Copertina

Selective classification using a robust meta-learning approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Jain, Nishant, Shanmugam, Karthikeyan, Shenoy, Pradeep
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Improving Generalization via Meta-Learning on Hard Samples
di: Jain, Nishant, et al.
Pubblicazione: (2024)

Glauber Generative Model: Discrete Diffusion Models via Binary Classification
di: Varma, Harshit, et al.
Pubblicazione: (2024)

Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
di: Nanavati, Praharsh, et al.
Pubblicazione: (2025)

Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
di: Dasgupta, Arpan, et al.
Pubblicazione: (2024)

Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
di: Anil, Gautham Govind, et al.
Pubblicazione: (2025)

General Identifiability and Achievability for Causal Representation Learning
di: Varıcı, Burak, et al.
Pubblicazione: (2023)

Bandits with Mean Bounds
di: Sharma, Nihal, et al.
Pubblicazione: (2020)

Regret minimization in Linear Bandits with offline data via extended D-optimal exploration
di: Vijayan, Sushant, et al.
Pubblicazione: (2025)

Linear Causal Representation Learning from Unknown Multi-node Interventions
di: Varıcı, Burak, et al.
Pubblicazione: (2024)

Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
di: Mukherjee, Arpan, et al.
Pubblicazione: (2024)

Masked Generative Nested Transformers with Decode Time Scaling
di: Goyal, Sahil, et al.
Pubblicazione: (2025)

Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
di: Parulekar, Advait, et al.
Pubblicazione: (2025)

Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
di: Sharma, Nihal, et al.
Pubblicazione: (2021)

Score-based Causal Representation Learning: Linear and General Transformations
di: Varıcı, Burak, et al.
Pubblicazione: (2024)

Path-specific effects for pulse-oximetry guided decisions in critical care
di: Zhang, Kevin, et al.
Pubblicazione: (2025)

Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems
di: Pathak, Anshul, et al.
Pubblicazione: (2026)

Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
di: Hoffman, Samuel C., et al.
Pubblicazione: (2022)

Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
di: Tatlı, Meltem, et al.
Pubblicazione: (2025)

Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
di: Tatlı, Meltem, et al.
Pubblicazione: (2025)

Distributionally robust minimization in meta-learning for system identification
di: Rufolo, Matteo, et al.
Pubblicazione: (2025)

Provable tradeoffs in adversarially robust classification
di: Dobriban, Edgar, et al.
Pubblicazione: (2020)

Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
di: Havaldar, Shreyas, et al.
Pubblicazione: (2023)

Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
di: Havaldar, Shreyas, et al.
Pubblicazione: (2023)

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions
di: Jain, Nishant, et al.
Pubblicazione: (2025)

Online Bidding under RoS Constraints without Knowing the Value
di: Vijayan, Sushant, et al.
Pubblicazione: (2025)

Leveraging advances in machine learning for the robust classification and interpretation of networks
di: Appaw, Raima Carol, et al.
Pubblicazione: (2024)

Machine learning approaches to seismic event classification in the Ostrava region
di: Pecha, Marek, et al.
Pubblicazione: (2025)

Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
di: Ghanghas, Nipun, et al.
Pubblicazione: (2026)

ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
di: Kulkarni, Pranamya, et al.
Pubblicazione: (2025)

Using Early Readouts to Mediate Featural Bias in Distillation
di: Tiwari, Rishabh, et al.
Pubblicazione: (2023)

Adaptive few-shot learning for robust part quality classification in two-photon lithography
di: Jia, Sixian, et al.
Pubblicazione: (2026)

Text classification using machine learning methods
di: Oancea, Bogdan
Pubblicazione: (2025)

Learning to learn ecosystems from limited data -- a meta-learning approach
di: Zhai, Zheng-Meng, et al.
Pubblicazione: (2024)

Machine learning approach to brain tumor detection and classification
di: Oh, Alice, et al.
Pubblicazione: (2024)

Calibeating for general proper losses: A Bregman divergence approach
di: Fichtl, Maximilian, et al.
Pubblicazione: (2026)

A comparative study on machine learning approaches for rock mass classification using drilling data
di: Hansen, Tom F., et al.
Pubblicazione: (2024)

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
di: Jain, Mayank Kumar, et al.
Pubblicazione: (2025)

Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
di: Anil, Gautham Govind, et al.
Pubblicazione: (2025)

Label-shift robust federated feature screening for high-dimensional classification
di: Qin, Qi, et al.
Pubblicazione: (2025)

DynGMA: a robust approach for learning stochastic differential equations from data
di: Zhu, Aiqing, et al.
Pubblicazione: (2024)