The resulting learned policies consistently outperform standard heuristics and resist distillation into simple interpretable models, providing empirical evidence that deep reinforcement learning allows the agents to exploit non-linear geometric structure beyond the scope of traditional heuristics.
R. Bunch, A. Ergür, Melika Golestani et al.· arXiv.org· 0 citations
This work introduces prequential posteriors, based upon a predictive-sequential (prequential) loss function, and proves that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance.
S. Roy, R. Everitt, Christian P. Robert et al.· arXiv.org· 0 citations
Depression symptom severity was associated with five lexical features, including reductions in word count measures, use of first-person plural pronouns and positive word frequency, andLexical features and vector embeddings did improve prediction accuracy beyond baseline models.
A. Tokareva, J. Dineley, Z. Firth et al.· arXiv.org· 0 citations
This work develops GREAT, a novel framework for crafting natural distributional backdoors in RLHF, which targets harmful response generation for a vulnerable user subpopulation featured by semantically violent requests paired with emotionally angry triggers.
Subrat Kishore Dutta, Yuelin Xu, P. Pant et al.· arXiv.org· 0 citations
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This work compares the performance of PINNs in solving inverse problems with that of a traditional approach using the finite element method combined with a numerical optimizer and finds that while PINNs may require less human effort and specialized knowledge, they are outperformed by the traditional approach.
Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen et al.· arXiv.org· 3 citations· ⚡1
This work introduces a probabilistic symbolic regression framework that represents mathematical expressions as ensembles of symbolic trees, and develops posterior concentration guarantees when symbolic expressions approximate the underlying relationship arbitrarily well, with a near-parametric rate when an exact finite formula exists.
Somjit Roy, Pritam Dey, B. Mallick et al.· 1 citation· ⚡1
Ampere is a novel collaborative training system that simultaneously minimizes on-device computation and device-server communication while improving model accuracy and reduces standard deviation of accuracy, highlighting superior performance when faced with heterogeneous data.
Zihan Zhang, Leon Wong, Blesson Varghese· IEEE Transactions on Paralle...· 1 citation
The results indicate that mixtures of M\"obius distributions provide interpretable, parsimonious models for the studied steady state density distributions, that match or outperform the alternatives.
Wanxin Gao, I. Nikolaidis, Janelle J. Harms· 2 citations
A Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions is proposed and validated using a multimodal target volume delineation model for prostate cancer radiotherapy.
Yujin Oh, Sangjoon Park, Xiang Li et al.· 0 citations
This work revisits this design choice and proposes a sub-center modeling framework for speaker embeddings, which improves intelligibility, increases pitch variability, achieves higher naturalness ratings, and retains strong speaker verification performance in zero-shot voice conversion.
Ismail Rasim Ulgen, J. Hansen, Carlos Busso et al.· 0 citations
These findings position ES as a distinct reasoning post-training paradigm rather than a less effective, memory-efficient alternative to GRPO, and study how hyperparameter design affects the effectiveness of ES, demonstrating that ES requires a smaller population size in a larger LLM.
Yunpeng Ba, Zhi Zheng, Yue Xie et al.· 0 citations
This work builds a test bench on 892 yield response curves from two long running UK experiments, and sweeps the nitrogen to grain price ratio to cover all price scenarios, finding that machine learning fails as a predictor and pays as a profit scored correction to standard advice.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
