Search Results for author: Saharon Rosset

Found 14 papers, 5 papers with code

Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators

no code implementations19 Feb 2023 Oren Yuval, Saharon Rosset

The main idea is to design different mechanisms for integrating the unlabeled data, and include in each of them a mixing parameter $\alpha$, controlling the weight given to the unlabeled data.

regression

Integrating Random Effects in Deep Neural Networks

1 code implementation7 Jun 2022 Giora Simchoni, Saharon Rosset

We propose to use the mixed models framework to handle correlated data in DNNs.

Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

1 code implementation NeurIPS 2021 Giora Simchoni, Saharon Rosset

Our results show that treating high-cardinality categorical features as random effects leads to a significant improvement in prediction performance compared to state of the art alternatives.

Entity Embeddings

Trees-Based Models for Correlated Data

1 code implementation16 Feb 2021 Assaf Rabinowicz, Saharon Rosset

This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data.

regression

Semi-Supervised Empirical Risk Minimization: Using unlabeled data to improve prediction

no code implementations1 Sep 2020 Oren Yuval, Saharon Rosset

The key ideas are carefully considering the null model as a competitor, and utilizing the unlabeled data to determine signal-noise combinations where SSL outperforms both supervised learning and the null model.

regression

Surprises in High-Dimensional Ridgeless Least Squares Interpolation

no code implementations19 Mar 2019 Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani

Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type.

Vocal Bursts Intensity Prediction

Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models

1 code implementation10 Dec 2018 Aviv Navon, Saharon Rosset

This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients.

regression

Lossless (and Lossy) Compression of Random Forests

no code implementations26 Oct 2018 Amichai Painsky, Saharon Rosset

In addition, we introduce a theoretically sound lossy compression scheme, which allows us to control the trade-off between the distortion and the coding rate.

Clustering

Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds

no code implementations16 Sep 2018 Amichai Painsky, Saharon Rosset, Meir Feder

Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases, as the scale of the problem grows.

The Everlasting Database: Statistical Validity at a Fair Price

no code implementations NeurIPS 2018 Blake Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro

The problem of handling adaptivity in data analysis, intentional or not, permeates a variety of fields, including test-set overfitting in ML challenges and the accumulation of invalid scientific discoveries.

Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance

no code implementations10 Dec 2015 Amichai Painsky, Saharon Rosset

The most important consequence of our approach is that categorical variables with many categories can be safely used in tree building and are only chosen if they contribute to predictive power.

Variable Selection

When Does More Regularization Imply Fewer Degrees of Freedom? Sufficient Conditions and Counter Examples from Lasso and Ridge Regression

no code implementations12 Nov 2013 Shachar Kaufman, Saharon Rosset

Regularization aims to improve prediction performance of a given statistical modeling approach by moving to a second approach which achieves worse training error but is expected to have fewer degrees of freedom, i. e., better agreement between training and prediction error.

regression

Decomposing Isotonic Regression for Efficiently Solving Large Problems

no code implementations NeurIPS 2010 Ronny Luss, Saharon Rosset, Moni Shahar

A new algorithm for isotonic regression is presented based on recursively partitioning the solution space.

regression

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