arXiv:2604.07143v1 Announce Type: cross Abstract: We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a dataset’s mutual reachability minimum spanning tree. The use of mutual reachability distances smoothens the data distribution and decreases the influence of low-density objects, such as noise points between clusters or outliers at their peripheries. The algorithm can be viewed as an alternative to […]

Ver mais

We give an algorithm for PAC learning intersections of $k$ halfspaces with a $ρ$ margin to within error $varepsilon$ that runs in time $textsf{poly}(k, varepsilon^{-1}, ρ^{-1}) cdot exp left(O(sqrt{n log(1/ρ) log k})right)$. Notably, this improves on prior work which had an exponential dependence on either $k$ or $ρ^{-1}$ and matches known cryptographic and Statistical Query lower bounds up to the logarithmic factors in $k$ and $ρ$ in the exponent. Our learning algorithm extends to the more general setting […]

Ver mais

Offline reinforcement learning (ORL) has shown potential for improving decision-making in healthcare. However, contemporary research typically aggregates patient data into fixed time intervals, simplifying their mapping to standard ORL frameworks. The impact of these temporal manipulations on model safety and efficacy remains poorly understood. In this work, using both a gridworld navigation task and the UVA/Padova clinical diabetes simulator, we demonstrate that temporal resampling significantly degrades the performance of offline reinforcement learning algorithms during live deployment. We propose […]

Ver mais

arXiv:2406.15904v2 Announce Type: replace-cross Abstract: Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This paper studies distribution shifts in the domain adaptation problem with unobserved confounding. We postulate a linear structural causal model to account for endogeneity and unobserved confounding, and we leverage exogenous […]

Ver mais

arXiv:2402.03352v3 Announce Type: replace-cross Abstract: In this paper, we study zeroth-order algorithms for nonconvex minimax problems with coupled linear constraints under the deterministic and stochastic settings, which have attracted wide attention in machine learning, signal processing and many other fields in recent years, e.g., adversarial attacks in resource allocation problems and network flow problems etc. We propose two single-loop algorithms, namely the zeroth-order primal-dual alternating projected gradient (ZO-PDAPG) algorithm and the zeroth-order regularized momentum primal-dual projected gradient algorithm […]

Ver mais

Label distribution learning (LDL) requires the learner to predict the degree of correlation between each sample and each label. To achieve this, a crucial task during learning is to leverage the correlation among labels. Deep Forest (DF) is a deep learning framework based on tree ensembles, whose training phase does not rely on backpropagation. DF performs in-model feature transform using the prediction of each layer and achieves competitive performance on many tasks. However, its exploration in the field […]

Ver mais

Release: llm-all-models-async 0.1 LLM plugins can define new models in both sync and async varieties. The async variants are most common for API-backed models – sync variants tend to be things that run the model directly within the plugin. My llm-mrchatterbox plugin is sync only. I wanted to try it out with various Datasette LLM features (specifically datasette-enrichments-llm) but Datasette can only use async models. So… I had Claude spin up this plugin that turns sync models into […]

Ver mais

arXiv:2601.08846v1 Announce Type: new Abstract: Iterative summarization based reasoning frameworks such as InftyThink enable long-horizon reasoning in large language models (LLMs) by controlling context growth, but they repeatedly regenerate similar reasoning strategies across tasks. We introduce InftyThink with Cross-Chain Memory, an extension that augments iterative reasoning with an embedding-based semantic cache of previously successful reasoning patterns. At each reasoning step, the model retrieves and conditions on the most semantically similar stored lemmas, guiding inference without expanding the context […]

Ver mais

arXiv:2512.11150v3 Announce Type: replace-cross Abstract: Measuring long-run LLM outcomes (user satisfaction, expert judgment, downstream KPIs) is expensive. Teams default to cheap LLM judges, but uncalibrated proxies can invert rankings entirely. Causal Judge Evaluation (CJE) makes it affordable to aim at the right target: calibrate cheap scores against a small oracle slice, then evaluate at scale with valid uncertainty. We treat surrogate validity as auditable: for each policy or deployment context, a small oracle audit tests whether the learned […]

Ver mais