Proposes Gossip learning with linear models using random walks and model averaging, enabling privacy-preserving, low-communication distributed classification.

Róbert Ormándi, István Hegedüs, Márk Jelasity

Proposed a prototype knowledge-based programming environment combining declarative and imperative styles.

Stef De Pooter, Johan Wittocx, Marc Denecker

Proposes a Bayesian simulation-based optimal experimental design framework combining Polynomial Chaos and stochastic approximation, improving efficiency in nonlinear systems.

Xun Huan, Youssef M. Marzouk

Using GGA+U with U=1.9 eV, J=1.2 eV, accurately reproduces low-temperature intrinsic anomalous Hall conductivity of nickel, matching experimental data.

Huei-Ru Fuh, Guang-Yu Guo

Introduces quantum annealing and quantum stochastic optimization, demonstrating improved heuristics for NP-hard problems with experimental validation.

Diego de Falco, Dario Tamascelli

Grounded Semantic Composition on Bishop: 59% correct referent selection.

P. Gorniak, D. Roy

Proposed a distributed reactive power compensation strategy using a randomized Gossip algorithm, achieving significant power loss reduction in smart microgrids.

Saverio Bolognani, Sandro Zampieri

SMOTE: Synthetic Minority Over-sampling Technique improves classifier performance on imbalanced datasets by generating synthetic minority samples, boosting AUC by over 0.07 in various experiments.

N. V. Chawla, K. W. Bowyer, L. O. Hall et al.

Proposes a strategy for continuum-armed bandits without knowing the Lipschitz constant, achieving optimal regret bounds.

Sébastien Bubeck, Gilles Stoltz, Jia Yuan Yu

The paper proposes minimax policies for combinatorial prediction games using Bregman projections and potential-based gradient descent to address worst-case minimax regret.

Jean-Yves Audibert, Sebastien Bubeck, Gabor Lugosi

Introduces RFCI, a fast algorithm for high-dimensional causal graph learning with latent and selection variables, ensuring asymptotic correctness.

Diego Colombo, Marloes H. Maathuis, Markus Kalisch et al.

Proposes column subset-based low-rank matrix reconstruction achieving an optimal Frobenius norm approximation ratio of \(\sqrt{rac{r+1}{r-k+1}}\), with algorithms running in O(r n m^ω log m).

Venkatesan Guruswami, Ali Kemal Sinop

Proposes a unified neural network framework leveraging large-scale unlabeled data for multiple NLP tasks, achieving state-of-the-art results without task-specific engineering.

Ronan Collobert, Jason Weston, Leon Bottou et al.

The KL-UCB algorithm outperforms UCB in bounded stochastic bandits, achieving optimal bounds in Bernoulli rewards.

Aurélien Garivier, Olivier Cappé

Using the LePage decomposition, the paper proves that every exp-1-stable point process is a Poisson process of random clusters.

Pascal Maillard

Convex NMF model with l_{1,∞} regularization selects data subset as dictionary, enabling physically meaningful dimensionality reduction.

Ernie Esser, Michael Möller, Stanley Osher et al.

Bayesian optimization efficiently maximizes expensive black-box functions using Gaussian processes and acquisition functions, reducing sample complexity.

Eric Brochu, Vlad M. Cora, Nando de Freitas

Kernel PCA-based nonlinear control system reduction in RKHS, capturing essential dynamics via data-driven Gramian diagonalization.

Jake Bouvrie, Boumediene Hamzi

By analyzing the zero-fluctuation limit, the paper links entropy minimization to Monge-Kantorovich optimal transport via Gamma-convergence and large deviations.

Christian Léonard

Tropp improves analysis of subsampled randomized Hadamard transform, achieving optimal constants for embedding dimension bounds.

Joel A. Tropp