arXiv:2405. 19466v4 Announce Type: replace Abstract: We pose uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rapid innovation.
arXiv:2610.07406v1 Announce Type: new Abstract: Active feature acquisition learns policies that sequentially acquire features to maximize information about a target variable. We study how to learn an...
arXiv:2511. 04666v4 Announce Type: replace Abstract: A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data.
arXiv:2603. 09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains.
arXiv:2603. 12617v2 Announce Type: replace-cross Abstract: Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens that are then verified in parallel by a larger target model.
arXiv:2510. 12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire.
arXiv:2511.20413v2 Announce Type: replace-cross Abstract: \emph{Decision-focused learning} (DFL) trains predictive models to optimize downstream decisions rather than prediction accuracy alone. While...
arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv:2510.09596v2 Announce Type: replace-cross Abstract: Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the gen...
The paper introduces prequential posteriors, a Bayesian approach that uses a predictive‑sequential loss function to update deep generative forecasting models (DGFMs) when new data arrive. By adopting a consistency notion suitable for model misspecification, the authors prove that both the loss minimizer and the posterior concentrate on parameters with optimal predictive performance. Scalable inference is achieved with parallelisable waste‑free sequential Monte Carlo samplers that employ preconditioned gradient kernels, and the method is validated on synthetic and real meteorological time‑series data.
arXiv:2503.00626v3 Announce Type: replace Abstract: Data-driven optimization aims to translate a machine learning model into decision-making by optimizing decisions on estimated costs. Such a pipelin...
The paper addresses look‑ahead bias in large language models (LLMs) used for financial prediction, which arises because LLMs are trained on long time‑series data. It proposes a low‑cost solution that adjusts the logits of a base model at inference time using two smaller, specialized models—one fine‑tuned to forget certain information and another to retain it. Experiments show that this method removes both verbatim and semantic knowledge, corrects biases, and outperforms previous approaches.