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Amogh Mannekote, Adam Davies, Jina Kang, Kristy Elizabeth Boyer

Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. While recent studies show the promise of using large language models (LLMs) for simulating human behavior, such approaches have not gone beyond rudimentary proof-of-concept stages due to key limitations. First, LLMs are highly sensitive to minor prompt variations, raising doubts about their ability to generalize to new scenarios without extensive prompt engineering. Moreover, apparently successful outcomes can often be unreliable, either because domain experts unintentionally guide LLMs to produce expected results, leading to self-fulfilling prophecies; or because the LLM has encountered highly similar scenarios in its training data, meaning that models may not be simulating behavior so much as regurgitating memorized content. To address these challenges, we propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior. Testing this framework in a physics learning environment, we found that GPT-4 Turbo maintains calibrated behavior even as the underlying learner model changes, providing the first evidence that LLMs can be used to simulate realistic behaviors in open-ended interactive learning environments, a necessary prerequisite for useful LLM behavioral simulation.

Alon Oved, Segev Shlomov, Sergey Zeltyn, Nir Mashkif, Avi Yaeli

Predicting the next activity in an ongoing process is one of the most common tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and aid both in risk mitigation and strategic decision-making. Existing state-of-the-art AI models for BPM do not fully capitalize on available semantic information within process event logs. As current advanced AI-BPM systems provide semantically richer textual data, the need for new adequate models grows. To address this gap, we develop SNAP—a novel system that utilizes LLMs by constructing narratives and semantic contextual stories for historical event logs, which are then used to generate precise and actionable predictions for the ongoing process. SNAP was evaluated on six benchmark datasets, where it demonstrated significant performance improvements over eleven SOTA models, particularly on datasets with high levels of semantic content. This work showcases the potential of integrating LLMs in BPM and outlines a clear path toward future deployment, emphasizing the relevance and innovation of our approach within the broader AI application landscape.

Aditya Mohan, Andie Gomez-Patron, Matthew Pritchard, Hannah Kerner

Geologists seek to understand the relationship between volcanic unrest and eruptions by identifying subtle Volcanic Thermal Features (VTFs) in high-resolution satellite imagery. This analysis requires the careful curation of large databases of relevant volcanic thermal information. However, volcanic unrest is characterized by highly subtle thermal anomalies. Manual identification on a global scale is highly labor- and time-intensive. We propose Hotspotter: an end-to-end system to automatically detect subtle volcanic thermal anomalies in satellite images and derive relevant thermal statistics. Previous solutions for automated VTF detection have limited data size and geographic diversity. To accommodate an unprecedentedly large and diverse volcanic dataset, we propose an automated pipeline combining unsupervised anomaly detection with supervised classification to filter anomalous regions. Hotspotter gives 90% anomaly detection accuracy and robust generalization to new volcanoes. Our automated approach can accelerate scientists' search for VTFs to help identify relevant thermal precursors and enable more precise forecasts of global volcanic eruptions.

Xiaorui Liu

Machine learning techniques are notably vulnerable to natural or adversarial perturbations, which can lead to catastrophic failures with significant economic, ethical, and societal risks. In this New Faculty Highlight Talk, I will showcase my research on harnessing robust statistics to build robust and trustworthy AI systems. Specifically, I will highlight my research breakthroughs in graph learning (GNNs), large language models (LLMs), deep equilibrium models (DEQs), and general deep representation learning. These breakthroughs stem from a unified and principled robust statistics framework that incorporates robustness as the core inductive bias in deep learning architecture. This approach has enabled significant improvements in intrinsic robustness and generalization, even in complex and challenging environments. My research demonstrates the transformative potential of harnessing robust statistics in enhancing the robustness and trustworthiness of AI systems. Looking forward, I will continue to push this frontier by advocating the design of robustness-informed neural networks across various areas.

Aryan Deshwal

Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.

Erdem Bıyık

In many human-robot collaboration and multi-agent tasks, it is vital to model the partners and estimate their objectives to efficiently collaborate/interact with them. While learning from demonstrations is the most common approach for this, it is very data-hungry, which we cannot afford in many settings including robotics, and demonstrations are unreliable in a surprisingly large number of domains, including those we think humans perform reasonably well, e.g., driving. In this talk, I will start with introducing comparison-based feedback and explain why it does not suffer from most of the problems that demonstrations have, but is still data-hungry. To address this problem, I will propose comparative language based feedback and active learning techniques, which will result in (1) a new type of human feedback, and (2) an active querying algorithm that optimizes the information the AI agent will elicit from the human. I will conclude the talk by discussing what other types of human feedback exist, e.g., interventions or hand gestures, and how we can incorporate them into the existing learning algorithms.

Biplav Srivastava

In this blue sky paper, we seek to stimulate the research community to pursue important new as well as existing (unsolved) AI problems in the context of a challenging, often ignored, socio-sensitive application domain. We outline the key challenges in conducting elections credibly in leading democracies around the world today and identify our vision of a path forward with an overarching goal to increase voter participation with a two-pronged approach of AI-lead technological innovations and interdisciplinary community building. On the technology front, we envisage the need to transform Collation and Distribution of election information, and promote its Comprehensibility for users understanding and trust (CDC). On the community front, we need to invigorate the multi-disciplinary community consisting of, but not limited to, researchers in AI, security, journalism, political science, sociology, and business, to PROMote AI's Safe usage for Elections (PROMISE) with best-practices. This work is informed by our interdisciplinary research as well as experience in conducting three workshops at leading AI conferences and the AI Magazine special issue on AI and Elections.

Sarit Kraus, Kayla Boggess, Robert Kim, Bryan H. Choi, Lu Feng

This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.

Ron Van Bree, Diego Marcos, Ioannis N. Athanasiadis

Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.

Kaipeng Niu, Danni Xu, Bingjian Yang, Wenxuan Liu, Zheng Wang

Existing video fact-checking datasets often lack detailed evidence and explanations, compromising the reliability and interpretability of fact-checking methods. To address these gaps, we developed a novel dataset featuring comprehensive annotations for each news item, including veracity labels, the rationales behind these labels, and supporting evidence. This dataset significantly enhances models' ability to accurately identify and explain video content. We also present an explainable automatic framework 3MFact, utilizing Multi-role Multimodal Models for video Fact-checking. Our framework iteratively gathers and synthesizes online evidence to progressively determine the veracity label, generating three key outputs: veracity label, rationale, and supported evidence. We aim for this work to be a pioneering effort, providing robust support for the field of video fact-checking.

Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Scepanovic, Cynthia Rudin

Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Leveraging this new resource, we use a variety of variable importance techniques to robustly identify the most informative predictors across multiple health outcomes. We then develop an interpretable machine learning framework based on Generalized Additive Models (GAMs) and Multiscale Geographically Weighted Regression (MGWR) to analyze both local and global spatial dependencies of each variable on various health outcomes. Our findings identify NO2 as a global predictor for asthma, hypertension, and anxiety, alongside other outcome-specific predictors related to occupation, marriage, and vegetation. Regional analyses reveal local variations with air pollution and solar radiation, with notable shifts during COVID. This comprehensive approach provides actionable insights for addressing health disparities, and advocates for the integration of interpretable machine learning in public health.

Matthew Forshaw, Rachel Gray, Bridgett VonHoldt, Alexander Ochoa, Joshua M. Miller, Kristin E. Brzeski, Adalgisa Caccone, Evelyn L. Jensen

Captive breeding programs play a critical role in combating the ongoing biodiversity crisis by preserving the most endangered species and supporting reintroduction efforts. Maintaining the genetic health of captive populations requires careful management to prevent inbreeding and maximize the effective population size. Decisions about which males and females should be bred together are guided by the principle of minimizing relatedness between pairs. Methods to select breeding pairs are well developed, however, some species' ecology requires them to live in groups, and evaluating optimal groupings of multiple males and females that would be suitable to breed together is a more complex problem. Current computational tools to support the design of group-living captive breeding programs suffer from challenges of scalability and flexibility. In this paper we demonstrate the applicability of constraint programming (CP) approaches to optimize breeding groups to minimize relatedness. We present the example of the Galapagos giant tortoises as the test case used to develop our approach. Exploration of the needs of this captive breeding program has informed the development of our flexible approach to capture the constraints on viable captive breeding program design. Our findings have directly informed the implementation of new group configurations at the captive breeding centre. We further demonstrate that our approach is broadly applicable in other contexts through a second case study, providing multi-objective optimisation of a breeding program of canids. Through these case studies and an ablation study using synthetic datasets, we show that our constraint optimisation approach provides an expressive and generalizable means to support captive breeding program design, including scaling to large captive populations, which are currently intractable using current computational methods.

Majd Al Aawar, Srikar Mutnuri, Mansooreh Montazerin, Ajitesh Srivastava

During the COVID-19 pandemic, a major driver of new surges has been the emergence of new variants. When a new variant emerges in one or more countries, other nations monitor its spread in preparation for its potential arrival. The impact of the new variant and the timings of epidemic peaks in a country highly depend on when the variant arrives. The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence. Can we predict when a variant existing elsewhere will arrive in a given region? To address this question, we propose a variant-dynamics-informed Graph Neural Network (GNN) approach. First, we derive the dynamics of variant prevalence across pairs of regions (countries) that apply to a large class of epidemic models. The dynamics motivate the introduction of certain features in the GNN. We demonstrate that our proposed dynamics-informed GNN outperforms all the baselines, including the currently pervasive framework of Physics-Informed Neural Networks (PINNs). To advance research in this area, we introduce a benchmarking tool to assess a user-defined model's prediction performance across 87 countries and 36 variants.

Yun-Da Tsai, Ting-Yu Yen, Keng-Te Liao, Shou-De Lin

Converting different modalities into generalized text, which then serves as input prompts for large language models (LLMs), is a common approach for aligning multimodal models, particularly when pairwise data is limited. Text-centric alignment method leverages the unique properties of text as a modality space, transforming diverse inputs into a unified textual representation, thereby enabling downstream models to effectively interpret various modal inputs. This study evaluates the quality and robustness of multimodal representations in the face of noise imperfections, dynamic input order permutations, and missing modalities, revealing that current text-centric alignment methods can compromise downstream robustness. To address this issue, we propose a new text-centric adversarial training approach that significantly enhances robustness compared to traditional robust training methods and pre-trained multimodal foundation models. Our findings underscore the potential of this approach to improve the robustness and adaptability of multimodal representations, offering a promising solution for dynamic and real-world applications.

Binxia Xu, Antonis Bikakis, Daniel F.O. Onah, Andreas Vlachidis, Luke Dickens

Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the information processing similarities between AI and humans, may be able to achieve these same ends. While Representational alignment (RA) approaches measure similarity between the internal states of two systems, the associated data can be expensive and difficult to collect for human systems. In contrast, Behavioural alignment (BA) comparisons are cheaper and easier, but questions remain as to their sensitivity and reliability. We propose two new behavioural alignment metrics misclassification agreement which measures the similarity between the errors of two systems on the same instances, and class-level error similarity which measures the similarity between the error distributions of two systems. We show that our metrics correlate well with RA metrics, and provide complementary information to another BA metric, within a range of domains, and set the scene for a new approach to value alignment.

Raghav Gupta, Ryan Sullivan, Yunxuan Li, Samrat Phatale, Abhinav Rastogi

Multi-objective preference alignment of large language models (LLMs) is critical for developing AI systems that are more configurable, personalizable, helpful, and safe. However, optimizing model outputs to satisfy diverse objectives with variable weights at inference time for truly personalized models presents a significant challenge. Existing approaches are either computationally expensive to train or do not sufficiently steer model behaviors. This paper introduces the Multi-Objective Online DPO (MO-ODPO) algorithm, designed to robustly and efficiently align model behaviors with multiple, potentially conflicting human preferences. Our approach incorporates a prompt conditioning mechanism, allowing us to train a single preference-conditional policy, that can adapt to new preference combinations at inference. Experiments on two popular benchmarks show that MO-ODPO Pareto-dominates existing baselines while providing excellent inference-time steerability between diverse objectives.

Selin Chun, Daejin Choi, Taekyoung Kwon

Significant efforts have been made to analyze the political stance or bias in news articles, especially as political polarization intensifies over the years. Recent advancements in machine learning have enabled researchers to develop various bias prediction models, which typically learn features not only from the text of the news articles but also from external knowledge. However, when training these models, the political bias label assigned to a news article is often based solely on the news source which published it. This approach can be problematic, as a news outlet with a particular political stance might publish an article that reflects a different political perspective. To address this issue, we first identify distinct text patterns associated with specific news sources or publishers, that are minimally relevant to predicting the political bias of a news article. We then conduct comprehensive experiments to investigate (i) whether existing models trained to predict political bias can also accurately predict the source, and (ii) whether these models change their predictions when a distinct pattern from a source with a different political stance is incorporated into a news article. Our experimental results reveal that all existing models tend to predict the source, even when trained solely to predict bias. Based on these findings, we propose a new deep learning model for political bias prediction that avoids learning source-indicative patterns specific to a given news source.

Heng Rao, Yu Gu, Jason Zipeng Zhang, Ge Yu, Yang Cao, Minghan Chen

Biological oscillations are periodic changes in various signaling processes crucial for the proper functioning of living organisms. These oscillations are modeled by ordinary differential equations, with coefficient variations leading to diverse periodic behaviors, typically measured by oscillatory frequencies. This paper explores sampling techniques for neural networks to model the relationship between system coefficients and oscillatory frequency. However, the scarcity of oscillations in the vast coefficient space results in many samples exhibiting non-periodic behaviors, and small coefficient changes near oscillation boundaries can significantly alter oscillatory properties. This leads to non-oscillatory bias and boundary sensitivity, making accurate predictions difficult. While existing importance and uncertainty sampling approaches partially mitigate these challenges, they either fail to resolve the sensitivity problem or result in redundant sampling. To address these limitations, we propose the Hierarchical Gradient-based Genetic Sampling (HGGS) framework, which improves the accuracy of neural network predictions for biological oscillations. The first layer, Gradient-based Filtering, extracts sensitive oscillation boundaries and removes redundant non-oscillatory samples, creating a balanced coarse dataset. The second layer, Multi-grid Genetic Sampling, utilizes residual information to refine these boundaries and explore new high-residual regions, increasing data diversity for model training. Experimental results demonstrate that HGGS outperforms seven comparative sampling methods across four biological systems, highlighting its effectiveness in enhancing sampling and prediction accuracy.

Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel Faissol, Brenden Petersen, Mikel Landajuela

We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables to sample new hybrid designs. In contrast to standard decoupled approaches, in which the discrete and continuous variables are optimized separately, our joint optimization approach uses fewer objective function evaluations, is robust against non-differentiable objectives, and learns from prior samples to guide the search, leading to significant improvement in performance and sample efficiency. Our experiments on a diverse set of optimization tasks demonstrate that the advantages of DisCo-DSO become increasingly evident as problem complexity grows. In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees.

Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen, Qian Dong, Yiqun Liu

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity. However, in the legal domain, textual semantic similarity does not always imply that the cases are relevant enough. Instead, relevance in legal cases primarily depends on the similarity of key facts that impact the final judgment. Without proper treatments, the discriminative ability of learned representations could be limited since legal cases are lengthy and contain numerous non-key facts. To this end, we introduce DELTA, a discriminative model designed for legal case retrieval. The basic idea involves pinpointing key facts in legal cases and pulling the contextualized embedding of the [CLS] token closer to the key facts while pushing away from the non-key facts, which can warm up the case embedding space in an unsupervised manner. To be specific, this study brings the word alignment mechanism to the contextual masked auto-encoder. First, we leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Second, we employ the deep decoder to enable ``translation'' between different structures, with the goal of pinpointing key facts to enhance discriminative ability. Comprehensive experiments conducted on publicly available legal benchmarks show that our approach can outperform existing state-of-the-art methods in legal case retrieval. It provides a new perspective on the in-depth understanding and processing of legal case documents.