Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop methods requires technical proficiency. Despite existing toolkits for model understanding and analysis, options to integrate human feedback are still limited. We propose IFAN, a framework for real-time explanation-based interaction with NLP models. Through IFAN's interface, users can provide feedback to selected model explanations, which is then integrated through adapter layers to align the model with human rationale. We show the system to be effective in debiasing a hate speech classifier with minimal impact on performance. IFAN also offers a visual admin system and API to manage models (and datasets) as well as control access rights. A demo is live at https://ifan.ml.

We present a novel machine learning-based approach to solving bilevel programs that involve a large number of independent followers, which as a special case include two-stage stochastic programming. We propose an optimization model that explicitly considers a sampled subset of followers and exploits a machine learning model to estimate the objective values of unsampled followers. Unlike existing approaches, we embed machine learning model training into the optimization problem, which allows us to employ general follower features that can not be represented using leader decisions. We prove bounds on the optimality gap of the generated leader decision as measured by the original objective function that considers the full follower set. We then develop follower sampling algorithms to tighten the bounds and a representation learning approach to learn follower features, which can be used as inputs to the embedded machine learning model. Using synthetic instances of a cycling network design problem, we compare the computational performance of our approach versus baseline methods. Our approach provides more accurate predictions for follower objective values, and more importantly, generates leader decisions of higher quality. Finally, we perform a real-world case study on cycling infrastructure planning, where we apply our approach to solve a network design problem with over one million followers. Our approach presents favorable performance compared to the current cycling network expansion practices.

The vast majority of techniques to train fair models require access to the protected attribute (e.g., race, gender), either at train time or in production. However, in many important applications this protected attribute is largely unavailable. In this paper, we develop methods for measuring and reducing fairness violations in a setting with limited access to protected attribute labels. Specifically, we assume access to protected attribute labels on a small subset of the dataset of interest, but only probabilistic estimates of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding) for the rest of the dataset. With this setting in mind, we propose a method to estimate bounds on common fairness metrics for an existing model, as well as a method for training a model to limit fairness violations by solving a constrained non-convex optimization problem. Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity. We provide an empirical illustration of our methods using voting data. First, we show our measurement method can bound the true disparity up to 5.5x tighter than previous methods in these applications. Then, we demonstrate that our training technique effectively reduces disparity while incurring lesser fairness-accuracy trade-offs than other fair optimization methods with limited access to protected attributes.

This paper considers the task of linear regression with shuffled labels, i.e., $\mathbf Y = \mathbf \Pi \mathbf X \mathbf B + \mathbf W$, where $\mathbf Y \in \mathbb R^{n\times m}, \mathbf Pi \in \mathbb R^{n\times n}, \mathbf X\in \mathbb R^{n\times p}, \mathbf B \in \mathbb R^{p\times m}$, and $\mathbf W\in \mathbb R^{n\times m}$, respectively, represent the sensing results, (unknown or missing) corresponding information, sensing matrix, signal of interest, and additive sensing noise. Given the observation $\mathbf Y$ and sensing matrix $\mathbf X$, we propose a one-step estimator to reconstruct $(\mathbf \Pi, \mathbf B)$. From the computational perspective, our estimator's complexity is $O(n^3 + np^2m)$, which is no greater than the maximum complexity of a linear assignment algorithm (e.g., $O(n^3)$) and a least square algorithm (e.g., $O(np^2 m)$). From the statistical perspective, we divide the minimum $snr$ requirement into four regimes, e.g., unknown, hard, medium, and easy regimes; and present sufficient conditions for the correct permutation recovery under each regime: $(i)$ $snr \geq \Omega(1)$ in the easy regime; $(ii)$ $snr \geq \Omega(\log n)$ in the medium regime; and $(iii)$ $snr \geq \Omega((\log n)^{c_0}\cdot n^{{c_1}/{srank(\mathbf B)}})$ in the hard regime ($c_0, c_1$ are some positive constants and $srank(\mathbf B)$ denotes the stable rank of $\mathbf B$). In the end, we also provide numerical experiments to confirm the above claims.

Statistical divergences quantify the difference between probability distributions, thereby allowing for multiple uses in machine-learning. However, a fundamental challenge of these quantities is their estimation from empirical samples since the underlying distributions of the data are usually unknown. In this work, we propose a divergence inspired by the Jensen-Shannon divergence which avoids the estimation of the probability density functions. Our approach embeds the data in an reproducing kernel Hilbert space (RKHS) where we associate data distributions with uncentered covariance operators in this representation space. Therefore, we name this measure the representation Jensen-Shannon divergence (RJSD). We provide an estimator from empirical covariance matrices by explicitly mapping the data to an RKHS using Fourier features. This estimator is flexible, scalable, differentiable, and suitable for minibatch-based optimization problems. Additionally, we provide an estimator based on kernel matrices without an explicit mapping to the RKHS. We provide consistency convergence results for the proposed estimator. Moreover, we demonstrate that this quantity is a lower bound on the Jensen-Shannon divergence, leading to a variational approach to estimate it with theoretical guarantees. We leverage the proposed divergence to train generative networks, where our method mitigates mode collapse and encourages samples diversity. Additionally, RJSD surpasses other state-of-the-art techniques in multiple two-sample testing problems, demonstrating superior performance and reliability in discriminating between distributions.

The Mars rover Perseverance captured a dust devil moving across the rim of a crater. A new compilation of images from NASA's Perseverance Mars Rover appears to show a dust devil twister dancing across the surface of the red planet, about 2.5 miles away from the six-wheeled geologist. NASA said only the lower portion of the twister was seen moving along the western rim of Mars' Jezero Crater, on Aug. 30, 2023. The images were snapped on Perseverance's 899th day on Martian soil. NASAs Mars Perseverance rover snapped several images of a dust devil moving across the Martian surface.

It took a while, and a rather bumpy false start with the Star Wars franchise (his Rogue One was plagued by rumours of studio interference and extensive reshoots), but with The Creator, the British director Gareth Edwards finally gets to make the sci-fi spectacular he was always destined to tackle. And with this ambitious, ideas-driven, expectation-subverting, man-versus-machines showdown, he has co-written and directed one of the finest original science-fiction films of recent years. It can be a little misleading, that word "original", when it comes to science fiction. At its most basic, it just refers to any picture that isn't part of an existing franchise or culled from a recognisable IP – be it a book, video game or television series. But very occasionally the word is fully earned, by a film so distinctive in its world-building, its aesthetic and its unexpected approach to well-worn themes that it becomes a definitive example of the genre.

To this end, vast amounts of data generated by various sources, including sensors and other monitoring systems, need to be effectively structured and represented in a way that can be easily understood and processed by both Artificial Intelligence (AI) systems and humans. The digitalisation of the wind energy sector is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle [2]. The digitalisation process encompasses solutions such as digital twins, decision support systems and AI systems, some of which need to still be developed, in order to contribute to reducing operation and maintenance costs, for increasing the amount of energy delivered, as well as for maximising the efficiency of wind energy systems. In this context, the term Knowledge-Based Systems (KBS) refers to AI systems that formalize knowledge as rules, logical expressions, and conceptualisations [3, 4]. Such systems can be realised as AI-enabled digital twins or decision support systems that rely on databases of knowledge (also referred to as knowledge bases or knowledge graphs), which contain machine-readable facts, rules, and logics about a domain of interest, to assist with problem-solving and decision-making [5].

Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret source code and natural language instructions. These emergent capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of LLMs in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents who have already adapted their curricula and assessments. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of LLMs on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving. Our aim is that this report will serve as a focal point for both researchers and practitioners who are exploring, adapting, using, and evaluating LLMs and LLM-based tools in computing classrooms.

An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings.