lipschitz function

Near-optimal Delta-convex Estimation of Lipschitz Functions JMLR 2026 Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension COLT 2026 Fundamental Limits of Visual Autoregressive Transformers: Universal Approximation Abilities ICML 2025 Rethinking Neural-based Matrix Inversion: Why can’t, and Where can AISTATS 2025 Sharper Bounds for Chebyshev Moment Matching, with Applications COLT 2025 Agnostic Active Learning of Single Index Models with Linear Sample Complexity COLT 2024 Certified private data release for sparse Lipschitz functions AISTATS 2024 Corruption-Robust Lipschitz Contextual Search ALT 2024 Universality and Limitations of Prompt Tuning NeurIPS 2023 When Does Optimizing a Proper Loss Yield Calibration? NeurIPS 2023 A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions NeurIPS 2022 On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions ICML 2022 Differentially Private Pairwise Learning Revisited IJCAI 2021 Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates NeurIPS 2021 Stochastic Bias-Reduced Gradient Methods NeurIPS 2021 Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss COLT 2021 Active Local Learning COLT 2020 Comparator-Adaptive Convex Bandits NeurIPS 2020 Depth Separations in Neural Networks: What is Actually Being Separated? COLT 2019 Contextual Pricing for Lipschitz Buyers NeurIPS 2018 An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback JMLR 2017 Bandits with Movement Costs and Adaptive Pricing COLT 2017 Global optimization of Lipschitz functions ICML 2017 Optimistic Bandit Convex Optimization NeurIPS 2016 Matrix Completion Under Monotonic Single Index Models NeurIPS 2015