Combining optimal transport and quantization, the paper derives convergence bounds for learning probability measures supported on manifolds, achieving rates of n^{-1/(2d+4)}.

Guillermo D. Canas, Lorenzo Rosasco

Introduces a variational principle for martingale optimal transport, supporting on two functions with support points ≤3, ensuring uniqueness under continuous marginals.

Mathias Beiglböck, Nicolas Juillet

Proposes a robust, fast recursive algorithm for separable NMF with theoretical guarantees under noise, outperforming existing methods in accuracy and efficiency.

Nicolas Gillis, Stephen A. Vavasis

Introduces ALE platform evaluating 55+ Atari games, combining reinforcement learning and planning for general AI assessment.

Marc G. Bellemare, Yavar Naddaf, Joel Veness et al.

Joint language-perception model using probabilistic grammar and classifiers significantly improves grounded attribute learning, achieving 82% precision in object set selection.

Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer et al.

Explores causal learning's impact on semi-supervised learning with hypotheses and validation.

Bernhard Schoelkopf, Dominik Janzing, Jonas Peters et al.

Proposes Conservative PC (CPC) algorithm relying only on adjacency faithfulness, improving causal inference accuracy.

Joseph Ramsey, Jiji Zhang, Peter L. Spirtes

Deep learning-based representation learning, using autoencoders and probabilistic models, enhances feature extraction and model generalization.

Yoshua Bengio, Aaron Courville, Pascal Vincent

Proposes a nonparametric RKHS embedding approach for modeling MDP transition dynamics, outperforming Gaussian processes and NPDP in efficiency and accuracy.

Steffen Grunewalder, Guy Lever, Luca Baldassarre et al.

Proposes a training-data-driven expert learning algorithm with context trees for improved short-sequence prediction.

Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz et al.

Koopman operator-based spectral analysis enables nonlinear system modal decomposition from data, improving high-dimensional understanding.

Marko Budišić, Ryan M. Mohr, Igor Mezić

The paper proposes a Bayesian optimization method using Gaussian processes, significantly enhancing hyperparameter tuning efficiency for ML algorithms.

Jasper Snoek, Hugo Larochelle, Ryan P. Adams

Proves Krivine schemes are asymptotically optimal for approximating the Grothendieck constant KG, with error (1+O(1/k)).

Assaf Naor, Oded Regev

Proposes a compressed sensing-based robust state estimator capable of tolerating up to p/2-1 sensor attacks, enhancing system security.

Hamza Fawzi, Paulo Tabuada, Suhas Diggavi

Revealed the equivalence between conditional mean embeddings and vector-valued regression, achieving near-optimal convergence rate of O(log(n)/n) with sparse regularization.

Steffen Grünewälder, Guy Lever, Luca Baldassarre et al.

This paper rigorously analyzes regret bounds for stochastic and adversarial multi-armed bandits, establishing near-optimal guarantees for UCB and Exp3 algorithms.

Sébastien Bubeck, Nicolò Cesa-Bianchi

Proposes a quantum algorithm leveraging HHL to efficiently assess least-squares fit quality over exponentially large datasets.

Nathan Wiebe, Daniel Braun, Seth Lloyd

Experiments plus Keller–Segel theory show chemically mediated clustering, with mean cluster size N* increasing linearly with swimming speed V.

I. Theurkauff, C. Cottin-Bizonne, J. Palacci et al.

Introduces a linear combination-based Hamiltonian simulation algorithm with complexity O(m²hte^{1.6√log(mht/ε)}), outperforming product formulas.

Andrew M. Childs, Nathan Wiebe

Proposes a ridge regression-based p-value method for high-dimensional linear models with strong error control, suitable for single and multiple hypotheses.

Peter Bühlmann