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Found 18 papers, 11 papers with code
Conformal Prediction via Regression-as-Classification
Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed.
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e. g. performance on another dataset, robustness, agreement with a prior).
Finite Sample Confidence Regions for Linear Regression Parameters Using Arbitrary Predictors
We explore a novel methodology for constructing confidence regions for parameters of linear models, using predictions from any arbitrary predictor.
Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection
From its properties, we intuit that equality separation is suitable for anomaly detection.
Conformalization of Sparse Generalized Linear Models
Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant.
Exact and Approximate Conformal Inference in Multiple Dimensions
It is common in machine learning to estimate a response y given covariate information x.
A Confidence Machine for Sparse High-Order Interaction Model
In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable.
Stable Conformal Prediction Sets
When one observes a sequence of variables $(x_1, y_1), \ldots, (x_n, y_n)$, Conformal Prediction (CP) is a methodology that allows to estimate a confidence set for $y_{n+1}$ given $x_{n+1}$ by merely assuming that the distribution of the data is exchangeable.
Continuation Path with Linear Convergence Rate
Path-following algorithms are frequently used in composite optimization problems where a series of subproblems, with varying regularization hyperparameters, are solved sequentially.
Root-finding Approaches for Computing Conformal Prediction Set
Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features.
Screening Rules and its Complexity for Active Set Identification
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning.
Computing Full Conformal Prediction Set with Approximate Homotopy
If you are predicting the label $y$ of a new object with $\hat y$, how confident are you that $y = \hat y$?
Safe Grid Search with Optimal Complexity
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task.
GAP Safe Screening Rules for Sparse-Group Lasso
For statistical learning in high dimension, sparse regularizations have proven useful to boost both computational and statistical efficiency.
Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term.
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance.
GAP Safe Screening Rules for Sparse-Group-Lasso
We adapt to the case of Sparse-Group Lasso recent safe screening rules that discard early in the solver irrelevant features/groups.
GAP Safe screening rules for sparse multi-task and multi-class models
The GAP Safe rule can cope with any iterative solver and we illustrate its performance on coordinate descent for multi-task Lasso, binary and multinomial logistic regression, demonstrating significant speed ups on all tested datasets with respect to previous safe rules.
