We study expected improvement (EI) for minimizing a deterministic function $f$ in the RKHS $\mathcal H_k$ of a continuous positive-semidefinite kernel $k$ on a nonempty compact set $\mathcal X\subset\mathbb R^d$. Function values are observed exactly, and EI is computed from a fixed zero-mean Gaussian-process model with...
Emmanuel Vazquez, S. Petit· arXiv.org· 0 citations
An online multiclass L2D algorithm that combines queried-action bandit feedback with a dynamically varying pool of experts is introduced that achieves expected true-deferral regret under a concentrated-score condition.
Duy Hoang Dang, Yannis Montreuil, Maxime Meyer et al.· arXiv.org· 5 citations
This work uncover and identify the notion of ``robust item-wise coverage''as the minimal data requirement to enable sample-efficient robust assortment learning and bridges the gap between robustness and statistical efficiency in assortment learning.
Miao Lu, Yuxuan Han, Han Zhong 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
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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...
Somjit Roy, Pritam Dey, B. Mallick et al.· 1 citation· ⚡1
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
Budget-Constrained Causal Bandits (BCCB), an online framework that learns which users respond to ads while simultaneously spending the budget, is proposed and results give practitioners a concrete decision rule for choosing between offline and online paradigms.
This work benchmarks several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks), and proposes a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer...
Marcin Sendera, Minsu Kim, Sarthak Mittal et al.· Neural Information Processin...· 52 citations· ⚡7
This paper proposes a method to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions, using a single GFlowNet whose sampling policy follows a two-phase process.
T. Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian et al.· Neural Information Processin...· 65 citations· ⚡4
It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...
Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al.· Neural Information Processin...· 302 citations· ⚡60
Together, the results clarify how the input geometry shapes the kernel features and fundamentally impacts its generalization properties and clarify how the input geometry shapes the kernel features and fundamentally impacts its generalization properties.
L. Rizzi, Arie Wortsman Zurich, Bruno Loureiro· 0 citations
For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the minimum-mean-square error estimator takes the form of a regularized least-squares data fit. In this chapter, we show that this equivalence extends to a much broader infinite-dimensional setting where generalized s...
During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
