arXiv:2505.08578v3 Announce Type: replace-cross Abstract: Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentially high-impact events, such as flooding or financial crises, regulators often require very high confidence for such intervals. However, if the desired level of confidence is too large relative to the amount of data used for calibration, then classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. In this paper, […]

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Coding in public: with filament and the tall stack If you’re interested to take part in open source, coding in public is naturally something we do. Here is an opportunity to come along with me on my own path to do the same. n n I’ve been looking for a way to share what I’ve learnt in my time and for that to be useful, not just for you the reader, but also for me. At the beginning […]

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arXiv:2603.08518v1 Announce Type: cross Abstract: While standard reinforcement learning optimizes a single reward signal, many applications require optimizing a nonlinear utility $f(J_1^pi,dots,J_M^pi)$ over multiple objectives, where each $J_m^pi$ denotes the expected discounted return of a distinct reward function. A common approach is concave scalarization, which captures important trade-offs such as fairness and risk sensitivity. However, nonlinear scalarization introduces a fundamental challenge for policy gradient methods: the gradient depends on $partial f(J^pi)$, while in practice only empirical return estimates […]

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arXiv:2602.09456v1 Announce Type: cross Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that reduces contextual bandit learning with general reward function approximation to offline regression. The framework allows near-optimal regret for contextual bandits with large action spaces with $O(log(T))$ calls to an offline regression oracle over $T$ rounds, and makes $O(loglog(T))$ calls when $T$ is known. The design of OE2D algorithm generalizes Falcon~citep{simchi2022bypassing} and its linear reward version~citep[][Section 4]{xu2020upper} in that it chooses an action […]

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arXiv:2601.08849v1 Announce Type: new Abstract: Automated answer matching, which leverages LLMs to evaluate free-text responses by comparing them to a reference answer, shows substantial promise as a scalable and aligned alternative to human evaluation. However, its reliability requires robustness against strategic attacks such as guesswork or verbosity that may artificially inflate scores without improving actual correctness. In this work, we systematically investigate whether such tactics deceive answer matching models by prompting examinee models to: (1) generate verbose responses, […]

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Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. That is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due […]

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