Previously, methods for learning marginally stable linear dynamical systems either required the transition matrix to be symmetric or incurred regret bounds that scale polynomially with the system's hidden dimension. In this work, we introduce a novel method that overcomes this trade-off, achieving dimension-free regret despite the presence of asymmetric matrices and marginal stability. Our method combines spectral filtering with linear predictors and employs Chebyshev polynomials in the complex plane to construct a novel spectral filtering basis. This construction guarantees sublinear regret in an online learning framework, without relying on any statistical or generative assumptions. Specifically, we prove that as long as the transition matrix has eigenvalues with complex component bounded by $1/\mathrm{poly} \log T$, then our method achieves regret $\tilde{O}(T^{9/10})$ when compared to the best linear dynamical predictor in hindsight.

We provide a general mechanism to design online learning algorithms based on a minimax analysis within a drifting-games framework. Different online learning settings (Hedge, multi-armed bandit problems and online convex optimization) are studied by converting into various kinds of drifting games. The original minimax analysis for drifting games is then used and generalized by applying a series of relaxations, starting from choosing a convex surrogate of the 0-1 loss function. With different choices of surrogates, we not only recover existing algorithms, but also propose new algorithms that are totally parameter-free and enjoy other useful properties. Moreover, our drifting-games framework naturally allows us to study high probability bounds without resorting to any concentration results, and also a generalized notion of regret that measures how good the algorithm is compared to all but the top small fraction of candidates. Finally, we translate our new Hedge algorithm into a new adaptive boosting algorithm that is computationally faster as shown in experiments, since it ignores a large number of examples on each round.

MindCraft is a modern platform designed to revolutionize education in rural India by leveraging Artificial Intelligence (AI) to create personalized learning experiences, provide mentorship, and foster resource-sharing. In a country where access to quality education is deeply influenced by geography and socio economic status, rural students often face significant barriers in their educational journeys. MindCraft aims to bridge this gap by utilizing AI to create tailored learning paths, connect students with mentors, and enable a collaborative network of educational resources that transcends both physical and digital divides. This paper explores the challenges faced by rural students, the transformative potential of AI, and how MindCraft offers a scalable, sustainable solution for equitable education system. By focusing on inclusivity, personalized learning, and mentorship, MindCraft seeks to empower rural students, equipping them with the skills, knowledge, and opportunities needed to thrive in an increasingly digital world. Ultimately, MindCraft envisions a future in which technology not only bridges educational gaps but also becomes the driving force for a more inclusive and empowered society.

By exploiting the duality between boosting and online learning, we present a boosting framework which proves to be extremely powerful thanks to employing the vast knowledge available in the online learning area. Using this framework, we develop various algorithms to address multiple practically and theoretically interesting questions including sparse boosting, smooth-distribution boosting, agnostic learning and, as a by-product, some generalization to double-projection online learning algorithms.

Summary and Contributions: This work shows that there is a class that is privately PAC learnable in polynomial time, but not efficiently learnable (assuming the existence of one-way functions) in the online setting, i.e., there is no polynomial time algorithm with a polynomial mistake bound. A line of recent work (focused on sample complexity) has demonstrated various equivalences between private PAC and online learning; this result focuses on the question of efficiency, proving that efficient private learnability does not imply efficient online learnability if one-way functions exist. The cryptographic assumption is standard for such results and is needed when ruling out general polynomial time learners. The class of functions that cannot be efficiently learned in the online model was given by [Blum 1994], which showed a separation between distribution free PAC learning and online learning. Much of the work toward separating the models of learning, which involves working out the cryptographic construction and giving an efficient PAC algorithm, was done there -- the technical contribution here is to give a private version of that algorithm.

Linear optimization is arguably the most widely used model of decision-making. Inverse linear optimization is its inverse problem, where the goal is to infer linear objective functions from observed outcomes. Since the early development in geographical science (Tarantola, 1988; Burton and Toint, 1992), inverse linear optimization has been an important subject of study (Ahuja and Orlin, 2001; Heuberger, 2004; Chan et al., 2019) and used in various applications, ranging from route recommendation to healthcare (Chan et al., 2023). Inverse linear optimization is particularly interesting when forward linear optimization is a decision-making model of a human agent.1 Then, the linear objective function represents the agent's internal preference. If the agent repeatedly takes an action upon facing a set of feasible actions, inverse linear optimization can be seen as online learning of the agent's internal preference from pairs of the feasible sets and the agent's choices. Bärmann et al. (2017) studied this setting and proposed an elegant approach based on online learning, which is the focus of this paper and is described below. Consider an agent who addresses decision problems of the following linear-optimization form for = 1,...,: maximize

However, navigating and synthesizing information across these disparate sources can be a timeintensive Efficient online learning requires seamless access to diverse resources and inefficient process, creating barriers to efficient online such as videos, code repositories, documentation, and general learning [8]. The challenges associated with multi-source learning web content. This poster paper introduces early-stage work are especially evident in technical domains, where the need to on a Multi-Agent Retrieval-Augmented Generation (RAG) System quickly find accurate and relevant information is critical. For instance, designed to enhance learning efficiency by integrating these heterogeneous a developer exploring a new framework might consult a resources. Using specialized agents tailored for specific YouTube tutorial for an overview, reference a GitHub repository resource types (e.g., YouTube tutorials, GitHub repositories, documentation for implementation details, examine the official documentation for websites, and search engines), the system automates deeper insights, and conduct general web searches for troubleshooting.

Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or account for dynamic changes in consumption patterns. This paper proposes a multi-task learning technique for online and probabilistic load forecasting. This technique provides accurate probabilistic predictions for the loads of multiple entities by leveraging their dynamic similarities. The method's performance is evaluated using datasets that register the load demand of multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load consumption scenarios.

Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data. To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time. Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors. Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data. Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a ''digital twin'' for online education.

Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Recent work develops online conformal prediction methods that adaptively construct prediction sets to accommodate distribution shifts. However, existing algorithms typically assume perfect label accuracy which rarely holds in practice. In this work, we investigate the robustness of online conformal prediction under uniform label noise with a known noise rate, in both constant and dynamic learning rate schedules. We show that label noise causes a persistent gap between the actual mis-coverage rate and the desired rate $\alpha$, leading to either overestimated or underestimated coverage guarantees. To address this issue, we propose Noise Robust Online Conformal Prediction (dubbed NR-OCP) by updating the threshold with a novel robust pinball loss, which provides an unbiased estimate of clean pinball loss without requiring ground-truth labels. Our theoretical analysis shows that NR-OCP eliminates the coverage gap in both constant and dynamic learning rate schedules, achieving a convergence rate of $\mathcal{O}(T^{-1/2})$ for both empirical and expected coverage errors under uniform label noise. Extensive experiments demonstrate the effectiveness of our method by achieving both precise coverage and improved efficiency.