Evaluating recommender systems remains challenging due to the gap between offline metrics and real user behavior, as well as the scarcity of interaction data.Recent work explores large language model (LLM) agents as synthetic users, yet they typically rely on few-shot prompting, which yields a shallow understanding of the environment and limits their ability to faithfully reproduce user actions.We introduce ALIGNUSER, a framework that learns world-model-driven agents from human interactions.Given rollout sequences of actions and states, we formalize world modeling as a next state prediction task that helps the agent internalize the environment.To align actions with human personas, we generate counterfactual trajectories around demonstrations and prompt the LLM to compare its decisions with human choices, identify suboptimal actions, and extract lessons.The learned policy is then used to drive agent interactions with the recommender system.We evaluate ALIGNUSER across multiple datasets and demonstrate closer alignment with genuine humans than prior work, both at the micro and macro levels.

We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for training large networks such as GPT-2 and ResNets from scratch. IVON's computational costs are nearly identical to Adam but its predictive uncertainty is better. We show several new use cases of IVON where we improve finetuning and model merging in Large Language Models, accurately predict generalization error, and faithfully estimate sensitivity to data. We find overwhelming evidence that variational learning is effective. Code is available at https://github.com/team-approx-bayes/ivon.

Geometric representation learning has shown great promise for important tasks inartificial intelligence and machine learning. However, an open problem is yethow to integrate non-Euclidean representations with standard machine learningmethods.In this work, we consider the task of regression onto hyperbolic space for whichwe propose two approaches: a non-parametric kernel-method for which we also proveexcess risk bounds and a parametric deep learning model that is informed bythe geodesics of the target space.By recasting predictions on trees as manifold regression problems we demonstrate the applications of our approach on two challenging tasks: 1)hierarchical classification via label embeddings and 2) inventing new conceptsby predicting their embedding in a continuous representation of a base taxonomy.In our experiments, we find that the proposed estimators outperform their naivecounterparts that perform regression in the ambient Euclidean space.

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold-valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study.