#TwoBlock
LightNews LightNews
cutgen.bsky.social
one of these seven photos is Sam's real hair. the other six are not 👀
guess the number, then upload your own photo and see six on your face.

cutgen.ai

#aihair #twoblock #menshair
October 1, 2026 at 12:00 AM
cutgen.bsky.social
six ways to style hiroki's thick hair ⚡
test any of these cuts on your head in seconds.

cutgen.ai

#twoblock #mullet #menshairstyles
September 15, 2026 at 5:40 PM
paperposterbot.bsky.social
arXiv📈🤖
Twoblock clustering trees with coskewness-based dimension reduction: recovering piecewise multivariate linear regimes
By Serneels
The twoblock clustering tree (\tbtree) is introduced as a highly interpretable regression tree for multivariate responses. Twoblock trees are deterministic decision trees that have local multivariate linear models as their leaves and use dense or sparse twoblock dimension reduction as local leaf models and in the impurity. The resulting models are both computationally efficient and can be highly interpretable. Beyond proposing the decision tree estimator itself, this paper also introduces an estimator for the twoblock dimension reduced space based on maximizing coskewness, which facilitates identification of non-normal clusters in the data. The tree inherently produces a set of local linear models and is therefore apt to recover peicewise linear regimes, which is illustrated in a simulation. However, two real world data examples illustrate that twoblock trees are also capable of modeling more complexly nonlinear dependencies and can perform on par with black box modeling techniques, such as random forests. At each point, both the twoblock models that generate the splits, as well as the ones in the leaves, can be inspected and interpreted.
July 24, 2026 at 4:20 AM
paperposterbot.bsky.social
arXiv📈🤖
Cellwise Robust Twoblock Dimension Reduction
By Serneels
Cellwise Robust Twoblock (CRTB) is introduced, the first cellwise robust method for simultaneous dimension reduction of multivariate predictor and response blocks, in both a dense and a sparse variable-selecting variant. Classical robust methods protect against casewise outliers by downweighting or removing entire observations, a strategy that becomes inefficient -- and eventually breaks down -- when contamination is scattered across individual cells rather than concentrated in whole rows. CRTB combines a column-wise pre-filter for cellwise outlier detection with model-based imputation of flagged cells inside an iteratively reweighted M-estimation loop, retaining the clean cells of partially contaminated rows instead of discarding the observation. An efficient algorithm is provided that uses the classical twoblock SVD as a warm start and converges in a handful of IRLS iterations at a moderate computational cost. The method resists settings where more than $50\%$ of rows contain contaminated cells while retaining comparable efficiency on clean data. A simulation study confirms these properties and shows that CRTB additionally recovers the underlying cellwise outlier pattern with high fidelity and, in the sparse setting, the correct set of informative variables. Two compelling examples illustrate CRTB's practical utility. In each of these, CRTB is shown to be conducive to results that are highly interpretable in the respective domains in the presence of cellwise outliers. As a by-product, the corresponding cells are identified with high fidelity.
April 17, 2026 at 4:41 AM
paperposterbot.bsky.social
arXiv📈🤖
Robust Twoblock Simultaneous Dimension Reduction
By Serneels
This paper introduces robust twoblock (RTB) simultaneous dimension reduction, which is the first statistically robust method to perform simultaneous dimension reduction in two blocks of variables and allows to fine-tune the model complexity in each block individually. The paper proposes both a dense and a sparse version of the new method. Sparse RTB is the first robust estimator that allows to select both model complexity and the degree of sparsity for each block individually. RTB thereby allows to optimally extract and summarize the relevant portion of information in each block of data, also in the presence of outliers. As a corollary, the estimators can be recombined into a single estimate of regression coefficients for multivariate regression that is operable when the number of variables exceeds the number of cases in each block. An extensive simulation study illustrates that the new methods are resistant to different types of outliers, while maintaining estimation efficiency. across a range of dimensionality settings. These findings both hold true for the dense and the sparse method. The methods' performance is further illustrated on two example data sets and a straightforward algorithm is presented and made accessible in an open source repository.
March 27, 2026 at 4:45 PM
paperposterbot.bsky.social
link 📈🤖
Sparse twoblock dimension reduction for simultaneous compression and variable selection in two blocks of variables (Serneels) A method is introduced to perform simultaneous sparse dimension reduction on two blocks of variables. Beyond dimension reduction, it also yields an estimator for m
November 28, 2024 at 4:09 PM
jaykita.bsky.social
nb asian bobcut vs nb asian twoblock (i was the twoblock)
March 4, 2026 at 9:17 PM
jaykita.bsky.social
rip twoblock cut jay this was prob the most genderfluid ive ever looked
January 19, 2026 at 6:03 AM
wekun.bsky.social
Spiral Perm × Two-Block | Short Hair with Dimensional Highlights✂️

Two-block undercut with a tight spiral perm and bleached highlights & lowlights. Bold texture, natural movement, and easy styling.

#SpiralPerm #TwoBlock #ShortHair #Highlights#tsunenorikawai#川井恒徳
#fiorista#japanmade#madeinjapan
July 18, 2026 at 6:21 AM
statme-bot.bsky.social
Sven Serneels: Twoblock clustering trees with coskewness-based dimension reduction: recovering piecewise multivariate linear regimes https://arxiv.org/abs/2607.20760 https://arxiv.org/pdf/2607.20760 https://arxiv.org/html/2607.20760
July 24, 2026 at 7:02 AM
statme-bot.bsky.social
Sven Serneels: Cellwise Robust Twoblock Dimension Reduction https://arxiv.org/abs/2604.15106 https://arxiv.org/pdf/2604.15106 https://arxiv.org/html/2604.15106
April 17, 2026 at 7:03 AM
statme-bot.bsky.social
Sven Serneels: Robust Twoblock Simultaneous Dimension Reduction https://arxiv.org/abs/2603.24820 https://arxiv.org/pdf/2603.24820 https://arxiv.org/html/2603.24820
March 27, 2026 at 6:52 AM
arxiv-stat-me.bsky.social
Sven Serneels
Twoblock clustering trees with coskewness-based dimension reduction: recovering piecewise multivariate linear regimes
https://arxiv.org/abs/2607.20760
July 25, 2026 at 2:49 AM
arxiv-stat-me.bsky.social
Sven Serneels
Sparse twoblock dimension reduction for simultaneous compression and variable selection in two blocks of variables
https://arxiv.org/abs/2411.17859
November 28, 2024 at 5:01 AM