Amazon (United Kingdom)

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. Building on the success of previous workshops, which established foundations in automated judgments and RAG evaluation, this third iteration aims to address emerging challenges as IR systems become increasingly personalized and interactive. The main goal of the third LLM4Eval workshop is to bring together researchers from industry and academia to explore three critical areas: the evaluation of personalized IR systems while maintaining fairness, the boundaries between automated and human assessment in subjective scenarios, and evaluation methodologies for systems that combine multiple IR paradigms (search, recommendations, and dialogue). By examining these challenges, we seek to understand how evaluation approaches can evolve to match the sophistication of modern IR applications. The format of the workshop is interactive, including roundtable discussion sessions, fostering dialogue about the future of IR evaluation while avoiding one-sided discussions. This is the third iteration of the workshop series, following successful events at SIGIR 2024 and WSDM 2025, with the first iteration attracting over 50 participants.

Large-scale test collections play a crucial role in Information Retrieval (IR) research. However, according to the Cranfield paradigm and the research into publicly available datasets, the existing information retrieval research studies are commonly developed on small-scale datasets that rely on human assessors for relevance judgments - a time-intensive and expensive process. Recent studies have shown the strong capability of Large Language Models (LLMs) in producing reliable relevance judgments with human accuracy but at a greatly reduced cost. In this paper, to address the missing large-scale ad-hoc document retrieval dataset, we extend the TREC Deep Learning Track (DL) test collection via additional language model synthetic labels to enable researchers to test and evaluate their search systems at a large scale. Specifically, such a test collection includes more than 1,900 test queries from the previous years of tracks. We compare system evaluation with past human labels from past years and find that our synthetically created large-scale test collection can lead to highly correlated system rankings.

Formal methods encompass a wide choice of techniques and tools for the specification, development, analysis, and verification of software and hardware systems. Formal methods are widely applied in industry, in activities ranging from the elicitation of requirements and the early design phases all the way to the deployment, configuration, and runtime monitoring of actual systems. Formal methods allow one to precisely specify the environment in which a system operates, the requirements and properties that the system should satisfy, the models of the system used during the various design steps, and the code embedded in the final implementation, as well as to express conformance relations between these specifications. We present a broad scope of successful applications of formal methods in industry, not limited to the well-known success stories from the safety-critical domain, like railways and other transportation systems, but also covering other areas such as lithography manufacturing and cloud security in e-commerce, to name but a few. We also report testimonies from a number of representatives from industry who, either directly or indirectly, use or have used formal methods in their industrial project endeavours. These persons are spread geographically, including Europe, Asia, North and South America, and the involved projects witness the large coverage of applications of formal methods, not limited to the safety-critical domain. We thus make a case for the importance of formal methods, and in particular of the capacity to abstract and mathematical reasoning that are taught as part of any formal methods course. These are fundamental Computer Science skills that graduates should profit from when working as computer scientists in industry, as confirmed by industry representatives.

Machine learning applications are rapidly adopted by industry leaders in any field. The growth of investment in AI-driven solutions,including the emerging field of General AI (GenAI), has created new challenges in managing Data Science and ML resources, people and projects as a whole. The discipline of managing applied machine learning teams, requires a healthy mix between agile product development tool-set and a long term research oriented mindset. The abilities of investing in deep research while at the same time connecting the outcomes to significant business results create a large knowledge based on management methods and best practices in the field. The Third KDD Workshop on Applied Machine Learning Management brings together applied research managers from various fields to share methodologies and case-studies on management of ML teams, products, and projects, achieving business impact with advanced AI-methods.

The SPARK programming language and analyzer.

Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AI assistants</i> , a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.

Program synthesis is the mechanised construction of software. One of the main difficulties is the efficient exploration of the very large solution space, and tools often require a user-provided syntactic restriction of the search space. While useful in general, such syntactic restrictions provide little help for the generation of programs that contain non-trivial constants, unless the user is able to provide the constants in advance. This is a fundamentally difficult task for state-of-the-art synthesisers. We propose a new approach to the synthesis of programs with non-trivial constants that combines the strengths of a counterexample-guided inductive synthesiser with those of a theory solver, exploring the solution space more efficiently without relying on user guidance. We call this approach CEGIS(mathcal {T}), where mathcal {T} is a first-order theory. We present two exemplars, one based on Fourier-Motzkin (FM) variable elimination and one based on first-order satisfiability. We demonstrate the practical value of CEGIS(mathcal {T}) by automatically synthesising programs for a set of intricate benchmarks. Additionally, we present a case study where we integrate CEGIS(mathcal {T}) within the mature synthesiser CVC4 and show that CEGIS(mathcal {T}) improves CVC4’s results.

Capsule networks (see Hinton etal., 2018) aim to encode knowledge of and reason about the relationship between an object and its parts. In this letter, we specify a generative model for such data and derive a variational algorithm for inferring the transformation of each model object in a scene and the assignments of observed parts to the objects. We derive a learning algorithm for the object models, based on variational expectation maximization (Jordan etal., 1999). We also study an alternative inference algorithm based on the RANSAC method of Fischler and Bolles (1981). We apply these inference methods to data generated from multiple geometric objects like squares and triangles ("constellations") and data from a parts-based model of faces. Recent work by Kosiorek etal. (2019) has used amortized inference via stacked capsule autoencoders to tackle this problem; our results show that we significantly outperform them where we can make comparisons (on the constellations data).

Modern recommender systems are often modelled under the sequential decision-making paradigm, where the systemdecideswhich recommendations to show in order to maximise some notion of either imminent or long-term reward. Such methods often require an explicit model of the reward a certain context-action pair will yield – for example, the probability of a click on a recommendation. This common machine learning task is highly non-trivial, as the data-generating process for contexts and actions can be skewed by the recommender system itself. Indeed, when the deployed recommendation policy at data collection time does not pick its actions uniformly-at-random, this leads to a selection bias that can impede effective reward modelling. This in turn makes off-policy learning – the typical setup in industry – particularly challenging. Existing approaches for value-based learning break down in such environments.In this work, we propose and validate a generalpessimisticreward modelling approach for off-policy learning in recommendation. Bayesian uncertainty estimates allow us to express scepticism about our own reward model, which can in turn be used to generate a conservative decision rule. We show how it alleviates a well-known decision making phenomenon known as the Optimiser’s Curse, and draw parallels with existing work on pessimistic policy learning. Leveraging the available closed-form expressions for both the posterior mean and variance when a ridge regressor models the reward, we show how to apply pessimism effectively and efficiently to an off-policy recommendation use-case. Empirical observations in a wide range of simulated environments show that pessimistic decision-making leads to a significant and robust increase in recommendation performance. The merits of our approach are most outspoken in realistic settings with limited logging randomisation, limited training samples, and larger action spaces. We discuss the impact of our contributions in the context of related applications like computational advertising, and present a scope for future research based on hybrid off-/on-policy bandit learning methods for recommendation.