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Found 10 papers, 1 papers with code
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
We propose such a pre-processing step for the PC algorithm which relies on performing CI tests on a few randomly selected large conditioning sets.
Estimation of Entropy in Constant Space with Improved Sample Complexity
Recent work of Acharya et al. (NeurIPS 2019) showed how to estimate the entropy of a distribution $\mathcal D$ over an alphabet of size $k$ up to $\pm\epsilon$ additive error by streaming over $(k/\epsilon^3) \cdot \text{polylog}(1/\epsilon)$ i. i. d.
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Our goal is to recover the directions of all causal or ancestral relations in $G$, via a minimum cost set of interventions.
Efficient Intervention Design for Causal Discovery with Latents
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process.
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters.
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Our second approach uses algebraic and combinatorial tools and applies to binomial mixtures with shared trial parameter $N$ and differing success parameters, as well as to mixtures of geometric distributions.
Sample Complexity of Learning Mixture of Sparse Linear Regressions
Ourtechniques are quite different from those in the previous work: for the noiselesscase, we rely on a property of sparse polynomials and for the noisy case, we providenew connections to learning Gaussian mixtures and use ideas from the theory of
Sample Complexity of Learning Mixtures of Sparse Linear Regressions
In the problem of learning mixtures of linear regressions, the goal is to learn a collection of signal vectors from a sequence of (possibly noisy) linear measurements, where each measurement is evaluated on an unknown signal drawn uniformly from this collection.
Mesh: Compacting Memory Management for C/C++ Applications
Programs written in C/C++ can suffer from serious memory fragmentation, leading to low utilization of memory, degraded performance, and application failure due to memory exhaustion.
Programming Languages Data Structures and Algorithms Performance
Compact Representation of Uncertainty in Clustering
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
