A recent work by Schlisselberg et al. (2025) studies a delay-as-payoff model for stochastic multi-armed bandits, where the payoff (either loss or reward) is delayed for a period that is proportional to the payoff. While this captures many real-world applications, the simple multi-armed bandit setting limits the practicality of their results. In this paper, we address this limitation by studying the delay-as-payoff model for contextual linear bandits. Specifically, we start from the case with a fixed action set and propose an efficient algorithm whose regret overhead compared to the standard no-delay case is only of order $D\Delta_{\max}\log T$, where $T$ is the total horizon, $D$ is the maximum delay, and $\Delta_{\max}$ is the maximum suboptimality gap. When payoff is loss, we also show further improvement of the bound, demonstrating a separation between reward and loss similar to Schlisselberg et al. (2025). Contrary to standard linear bandit algorithms that construct least squares estimator and confidence ellipsoid, the main novelty of our algorithm is to apply a phased arm elimination procedure by only picking the **volumetric spanners** of the action set, which addresses challenges arising from both payoff-dependent delays and large action sets. We further extend our results to the case with varying action sets by adopting the reduction from Hanna et al. (2023). Finally, we implement our algorithm and showcase its effectiveness and superior performance in experiments.

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference within machine learning models. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.

When large online platforms test a new strategy to implement with their user traffic, the phenomenon of diminishing marginal effects may arise. For example, when a strategy is implemented on 100% of the user traffic, the expected per-user effect can be lower compared to the expected per-user effect when a strategy is implemented on 10% of the user traffic, potentially due to limits of overall budget, resource, attention or content involved with that strategy. This diminishing marginal effect phenomenon brings an additional delicacy to online sequential experiments. In particular, for the classical goal of achieving the largest expected reward, the optimal decision may no longer be assigning 100% traffic to one strategy, but instead a mixture of strategies. We deliver two tasks for online sequential experiments in presence of diminishing marginal effect: (1) Adaptively identify the optimal traffic allocation to maximize the expected cumulative reward and (2) Construct valid central limit theorem (which is critically needed for A/B tests in online platforms) to perform reliable statistical inference for the expected reward under the optimal traffic allocation that is a priori unknown. We show that classical algorithms can fail to deliver the second task, especially because the statistical inference task presents its own difficulty. We develop a new online algorithm that leverages an additional smoothness condition on how the marginal effects change to achieve both tasks. We prove that this algorithm obtains the best achievable expected cumulative reward. Further, crucially for online platforms' need to do trustworthy statistical inference, the algorithm is proved to enjoy a valid central limit theorem. The theoretical findings are illustrated through numerical experiments.

MOTIVATION: DNA sequence and shape readout represent different modes of protein-DNA recognition. Current tools lack the functionality to simultaneously consider alterations in different readout modes caused by sequence mutations. DNAdesign is a web-based tool to compare and design mutations based on both DNA sequence and shape characteristics. Users input a wild-type sequence, select sites to introduce mutations and choose a set of DNA shape parameters for mutation design. RESULTS: DNAdesign utilizes Deep DNAshape to provide ultra-fast predictions of DNA shape based on extended k-mers and offers multiple encoding methods for nucleotide sequences, including the physicochemical encoding of DNA through their functional groups in the major and minor groove. DNAdesign provides all mutation candidates along the sequence and shape dimensions, with interactive visualization comparing each candidate with the wild-type DNA molecule. DNAdesign provides an approach to studying gene regulation and applications in synthetic biology, such as the design of synthetic enhancers and transcription factor binding sites. AVAILABILITY AND IMPLEMENTATION: The DNAdesign webserver and documentation are freely accessible at https://dnadesign.usc.edu.

Randomized controlled trials (RCTs) are gold standards for assessing intervention efficacy. Yet, generalizing evidence from classical RCTs can be challenging and sometimes problematic due to their limited external validity under stringent eligibility criteria and inadequate statistical power resulting from limited sample sizes under budgetary constraints. "Digital clinical trial," which utilizes digital technology and electronic medical records (EMRs) to expand eligibility criteria and enhance data collection efficiency, offers a promising concept for solving the above-mentioned conundrums encountered in classical RCTs. In this paper, we propose two novel digital clinical trial design strategies assisted by EMRs collected from diverse patient populations. On the one hand, leveraging digital technologies, our design strategies adaptively modify both the eligibility criteria and treatment assignment mechanism to enhance data collection efficiency. As a result, evidence gathered from our design can possess greater statistical power. On the other hand, since EMRs capture diverse patient populations and provide large sample sizes, our design not only broadens the trial’s eligibility criteria but also enhances its statistical power, enabling us to collect more generalizable evidence with boosted statistical power for evaluating intervention efficacy than classical RCTs. We demonstrate the validity and merit of the proposed designs with detailed theoretical investigation, simulation studies, and a synthetic case study.

There has been increasing interest in applying multi-armed bandits to adaptive designs in clinical trials. However, most literature assumes that a previous patient’s survival response of a treatment is known before the next patient is treated, which is unrealistic. The inability to account for response delays is cited frequently as one of the problems in using adaptive designs in clinical trials. More critically, the “delays” in observing the survival response are the same as the rewards rather than being external stochastic noise. We formalize this problem as a novel stochastic multi-armed bandit (MAB) problem with reward-dependent delays, where the delay at each round depends on the reward generated on the same round. For general reward/delay distributions with finite expectation, our proposed censored-UCB algorithm achieves near-optimal regret in terms of both problem-dependent and problem-independent bounds. With bounded or sub-Gaussian reward distributions, the upper bounds are optimal with a matching lower bound. Our theoretical results and the algorithms’ effectiveness are validated by empirical experiments.

Skin tone as a demographic bias and inconsistent human labeling poses challenges in dermatology AI. We take another angle to investigate color contrast’s impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models. To study this, we first propose a robust labeling method to quantify color contrast scores of each image and validate our method by showing small labeling variations. More importantly, applying our method to the only diverse-skin tone and pathologically-confirmed skin disease dataset DDI, yields DDI-CoCo Dataset1, and we observe a performance gap between the high and low color difference groups. This disparity remains consistent across various state-of-the-art (SoTA) image classification models, which supports our hypothesis. Furthermore, we study the interaction between skin tone and color difference effects and suggest that color difference can be an additional reason behind model performance bias between skin tones. Our work provides a complementary angle to dermatology AI for improving skin disease detection.

The workshop's rationale stems from the escalating interest in causal inference and machine learning methodologies within various industrial contexts. This surge in demand underscores the importance for both scholars and practitioners to exchange knowledge and best practices regarding the application of these techniques to tackle real-world challenges. Yet, applying causal machine learning techniques in real-world scenarios presents a range of challenges not addressed in the academic literature. This workshop aims to address the challenges for practical causal machine learning and explore new industry use cases. The workshop will provide a forum for practitioners and researchers to exchange ideas and explore new collaborations. Moreover, this workshop aims to capitalize on the success and achievements of the KDD 2023 Workshop titled "Causal Inference and Machine Learning in Practice".

Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms’ SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online convex optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents’ strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness.

The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of the underlying mechanisms and to develop more effective solutions to real-world problems.

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 2nd Multi-armed Bandits and Reinforcement Learning Workshop was a half day workshop co-located with the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2023) in Long Beach, California.

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 1st Multi-armed Bandits and Reinforcement Learning Workshop was a full day workshop co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021) in Singapore.

We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be inferred from fluorescence measurements obtained by binding a complementary probe with fluorescence markers to the targeted regions. We perform a regularized, sparse linear model with a log link function where the marginal contribution to the thermodynamic cycle of each nucleotide is purely additive. The SpKG algorithm uniquely combines the Bayesian ranking and selection problem with the frequentist l1regularized regression approach Lasso. We use this algorithm to identify the sparsity pattern of the linear model as well as sequentially decide the best regions to test before exhausting an experimental budget. We also develop two new algorithms: batch SpKG and batch SpKG-LM. The first algorithm generates more suggestions sequentially to run parallel experiments. The second one dynamically adds new alternatives, in the form of types of probes, which are created by inserting, deleting, or mutating nucleotides within existing probes. In simulation, we demonstrate these algorithms on the Tetrahymena Group I intron (a midsize RNA molecule), showing that they efficiently learn the correct sparsity pattern, identify the most accessible region, and outperform several other policies.

The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed. Specifically, if the interested layer is sparsely connected and the node behaviors of the other layer are observable at a few time points, then a theoretical framework is established based on compressive sensing and regularization. Some numerical examples illustrate the effectiveness of the identification scheme, its requirement of a relatively small number of observations, as well as its robustness against small noise. It is noteworthy that the framework can be straightforwardly extended to multilayer networks, thus applicable to a variety of real-world complex systems.

Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimum-cost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability.With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-through rate is predicted using logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.

Query auto-completion (QAC) is widely used by modern search engines to assist users by predicting their intended queries. Most QAC approaches rely on deterministic batch learning algorithms trained from past query log data. However, query popularities keep changing all the time and QAC operates in a real-time scenario where users interact with the search engine continually. So, ideally, QAC must be timely and adaptive enough to reflect time-sensitive changes in an online fashion. Second, due to the vertical position bias, a query suggestion with a higher rank tends to attract more clicks regardless of user's original intention. Hence, in the long run, it is important to place some lower ranked yet potentially more relevant queries to higher positions to collect more valuable user feedbacks. In order to tackle these issues, we propose to formulate QAC as a ranked Multi-Armed Bandits (MAB) problem which enjoys theoretical soundness. To utilize prior knowledge from query logs, we propose to use Bayesian inference and Thompson Sampling to solve this MAB problem. Extensive experiments on large scale datasets show that our QAC algorithm has the capacity to adaptively learn temporal trends, and outperforms existing QAC algorithms in ranking qualities.

We consider the problem of sequentially making decisions that are rewarded by “successes” and “failures” which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success, either after the offline training phase or minimizing regret in online learning. Our problem is motivated by real-world applications where observations are time consuming and/or expensive. With the adaptation of an online Bayesian linear classifier, we develop a knowledge-gradient type policy to guide the experiment by maximizing the expected value of information of labeling each alternative, in order to reduce the number of expensive physical experiments. We provide a finite-time analysis of the estimated error and demonstrate the performance of the proposed algorithm on both synthetic problems and benchmark UCI datasets.