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Found 13 papers, 1 papers with code
Unbiased Objective Estimation in Predictive Optimization
Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i. e., the value of the objective function is overestimated.
Scalable Model Selection for Belief Networks
We propose a scalable algorithm for model selection in sigmoid belief networks (SBNs), based on the factorized asymptotic Bayesian (FAB) framework.
Distributed Bayesian Piecewise Sparse Linear Models
The importance of interpretability of machine learning models has been increasing due to emerging enterprise predictive analytics, threat of data privacy, accountability of artificial intelligence in society, and so on.
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis.
Human Activity Recognition
Vocal Bursts Intensity Prediction
On The Projection Operator to A Three-view Cardinality Constrained Set
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing.
Large-Scale Price Optimization via Network Flow
On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms.
Optimization Beyond Prediction: Prescriptive Price Optimization
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning.
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models
Factorial hidden Markov models (FHMMs) are powerful tools of modeling sequential data.
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Our analysis provides a formal justification of FIC as a model selection criterion for LVMs and also a systematic procedure for pruning redundant latent variables that have been removed heuristically in previous studies.
Exclusive Feature Learning on Arbitrary Structures via \ell_{1,2}-norm
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level.
Partition-wise Linear Models
One of the key challenges in their use is non-convexity in simultaneous optimization of regions and region-specific models.
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Our new bounds are consistent with the bounds of a special case (least squares) and fills a previously existing theoretical gap for general convex smooth functions; 3) We show that the restricted strong convexity condition is satisfied if the number of independent samples is more than $\bar{k}\log d$ where $\bar{k}$ is the sparsity number and $d$ is the dimension of the variable; 4) We apply FoBa-gdt (with the conditional random field objective) to the sensor selection problem for human indoor activity recognition and our results show that FoBa-gdt outperforms other methods (including the ones based on forward greedy selection and L1-regularization).
Factorized Asymptotic Bayesian Inference for Latent Feature Models
This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models~(LFMs).
