detailed metadata, enhancing the usability of relational databases.

FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability

We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI). FairX enables users to train benchmarking bias-removal models and evaluate their fairness using a wide array of fairness metrics, data utility metrics, and generate explanations for model predictions, all within a unified framework. Existing benchmarking tools do not have the way to evaluate synthetic data generated from fair generative models, also they do not have the support for training fair generative models either. In FairX, we add fair generative models in the collection of our fair-model library (pre-processing, in-processing, post-processing) and evaluation metrics for evaluating the quality of synthetic fair data. This version of FairX supports both tabular and image datasets. It also allows users to provide their own custom datasets. The open-source FairX benchmarking package is publicly available at https://github.com/fahim-sikder/FairX.

Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models

Many of today's data is time-series data originating from various sources, such as sensors, transaction systems, or production systems. Major challenges with such data include privacy and business sensitivity. Generative time-series models have the potential to overcome these problems, allowing representative synthetic data, such as people's movement in cities, to be shared openly and be used to the benefit of society at large. However, contemporary approaches are limited to prohibitively short sequences and small scales. Aside from major memory limitations, the models generate less accurate and less representative samples the longer the sequences are. This issue is further exacerbated by the lack of a comprehensive and accessible benchmark. Furthermore, a common need in practical applications is what-if analysis and dynamic adaptation to data distribution changes, for usage in decision making and to manage a changing world: What if this road is temporarily blocked or another road is added? The focus of this paper is on mobility data, such as people's movement in cities, requiring all these issues to be addressed. To this end, we propose a transformer-based diffusion model, TDDPM, for time-series which outperforms and scales substantially better than state-of-the-art. This is evaluated in a new comprehensive benchmark across several sequence lengths, standard datasets, and evaluation measures. We also demonstrate how the model can be conditioned on a prior over spatial occupancy frequency information, allowing the model to generate mobility data for previously unseen environments and for hypothetical scenarios where the underlying road network and its usage changes. This is evaluated by training on mobility data from part of a city. Then, using only aggregate spatial information as prior, we demonstrate out-of-distribution generalization to the unobserved remainder of the city.

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. We conduct extensive experiments using the MIMIC-III database. Our results demonstrate that Bt-GAN achieves SOTA accuracy while significantly improving fairness and minimizing bias amplification. We also perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and constitutes a foundational capability of autonomous systems operating in most of the real world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperform state-of-the-art planners in dynamic and large-scale environments. DAEP is shown to be more effective at both exploration and collision avoidance.

TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers

The generation of high-quality, long-sequenced time-series data is essential due to its wide range of applications. In the past, standalone Recurrent and Convolutional Neural Network-based Generative Adversarial Networks (GAN) were used to synthesize time-series data. However, they are inadequate for generating long sequences of time-series data due to limitations in the architecture. Furthermore, GANs are well known for their training instability and mode collapse problem. To address this, we propose TransFusion, a diffusion, and transformers-based generative model to generate high-quality long-sequence time-series data. We have stretched the sequence length to 384, and generated high-quality synthetic data. To the best of our knowledge, this is the first study that has been done with this long-sequence length. Also, we introduce two evaluation metrics to evaluate the quality of the synthetic data as well as its predictive characteristics. We evaluate TransFusion with a wide variety of visual and empirical metrics, and TransFusion outperforms the previous state-of-the-art by a significant margin.

Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms

This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generate feasible solutions of higher quality more quickly. Our results indicate that our approach could be a promising future method for constructing such heuristics.

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.