Memory Networks integrate inference and long-term memory to enhance QA task performance.

Jason Weston, Sumit Chopra, Antoine Bordes

The paper proposes Bandit algorithms for tree search, improving the over-optimism issue of the UCT algorithm.

Pierre-Arnuad Coquelin, Remi Munos

This survey introduces a taxonomy for multi-objective sequential decision-making, classifying algorithms based on scenarios, scalarization functions, and policy types.

Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson et al.

Analyzes belief updating with sets of probabilities under minimax, emphasizing rectangularity for time consistency and calibration.

Peter D Grunwald, Joseph Y Halpern

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

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

David Maxwell Chickering

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 ALE platform evaluating 55+ Atari games, combining reinforcement learning and planning for general AI assessment.

Marc G. Bellemare, Yavar Naddaf, Joel Veness et al.

Proposes Conservative PC (CPC) algorithm relying only on adjacency faithfulness, improving causal inference accuracy.

Joseph Ramsey, Jiji Zhang, Peter L. Spirtes

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

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

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

M. T. J. Spaan, N. Vlassis

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

P. Gorniak, D. Roy

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.