bregman divergence

Bregman contrastive estimation as a general framework for learning unnormalized models: Efficiency, robustness and optimal noise UAI 2026 Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence JMLR 2026 Kernel-based Distributed Learning JMLR 2026 A Mirror Descent Perspective of Smoothed Sign Descent UAI 2025 Bias-variance decompositions: the exclusive privilege of Bregman divergences JMLR 2025 Binary Losses for Density Ratio Estimation ICLR 2025 Difference-of-submodular Bregman Divergence ICLR 2025 Modified K-means Algorithm with Local Optimality Guarantees ICML 2025 Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning JMLR 2025 Reassessing How to Compare and Improve the Calibration of Machine Learning Models ICLR 2025 Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss ICML 2025 WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning ICLR 2025 Configurable Mirror Descent: Towards a Unification of Decision Making ICML 2024 ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection ICLR 2024 Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors AISTATS 2024 How to Evaluate Behavioral Models AAAI 2024 Learning Bregman Divergences with Application to Robustness NeurIPS 2024 Local Composite Saddle Point Optimization ICLR 2024 Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024 Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models COLT 2024 A Bregman Divergence View on the Difference-of-Convex Algorithm AISTATS 2023 Adaptive Learning of Density Ratios in RKHS JMLR 2023 Agnostically Learning Single-Index Models using Omnipredictors NeurIPS 2023 Beta Diffusion NeurIPS 2023 Bregman Deviations of Generic Exponential Families COLT 2023