Survey on data-driven grasp synthesis methods, categorized by known, familiar, and unknown objects.

Jeannette Bohg, Antonio Morales, Tamim Asfour et al.

Four gradient estimators for stochastic binary neurons enable efficient conditional computation and sparsity in deep networks.

Yoshua Bengio, Nicholas Léonard, Aaron Courville

Using spinor helicity and BCFW recursion, the paper simplifies multi-particle scattering amplitudes, improving computational efficiency.

Henriette Elvang, Yu-tin Huang

Introduces a Linearized Phrase Structure model to improve semantic orientation detection in financial texts.

Pekka Malo, Ankur Sinha, Pyry Takala et al.

Introduces Exp3-DOM algorithm leveraging dominating sets to bound regret in directed, dynamic observation graphs (under 30 words)

Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile et al.

SLIM combines 0–1 loss, L0 sparsity, and integer constraints to produce accurate, hand-computable scoring systems.

Berk Ustun, Stefano Tracà, Cynthia Rudin

Proposes a loss-function-based Bayesian updating framework, suitable for models with incomplete information or parameters not directly linked to densities.

Pier Giovanni Bissiri, Chris Holmes, Stephen Walker

Modern proof of Hanson-Wright inequality for sub-gaussian quadratic forms, deriving concentration bounds for high-dimensional vectors and matrices.

Mark Rudelson, Roman Vershynin

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

Proposed graph-cluster randomization method significantly reduces estimator variance under network interference.

Johan Ugander, Brian Karrer, Lars Backstrom et al.

Using survival theory to model information propagation, proposing additive and multiplicative risk models for efficient network inference.

Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf

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.

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

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.