Proposes a broadcast-based subgradient push algorithm for distributed optimization over time-varying directed graphs, achieving an O(ln t/√t) convergence rate.

Angelia Nedic, Alex Olshevsky

Proposes an asymptotically optimal inference method for high-dimensional models using desparsified Lasso, establishing asymptotic normality and efficiency.

Sara van de Geer, Peter Bühlmann, Ya'acov Ritov et al.

This paper applies inductive conformal prediction combined with projection and pseudo-density techniques to construct distribution-free, finite-sample guaranteed prediction bands and clustering trees for functional data, enabling outlier detection and structure exploration.

Jing Lei, Alessandro Rinaldo, Larry Wasserman

Proposes a causal inference framework robust to latent variables and selection bias, based on conditional independence and inducing path analysis.

Peter L. Spirtes, Christopher Meek, Thomas S. Richardson

Proposes algorithms for causal inference with background knowledge, addressing existence and commonality of causal explanations.

Christopher Meek

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

Proposes an equivalence-class-based search space for Bayesian network structure learning, improving greedy search performance.

David Maxwell Chickering

SAGG-RIAC architecture enables active learning of inverse models in redundant robots through intrinsically motivated goal exploration.

Adrien Baranes, Pierre-Yves Oudeyer

Proposes two efficient models—CBOW and Skip-gram—for large-scale word embedding training, achieving 53.3% accuracy on semantic-syntactic tasks within 3 days on 1.6B words.

Tomas Mikolov, Kai Chen, Greg Corrado et al.

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

Ian Lenz, Honglak Lee, Ashutosh Saxena

Unified geometric framework for Blackwell approachability, regret minimization, and calibration, enabling efficient multi-objective sequential decision strategies.

Vianney Perchet

Expectation Propagation (EP) unifies assumed-density filtering and loopy belief propagation for efficient approximate Bayesian inference in hybrid networks, outperforming Laplace, VB, and Monte Carlo.

Thomas P. Minka

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

Introduces non-commutative Khinchin inequalities, relaxes k/n→0 condition for series estimators, establishing pointwise and uniform asymptotic results.

Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov et al.

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

Guillaume Alain, Yoshua Bengio

Introduces OSNAP, a sparse subspace embedding with dimension O(d^2/ε^2), supporting s=1 sparsity, enabling faster linear algebra algorithms.

Jelani Nelson, Huy L. Nguyen

Introduces an efficient inference algorithm for fully connected CRFs, enhancing image segmentation accuracy.

Philipp Krähenbühl, Vladlen Koltun

Proposes a graph-based discrete signal processing framework using adjacency matrices and Jordan form, enabling spectral analysis for directed and weighted graphs.

Aliaksei Sandryhaila, Jose M. F. Moura

Self-Delimiting Neural Networks use threshold activation functions and halt neurons for efficient learning.

Juergen Schmidhuber

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

Vladimir Vovk