Cover Image

A New Framework to Analyse the Distributional Robustness of Deep Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khaitan, Divij, Banerjee, Subhashis
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LIFT: Last-Mile Fine-Tuning for Table Explicitation
by: Khaitan, Divij, et al.
Published: (2026)

Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)

Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks
by: Li, Yi, et al.
Published: (2025)

Topology of Out-of-Distribution Examples in Deep Neural Networks
by: Datta, Esha, et al.
Published: (2025)

Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
by: Gupta, Divij, et al.
Published: (2024)

Certifying Global Robustness for Deep Neural Networks
by: Li, You, et al.
Published: (2024)

Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
by: Lyu, Shen-Huan, et al.
Published: (2018)

Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
by: Hajij, Mustafa, et al.
Published: (2025)

Bayesian Semi-supervised Multi-category Classification under Nonparanormality
by: Zhu, Rui, et al.
Published: (2020)

Wasserstein Distributionally Robust Shallow Convex Neural Networks
by: Pallage, Julien, et al.
Published: (2024)

Stock Market Price Prediction using Neural Prophet with Deep Neural Network
by: Chhibber, Navin, et al.
Published: (2026)

On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networks
by: Park, Seongjin, et al.
Published: (2022)

Time-Efficient Evaluation and Enhancement of Adversarial Robustness in Deep Neural Networks
by: Lin, Runqi
Published: (2025)

Network Dynamics-Based Framework for Understanding Deep Neural Networks
by: Lin, Yuchen, et al.
Published: (2025)

Distributionally Robust Statistical Verification with Imprecise Neural Networks
by: Dutta, Souradeep, et al.
Published: (2023)

Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems
by: Anandh, Thivin, et al.
Published: (2024)

Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks
by: Muller, Nadine, et al.
Published: (2026)

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
by: Liu, Hao, et al.
Published: (2024)

IGNIS: A Robust Neural Network Framework for Constrained Parameter Estimation in Archimedean Copulas
by: Aich, Agnideep
Published: (2025)

Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
by: Michańków, Jakub
Published: (2025)

DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
by: Kim, Somin, et al.
Published: (2025)

DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks
by: Ruggieri, Giovanni
Published: (2025)

Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
by: Dawn, Rudrajit, et al.
Published: (2025)

SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks
by: Valipour, Mojtaba, et al.
Published: (2023)

JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates
by: Yi, Kai, et al.
Published: (2026)

Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage
by: Gupta, Aishwarya, et al.
Published: (2024)

Pruning Deep Neural Networks via a Combination of the Marchenko-Pastur Distribution and Regularization
by: Berlyand, Leonid, et al.
Published: (2025)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
by: Moya, Christian, et al.
Published: (2024)

Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
by: Yang, You, et al.
Published: (2026)

A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting
by: Salazar, Christopher, et al.
Published: (2023)

Robust and Efficient Deep Hedging via Linearized Objective Neural Network
by: Zhao, Lei, et al.
Published: (2025)

A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
by: Wang, Housen, et al.
Published: (2024)

DeepCDCL: An CDCL-based Neural Network Verification Framework
by: Liu, Zongxin, et al.
Published: (2024)

On the Neural Feature Ansatz for Deep Neural Networks
by: Tansley, Edward, et al.
Published: (2025)

VAEs and GANs: Implicitly Approximating Complex Distributions with Simple Base Distributions and Deep Neural Networks -- Principles, Necessity, and Limitations
by: Wei, Yuan-Hao
Published: (2025)

CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
by: McDanel, Bradley, et al.
Published: (2026)

A DPLL(T) Framework for Verifying Deep Neural Networks
by: Duong, Hai, et al.
Published: (2023)

Enhancing Certifiable Semantic Robustness via Robust Pruning of Deep Neural Networks
by: Hu, Hanjiang, et al.
Published: (2025)

Mini-Batch Robustness Verification of Deep Neural Networks
by: Tzour-Shaday, Saar, et al.
Published: (2025)

MoE-Spec: Expert Budgeting for Efficient Speculative Decoding
by: McDanel, Bradley, et al.
Published: (2026)