Proposes BOLD and QPM-D algorithms for online learning with delayed feedback; theoretical bounds show multiplicative regret in adversarial and additive in stochastic settings.

Pooria Joulani, András György, Csaba Szepesvári

Proposes GP-UCB-PE, combining UCB and pure exploration for parallel Gaussian process optimization, with bounds outperforming sequential methods.

Emile Contal, David Buffoni, Alexandre Robicquet et al.

Establishes Ω(T^{2/3}) regret lower bound for online learning with switching costs under bandit feedback, highlighting the difficulty compared to full-information settings.

Nicolo Cesa-Bianchi, Ofer Dekel, Ohad Shamir

Deep learning detects robotic grasps with 84% and 89% success rates.

Ian Lenz, Honglak Lee, Ashutosh Saxena

Introduces a weighted averaging scheme in projected stochastic subgradient method achieving O(1/t) convergence, with simple implementation and proof.

Simon Lacoste-Julien, Mark Schmidt, Francis Bach

Regularized reconstruction error enables auto-encoders to learn local features of data distribution, estimating gradients and Hessian matrices.

Guillaume Alain, Yoshua Bengio

Vovk extends inductive conformal prediction to conditional guarantees and proves that exact object-conditional validity is generally inefficient.

Vladimir Vovk

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

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

Bernhard Schoelkopf, Dominik Janzing, Jonas Peters et al.

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.

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

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.

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

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

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 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.