Describes MLC search spaces in MEKA and WEKA software.
problem Understanding MLC algorithms and their transformations into SLC problems.
method Overviewed 26 MLC algorithms and 28 SLC algorithms, proposed a context-free grammar.
result Formal description of MLC search spaces and their transformations.
This paper automates multi-label classification using an extended AutoML tool.
problem Lack of automated solutions for multi-label classification.
method Extends ML-Plan for multi-class classification to multi-label classification using MEKA.
result The proposed approach yields superb results and performs significantly better than baselines.
Scikit-multiflow is a Python framework for multi-output/stream data mining.
problem Handling multi-output/stream data efficiently.
method Multi-output/multi-label stream data mining framework with state-of-the-art methods.
result Enables democratization of stream learning research.
Efficiently learns Ising models with missing data.
problem Learning Ising models with missing data due to independent failures.
method Developed a novel unbiased estimator for the ISO gradient and applied stochastic multiplicative gradient descent.
result Matches optimal runtime and sample complexity bounds for learning Ising models.
Paper adapts multiplicative weights method to Gaussian graphical models.
problem Graphical model selection in Gaussian random fields.
method Adapted multiplicative weights method from Ising model to Gaussian model.
result Achieves sample complexity bound similar to existing methods.
Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.
problem Learning parameters of random graphical models at low temperatures.
method Multiplicative-weight update algorithm for polynomial time learning.
result Algorithm learns SK model parameters at β ≤ log n β\leq \sqrt{\log n} β ≤ log n . Hard problem of learning simple generative models from i.i.d. samples.
problem Learning simple neural network distributions from samples.
method Statistical query model, ODE-based construction of piecewise-linear functions.
result No polynomial-time algorithm can solve this problem even with one-hidden-layer ReLU networks.
Quantifies polynomial approximation rates for smooth functions under various distributions.
problem Approximating smooth functions with polynomials under different distributional constraints.
method Develops a quantitative analogue of Carleman's theorem using complex analysis.
result Establishes superexponential rates of approximation for certain function classes over general distributions.
Efficiently estimates linear models robust to corrupted data.
problem Learning linear models under adversarial corruption and minimal distributional assumptions.
method Develops a polynomial relaxation of independence to achieve optimal convergence rate.
result Achieves optimal convergence rate of ε 2 − 2 / k ε^{2-2/k} ε 2 − 2/ k for k k k -hypercontractive distributions. Given a large data matrix A ∈ R n × n A\in\mathbb{R}^{n\times n} A ∈ R n × n , we consider the problem of determining whether its entries are i.i.d. with some known marginal distribution A i j ∼ P 0 A_{ij}\sim P_0 A ij ∼ P 0 , or instead A A A contains a principal submatrix A Q , Q A_{{\sf Q},{\sf Q}} A Q , Q whose entries have marginal distribution A i j ∼ P 1 ≠ P 0 A_{ij}\sim P_1\neq P_0 A ij ∼ P 1 = P 0 . As …