Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
Fuente:
arXiv
Saved in:
| Main Authors: | Chandrasekaran, Gautam, Klivans, Adam R., Lee, Lin Lin, Stavropoulos, Konstantinos |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Efficient Discrepancy Testing for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2024)
by: Chandrasekaran, Gautam, et al.
Published: (2024)
Iterative Chow Filtering for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2026)
by: Chandrasekaran, Gautam, et al.
Published: (2026)
Testable Learning with Distribution Shift
by: Klivans, Adam R., et al.
Published: (2023)
by: Klivans, Adam R., et al.
Published: (2023)
A Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube
by: Chandrasekaran, Gautam, et al.
Published: (2025)
by: Chandrasekaran, Gautam, et al.
Published: (2025)
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds
by: Klivans, Adam R., et al.
Published: (2024)
by: Klivans, Adam R., et al.
Published: (2024)
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
by: Klivans, Adam R., et al.
Published: (2026)
by: Klivans, Adam R., et al.
Published: (2026)
Learning Juntas under Markov Random Fields
by: Chandrasekaran, Gautam, et al.
Published: (2025)
by: Chandrasekaran, Gautam, et al.
Published: (2025)
Learning the Sherrington-Kirkpatrick Model Even at Low Temperature
by: Chandrasekaran, Gautam, et al.
Published: (2024)
by: Chandrasekaran, Gautam, et al.
Published: (2024)
Testing Noise Assumptions of Learning Algorithms
by: Goel, Surbhi, et al.
Published: (2025)
by: Goel, Surbhi, et al.
Published: (2025)
The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination
by: Klivans, Adam R., et al.
Published: (2025)
by: Klivans, Adam R., et al.
Published: (2025)
Learning Constant-Depth Circuits in Malicious Noise Models
by: Klivans, Adam R., et al.
Published: (2024)
by: Klivans, Adam R., et al.
Published: (2024)
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
by: Chandrasekaran, Gautam, et al.
Published: (2025)
by: Chandrasekaran, Gautam, et al.
Published: (2025)
Sparse Linear Regression is Easy on Random Supports
by: Chandrasekaran, Gautam, et al.
Published: (2025)
by: Chandrasekaran, Gautam, et al.
Published: (2025)
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
by: Goel, Surbhi, et al.
Published: (2024)
by: Goel, Surbhi, et al.
Published: (2024)
Efficient Calibration for Decision Making
by: Gopalan, Parikshit, et al.
Published: (2025)
by: Gopalan, Parikshit, et al.
Published: (2025)
Learning $\mathsf{AC}^0$ Under Graphical Models
by: Chandrasekaran, Gautam, et al.
Published: (2026)
by: Chandrasekaran, Gautam, et al.
Published: (2026)
The Importance of Being Smoothly Calibrated
by: Gopalan, Parikshit, et al.
Published: (2026)
by: Gopalan, Parikshit, et al.
Published: (2026)
Efficient and Provable Algorithms for Covariate Shift
by: Adil, Deeksha, et al.
Published: (2025)
by: Adil, Deeksha, et al.
Published: (2025)
Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors
by: Johnson, Ezra, et al.
Published: (2025)
by: Johnson, Ezra, et al.
Published: (2025)
Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph
by: Kamath, Gautam, et al.
Published: (2025)
by: Kamath, Gautam, et al.
Published: (2025)
An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
by: Dehghankar, Mohsen, et al.
Published: (2024)
by: Dehghankar, Mohsen, et al.
Published: (2024)
SoS Certifiability of Subgaussian Distributions and its Algorithmic Applications
by: Diakonikolas, Ilias, et al.
Published: (2024)
by: Diakonikolas, Ilias, et al.
Published: (2024)
Spectral Guarantees for Adversarial Streaming PCA
by: Price, Eric, et al.
Published: (2024)
by: Price, Eric, et al.
Published: (2024)
Dynamic Spectral Clustering with Provable Approximation Guarantee
by: Laenen, Steinar, et al.
Published: (2024)
by: Laenen, Steinar, et al.
Published: (2024)
Online Multi-Class Selection with Group Fairness Guarantee
by: Zargari, Faraz, et al.
Published: (2025)
by: Zargari, Faraz, et al.
Published: (2025)
Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions
by: Chen, Yixin, et al.
Published: (2026)
by: Chen, Yixin, et al.
Published: (2026)
Efficient Adaptive Data Analysis over Dense Distributions
by: Huh, Joon Suk
Published: (2026)
by: Huh, Joon Suk
Published: (2026)
Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret
by: Harada, Tsubasa, et al.
Published: (2025)
by: Harada, Tsubasa, et al.
Published: (2025)
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem
by: Blum, Avrim, et al.
Published: (2024)
by: Blum, Avrim, et al.
Published: (2024)
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
by: Li, Shuyao, et al.
Published: (2024)
by: Li, Shuyao, et al.
Published: (2024)
Stochastic Bandits with ReLU Neural Networks
by: Xu, Kan, et al.
Published: (2024)
by: Xu, Kan, et al.
Published: (2024)
Training Overparametrized Neural Networks in Sublinear Time
by: Deng, Yichuan, et al.
Published: (2022)
by: Deng, Yichuan, et al.
Published: (2022)
A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition
by: Fahrbach, Matthew, et al.
Published: (2025)
by: Fahrbach, Matthew, et al.
Published: (2025)
Learning the Positions in CountSketch
by: Li, Yi, et al.
Published: (2023)
by: Li, Yi, et al.
Published: (2023)
Distribution Learning Meets Graph Structure Sampling
by: Bhattacharyya, Arnab, et al.
Published: (2024)
by: Bhattacharyya, Arnab, et al.
Published: (2024)
Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
Active Learning for Decision Trees with Provable Guarantees
by: Moakhar, Arshia Soltani, et al.
Published: (2026)
by: Moakhar, Arshia Soltani, et al.
Published: (2026)
New Statistical and Computational Results for Learning Junta Distributions
by: Beretta, Lorenzo
Published: (2025)
by: Beretta, Lorenzo
Published: (2025)
Towards Efficient Contrastive PAC Learning
by: Shen, Jie
Published: (2025)
by: Shen, Jie
Published: (2025)
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
by: Lechowicz, Adam, et al.
Published: (2023)
by: Lechowicz, Adam, et al.
Published: (2023)
Similar Items
-
Efficient Discrepancy Testing for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2024) -
Iterative Chow Filtering for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2026) -
Testable Learning with Distribution Shift
by: Klivans, Adam R., et al.
Published: (2023) -
A Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube
by: Chandrasekaran, Gautam, et al.
Published: (2025) -
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds
by: Klivans, Adam R., et al.
Published: (2024)