Select high-dimensional features using feature-wise kernelized Lasso, significantly improving non-linear dependency feature selection efficiency.

Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal et al.

Scikit-learn is a Python library integrating state-of-the-art ML algorithms like SVM and PCA, emphasizing usability and performance.

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al.

Large-scale unsupervised deep autoencoder trained on 10 million images achieves high-level feature detection, boosting ImageNet accuracy by 70%.

Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga et al.

Entropy Search algorithm optimizes global search by maximizing information gain, enhancing efficiency.

Philipp Hennig, Christian J. Schuler

The No-U-Turn Sampler (NUTS) enhances HMC efficiency by adaptively setting path lengths.

Matthew D. Hoffman, Andrew Gelman

Proposes an efficient nonparametric conformal prediction method combining kernel density estimation, guaranteeing finite-sample coverage with explicit convergence rates.

Jing Lei, James Robins, Larry Wasserman

SATzilla employs empirical hardness models for dynamic SAT instance algorithm selection, significantly improving solving efficiency.

Lin Xu, Frank Hutter, Holger H. Hoos et al.

Quantum states enable publicly-verifiable quantum money and quantum copy-protection.

Scott Aaronson

Proves exponential tail bounds for quadratic forms of subgaussian vectors, analogous to Gaussian case, with explicit constants.

Daniel Hsu, Sham M. Kakade, Tong Zhang

Proposes a bias-corrected low-dimensional projection (LDPE) method for constructing confidence intervals in high-dimensional linear models, effective without uniform signal strength assumptions.

Cun-Hui Zhang, Stephanie S. Zhang

Analysis of 0-1 loss reveals superior noise tolerance, especially under non-uniform label noise, with theoretical guarantees and empirical validation.

Naresh Manwani, P. S. Sastry

Machine learning model predicts molecular atomization energies with ~10 kcal/mol MAE, using nuclear charges and atomic positions, enabling rapid large-scale energy estimation.

Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller et al.

Proposes a model-free, weighted risk-sensitive RL algorithm balancing reward and error state probability, validated on continuous and discrete control tasks.

P. Geibel, F. Wysotzki

Perseus: a randomized point-based value iteration method for large-scale POMDPs, improving efficiency with belief subset sampling

M. T. J. Spaan, N. Vlassis

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

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