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Found 15 papers, 2 papers with code
What Twitter Data Tell Us about the Future?
This study aims to investigate the futures projected by futurists on Twitter and explore the impact of language cues on anticipatory thinking among social media users.
Exact Non-Oblivious Performance of Rademacher Random Embeddings
This paper revisits the performance of Rademacher random projections, establishing novel statistical guarantees that are numerically sharp and non-oblivious with respect to the input data.
Robust and Provable Guarantees for Sparse Random Embeddings
In this work, we improve upon the guarantees for sparse random embeddings, as they were recently provided and analyzed by Freksen at al. (NIPS'18) and Jagadeesan (NIPS'19).
Mean-Squared Accuracy of Good-Turing Estimator
This work characterizes the maximal mean-squared error of the Good-Turing estimator, for any sample \emph{and} alphabet size.
Confidence-Optimal Random Embeddings
The seminal result of Johnson and Lindenstrauss on random embeddings has been intensively studied in applied and theoretical computer science.
Bernstein-Type Bounds for Beta Distribution
This work obtains sharp closed-form exponential concentration inequalities of Bernstein type for the ubiquitous beta distribution, improving upon sub-gaussian and sub-gamma bounds previously studied in this context.
Probability Statistics Theory Applications Statistics Theory 60E05 G.3
Random Embeddings with Optimal Accuracy
This work constructs Jonson-Lindenstrauss embeddings with best accuracy, as measured by variance, mean-squared error and exponential concentration of the length distortion.
A Modern Analysis of Hutchinson's Trace Estimator
The paper establishes the new state-of-art in the accuracy analysis of Hutchinson's trace estimator.
Simple Analysis of Johnson-Lindenstrauss Transform under Neuroscience Constraints
The paper re-analyzes a version of the celebrated Johnson-Lindenstrauss Lemma, in which matrices are subjected to constraints that naturally emerge from neuroscience applications: a) sparsity and b) sign-consistency.
Revisiting Concentration of Missing Mass
We revisit the problem of \emph{missing mass concentration}, developing a new method of estimating concentration of heterogenic sums, in spirit of celebrated Rosenthal's inequality.
Revisiting Initialization of Neural Networks
The proper initialization of weights is crucial for the effective training and fast convergence of deep neural networks (DNNs).
Efficient Sampled Softmax for Tensorflow
This short paper discusses an efficient implementation of \emph{sampled softmax loss} for Tensorflow.
Bounds on Bayes Factors for Binomial A/B Testing
Bayes factors, in many cases, have been proven to bridge the classic -value based significance testing and bayesian analysis of posterior odds.
Kernel Density Estimation Bias under Minimal Assumptions
The contribution of this paper is twofold (a) we demonstrate that, when the bandwidth is an arbitrary invertible matrix going to zero, it is necessary to keep a certain balance between the \emph{kernel decay} and \emph{magnitudes of bandwidth eigenvalues}; in fact, without the sufficient decay the estimates may not be even bounded (b) we give a rigorous derivation of bounds with explicit constants for the bias, under possibly minimal assumptions.
Simple Root Cause Analysis by Separable Likelihoods
Root Cause Analysis for Anomalies is challenging because of the trade-off between the accuracy and its explanatory friendliness, required for industrial applications.
