New bounds for weighted ERM in networked data.
problem Learning from networked data with unknown target values.
method General weighted ERM, new universal risk bounds, FPTAS.
result Appropriate weights for networked examples.
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48 results for “weighted ERM”
A new DP algorithm for weighted ERM protects sensitive data in predictive models.
problem Protecting sensitive personal information in predictive models trained via ERM.
method Proposes the first differentially private algorithm for weighted ERM with formal privacy guarantees.
result Demonstrates strong DP guarantees while maintaining robust performance in real-world data.
Reweighting improves risk bounds in certain data regions.
problem Improving risk bounds in classification and heteroscedastic regression.
method Weighted empirical risk minimization with a data-dependent weight function.
result A weighted ERM estimator can achieve superior performance in specific sub-regions.
Paper proposes WD-DP ERM for distributed learning with improved privacy and performance.
problem Training models in distributed settings with privacy and performance guarantees.
method Weighted distributed differential privacy (WD-DP) for ERM, considering different weights of clients.
result Improved noise bound and excess empirical risk bound in distributed settings.
New algorithm reduces ERM problem size while maintaining accuracy.
problem Empirical risk minimization problem size reduction.
method Adaptive Deterministic Uniform-Weight Trimming (ADUWT) algorithm.
result Uniform relative-error approximation for ERM objective.
NAPP-ERM improves ERM with differential privacy guarantees by iteratively achieving target regularization and delivering strong convexity.
problem Over-regularization in privacy-preserving ERM approaches.
method Noise-Augmented Privacy-Preserving Empirical Risk Minimization (NAPP-ERM) with a dual-purpose l2 regularizer and privacy budget retrieval strategy.
result Mitigates over-regularization and achieves strong convexity through a single regularizer.
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithmcs.LG
We analyze Gibbs-based transfer learning algorithms using information theory.
problem Understanding the generalization error of transfer learning.
method Information-theoretic analysis focusing on -weighted-ERM and two-stage-ERM.
result Exact characterization of generalization behavior using conditional symmetrized KL information.
Method identifies change points in high-dimensional models using sample weights.
problem Identifying change points in high-dimensional generalized linear models.
method Sample-weighted empirical risk minimization (Weighted ERM).
result Weighted ERM yields precise asymptotic performance characterization for Gaussian designs.
ERM struggles with synthetic data, but some algorithms can still learn correctly.
problem Learning from a mix of natural and synthetic data.
method Modeling the scenario as a sequence of learning tasks with oblivious algorithms, studying ERM and its limitations.
result ERM converges to the true mean but is outperformed by weighted algorithms. ERM does not always converge in the PAC setting, but there are algorithms capable of learning the correct hypothesis.
Recent work shows GRW approaches do not improve over ERM in distributional shift.
problem Improving robustness to distributional shift in machine learning models.
method Generalized Reweighting (GRW) algorithms, which iteratively update model parameters based on reweighting of training samples.
result GRW approaches do not significantly improve over ERM in real applications with distribution shift.
We consider the problem of adaptation to the margin and to complexity in binary classification. We suggest an exponential weighting aggregation scheme. We use this aggregation procedure to construct classifiers which adapt automatically to margin and complexity. Two main examples are worked out in which adaptivity is a…
This guide simplifies high-probability regret bounds in empirical risk minimization.
problem High-probability regret bounds in empirical risk minimization.
method Modular presentation, three-step recipe, localized Rademacher complexity, local maximal inequalities, metric-entropy integrals.
result Recover familiar rates for various function classes and derive regret bounds for nuisance components.
PAIR optimizes machine learning models to generalize better to out-of-distribution data.
problem Optimization of machine learning models for out-of-distribution (OOD) generalization often leads to compromises that weaken robustness.
method Introduces a multi-objective optimization (MOO) perspective and a new optimization scheme called PAreto Invariant Risk Minimization (PAIR).
result PAIR improves robustness of OOD objectives by cooperatively optimizing with other objectives, yielding top OOD performances.
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learningstat.ML
New guarantees for ERM with adaptively collected data.
problem Failure of ERM guarantees with adaptively collected data.
method Importance sampling weighted ERM algorithm with maximal inequality.
result First generalization guarantees and fast convergence rates for adaptively collected data.
The study analyzes multi-class teacher-student perceptron performance and generalization errors.
problem Analyzing multi-class classification with the teacher-student perceptron.
method Deriving asymptotic expressions for Bayes-optimal and empirical risk minimization (ERM) generalization errors.
result Regularised cross-entropy minimization yields close-to-optimal accuracy for multi-class classification.
Entropy asymmetry affects regularization in ERM, leading to biased solutions.
problem Analyzing the impact of relative entropy asymmetry in ERM regularization.
method Examined Type-I and Type-II ERM-RER, comparing their solutions and properties.
result Type-II ERM-RER regularization introduces a strong bias against training data.
Simple DP algorithms find approximate solutions for nonconvex ERM.
problem Finding approximate solutions to nonconvex ERM problems with privacy.
method Differential privacy, descent directions, line search, mini-batching, two-phase strategy.
result Effective algorithms for nonconvex ERM with privacy guarantees.
Study on Privileged ERM showing limitations and providing capacity analysis.
problem Improving classification accuracy with privileged information.
method Theoretical analysis of Privileged ERM using VC dimension and generalization bounds.
result Worst-case guarantees for Privileged ERM cannot improve over standard ERM unless privileged information capacity is similar or smaller.
New dual formulation reduces generalization error for ERM-fDR.
problem Generalization error in constrained optimization problems.
method Introduces a dual formulation of ERM-fDR using Legendre-Fenchel transform and implicit function theorem.
result Explicit characterizations of generalization error for algorithms under mild conditions.
New method integrates real and synthetic data to improve machine learning models.
problem Expensive or impractical collection of high-quality data limits machine learning.
method Weighted empirical risk minimization approach for integrating surrogate data.
result Integrating surrogate data can significantly reduce test error on the original distribution.
Paper shows ERM's suboptimality due to bias, not variance.
problem Understanding why ERM fails to achieve optimal rates.
method Probabilistic and admissibility proofs for ERM in various settings.
result ERM's suboptimality is due to bias, not variance.
No best k-layer neural network approximations exist in general for common activations.
problem The non-existence of best k-layer neural network approximations for common activation functions.
method Empirical Risk Minimization (ERM) for neural networks with k layers and various activation functions.
result For k = 2 and common activations like ReLU, hyperbolic tangent, and sigmoid, the ERM infimum is not attainable in general.
This work analyzes IRM and ERM from sample complexity perspective, revealing different behaviors under various distribution shifts.
problem Choosing between IRM and ERM for OOD generalization.
method Sample complexity analysis comparing IRM and ERM under different data generation mechanisms.
result IRM is preferred over ERM for certain distribution shifts, leading to better OOD generalization.
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networkscs.CC
Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there has been a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems…
New algorithm for privacy-preserving nonconvex optimization.
problem Privacy-preserving nonconvex empirical risk minimization.
method Differentially private stochastic gradient descent algorithm.
result Achieves strong privacy guarantees efficiently with improved utility.
Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Informationmath.OC
Paper proposes a new method for SP with covariates using PADR and ERM.
problem Stochastic programming with covariate information.
method Empirical risk minimization (ERM) with nonconvex piecewise affine decision rules (PADR).
result The method provides theoretical consistency and computational tractability for nonconvex SP problems.
Study on tradeoffs between mistakes and ERM oracle calls in online and transductive learning.
problem Analyzing online and transductive learning with limited ERM and weak consistency oracle access.
method Proves lower bounds and upper bounds on mistakes and oracle calls, considering realizable and agnostic cases.
result Achieves optimal mistake bounds with weak consistency queries for certain concept classes.
This paper studies universal rates of ERM for binary classification under agnostic learning.
problem The challenge of achieving universal rates of ERM for binary classification under agnostic learning.
method The paper explores the agnostic universal rates of ERM for binary classification, revealing three possible rates: , , or arbitrarily slow.
result The paper provides a complete characterization of which concept classes fall into each of the three categories of agnostic universal rates.
Default-ERM shortcut learning persists even without additional information.
problem Default-ERM shortcut learning in perception tasks despite stable feature sufficiency.
method Studied linear perception task; developed margin control (MARG-CTRL) loss functions.
result Margin control mitigates shortcut learning on various tasks.
Dual optimization connects ERM-fDR to normalization function.
problem Empirical risk minimization with f-divergence regularization.
method Dual formulation, Legendre-Fenchel transform, implicit function theorem, nonlinear ODE.
result Computational method to calculate normalization function efficiently.
Paper presents ERM with -divergence regularization and its properties.
problem Minimizing empirical risk with -divergence constraints.
method Introduces normalization function and solves ERM-DR via ODE.
result Characterizes difference between empirical risks and provides numerical algorithm.
Develops a new learning framework for dynamic data.
problem Poor performance of existing strategies in dynamic data and goals.
method Prospective Learning framework and Prospective ERM algorithm.
result Prospective ERM converges to Bayes risk under certain assumptions.
Simpler majority vote of three classifiers achieves optimal error bounds.
problem Developing an optimal PAC learning algorithm in the realizable setting.
method Returning the majority vote of three ERM classifiers.
result Achieves optimal in-expectation bound on error.
A new federated learning algorithm improves on existing methods by exploiting data smoothness.
problem Federated learning optimization with smooth loss functions.
method Federated Low Rank Gradient Descent (FedLRGD) algorithm.
result FedLRGD outperforms Federated Averaging (FedAve) in federated oracle complexity under certain conditions.
Improved algorithm reduces excess risk in selective learning.
problem Selective learning with windowed model selection.
method Hybrid Exponential Weights Algorithm and bounded-recall ERM.
result Achieves expected excess risk of O((log log |L| + log log n) / log n).
Paper explores universal rates of ERM in machine learning.
problem Understanding universal learning rates for ERM.
method Analyzes realizable concept classes and ERM principles.
result Four possible universal learning rates by ERM.
Algorithm solves online binary classification and infinite games using ERM oracle.
problem Online learning and solving infinite games with computationally inefficient oracles.
method Proposes an algorithm relying solely on ERM oracle calls for online binary classification and nonparametric games.
result Achieves finite and sublinearly growing regret in various settings.
Upper bounds and lower bounds show ERM outperforms DG methods in various settings.
problem Limitations of domain generalisation methods in various settings.
method Upper bounds and lower bounds on excess risk of ERM, and analysis of DG settings.
result It is not possible to significantly outperform ERM in DG settings.
Study risk bounds for distributed ERM with general loss functions and hypothesis spaces.
problem Limited theoretical analysis for distributed ERM with general loss functions and hypothesis spaces.
method Derive tight risk bounds under assumptions on hypothesis space and loss function.
result Developed more general risk bound for distributed ERM without strong convexity restriction.
ERM with square loss achieves sublinear error for learnable function classes with smoothed data.
problem Statistical and computational hardness in sequential decision-making.
method Empirical Risk Minimization (ERM) with square loss, focusing on unknown base measure and smooth data.
result ERM achieves error scaling as for learnable function classes.
Sharp bounds on ERM's minimal error in regression.
problem Understanding ERM's performance in regression tasks.
method Sharp lower bounds for ERM in random and fixed design settings.
result ERM's performance depends on the global or local complexity of the model.
FeAT improves OOD generalization by learning richer features.
problem Improving feature learning for out-of-distribution (OOD) generalization.
method Feature Augmented Training (FeAT) iteratively augments and retains features from different subsets of training data.
result FeAT effectively learns richer features, boosting OOD performance.
AFR simplifies reducing reliance on spurious features, improving model performance.
problem Reducing reliance on spurious features for out-of-distribution generalization.
method Automatic Feature Reweighting (AFR) updates the model with a weighted loss.
result AFR improves model performance on benchmarks with minimal compute.
Online TERM improves robustness and fairness in streaming data.
problem Streaming data's lack of worst-case fairness and robustness in ERM.
method Proposes an online TERM formulation to balance average-case accuracy with worst-case fairness and robustness.
result Negative tilting effectively suppresses outlier influence, positive tilting improves recall with minimal precision loss.
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimizationcs.LG
A new distributed optimization method for ERM problems.
problem Efficiently solving ERM problems with nonsmooth regularization in a distributed setting.
method Second-order distributed optimization using successive quadratic approximations and Hessian approximation.
result Global linear convergence for a broad range of non-strongly convex problems.
This paper develops fast rates for ERM and SA algorithms under error bound conditions.
problem Developing fast and adaptive optimization algorithms for statistical learning.
method Empirical Risk Minimization (ERM) and Stochastic Approximation (SA) algorithms with fast convergence rates under error bound conditions.
result Fast and adaptive convergence rates for ERM and SA algorithms, spanning from to , depending on error bound conditions.
Study identifies and analyzes spurious correlations in data-driven models.
problem Spurious correlations in data-driven models are unreliable and hard to detect.
method Collect and analyze synthetic datasets generated from causal graphs to investigate spurious correlations.
result Patterns connecting spurious correlation hypotheses and model design choices were observed.
ERM uses energy-based selection to improve recursive reasoning.
problem Lack of principled inference mechanism in recursive models.
method Energy-guided Recursive Model (ERM) introduces Hopfield energies for trajectory selection.
result ERM achieves optimal solutions on various puzzles.