Saved in:
| Main Authors: | Jain, Nishant, Shanmugam, Karthikeyan, Shenoy, Pradeep |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2212.05987 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improving Generalization via Meta-Learning on Hard Samples
by: Jain, Nishant, et al.
Published: (2024)
by: Jain, Nishant, et al.
Published: (2024)
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
by: Varma, Harshit, et al.
Published: (2024)
by: Varma, Harshit, et al.
Published: (2024)
Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
by: Nanavati, Praharsh, et al.
Published: (2025)
by: Nanavati, Praharsh, et al.
Published: (2025)
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
by: Dasgupta, Arpan, et al.
Published: (2024)
by: Dasgupta, Arpan, et al.
Published: (2024)
Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
by: Anil, Gautham Govind, et al.
Published: (2025)
by: Anil, Gautham Govind, et al.
Published: (2025)
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by: Mukherjee, Arpan, et al.
Published: (2024)
by: Mukherjee, Arpan, et al.
Published: (2024)
General Identifiability and Achievability for Causal Representation Learning
by: Varıcı, Burak, et al.
Published: (2023)
by: Varıcı, Burak, et al.
Published: (2023)
Bandits with Mean Bounds
by: Sharma, Nihal, et al.
Published: (2020)
by: Sharma, Nihal, et al.
Published: (2020)
Regret minimization in Linear Bandits with offline data via extended D-optimal exploration
by: Vijayan, Sushant, et al.
Published: (2025)
by: Vijayan, Sushant, et al.
Published: (2025)
Linear Causal Representation Learning from Unknown Multi-node Interventions
by: Varıcı, Burak, et al.
Published: (2024)
by: Varıcı, Burak, et al.
Published: (2024)
Masked Generative Nested Transformers with Decode Time Scaling
by: Goyal, Sahil, et al.
Published: (2025)
by: Goyal, Sahil, et al.
Published: (2025)
Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
by: Parulekar, Advait, et al.
Published: (2025)
by: Parulekar, Advait, et al.
Published: (2025)
Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
by: Sharma, Nihal, et al.
Published: (2021)
by: Sharma, Nihal, et al.
Published: (2021)
Score-based Causal Representation Learning: Linear and General Transformations
by: Varıcı, Burak, et al.
Published: (2024)
by: Varıcı, Burak, et al.
Published: (2024)
Path-specific effects for pulse-oximetry guided decisions in critical care
by: Zhang, Kevin, et al.
Published: (2025)
by: Zhang, Kevin, et al.
Published: (2025)
Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems
by: Pathak, Anshul, et al.
Published: (2026)
by: Pathak, Anshul, et al.
Published: (2026)
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
by: Hoffman, Samuel C., et al.
Published: (2022)
by: Hoffman, Samuel C., et al.
Published: (2022)
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
by: Tatlı, Meltem, et al.
Published: (2025)
by: Tatlı, Meltem, et al.
Published: (2025)
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
by: Tatlı, Meltem, et al.
Published: (2025)
by: Tatlı, Meltem, et al.
Published: (2025)
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
by: Havaldar, Shreyas, et al.
Published: (2023)
by: Havaldar, Shreyas, et al.
Published: (2023)
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
by: Havaldar, Shreyas, et al.
Published: (2023)
by: Havaldar, Shreyas, et al.
Published: (2023)
Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
by: Ghanghas, Nipun, et al.
Published: (2026)
by: Ghanghas, Nipun, et al.
Published: (2026)
A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions
by: Jain, Nishant, et al.
Published: (2025)
by: Jain, Nishant, et al.
Published: (2025)
Distributionally robust minimization in meta-learning for system identification
by: Rufolo, Matteo, et al.
Published: (2025)
by: Rufolo, Matteo, et al.
Published: (2025)
Online Bidding under RoS Constraints without Knowing the Value
by: Vijayan, Sushant, et al.
Published: (2025)
by: Vijayan, Sushant, et al.
Published: (2025)
ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
by: Kulkarni, Pranamya, et al.
Published: (2025)
by: Kulkarni, Pranamya, et al.
Published: (2025)
Leveraging advances in machine learning for the robust classification and interpretation of networks
by: Appaw, Raima Carol, et al.
Published: (2024)
by: Appaw, Raima Carol, et al.
Published: (2024)
Provable tradeoffs in adversarially robust classification
by: Dobriban, Edgar, et al.
Published: (2020)
by: Dobriban, Edgar, et al.
Published: (2020)
Using Early Readouts to Mediate Featural Bias in Distillation
by: Tiwari, Rishabh, et al.
Published: (2023)
by: Tiwari, Rishabh, et al.
Published: (2023)
Machine learning approaches to seismic event classification in the Ostrava region
by: Pecha, Marek, et al.
Published: (2025)
by: Pecha, Marek, et al.
Published: (2025)
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
by: Anil, Gautham Govind, et al.
Published: (2025)
by: Anil, Gautham Govind, et al.
Published: (2025)
Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
by: Jain, Mayank Kumar, et al.
Published: (2025)
by: Jain, Mayank Kumar, et al.
Published: (2025)
Adaptive few-shot learning for robust part quality classification in two-photon lithography
by: Jia, Sixian, et al.
Published: (2026)
by: Jia, Sixian, et al.
Published: (2026)
Learning to learn ecosystems from limited data -- a meta-learning approach
by: Zhai, Zheng-Meng, et al.
Published: (2024)
by: Zhai, Zheng-Meng, et al.
Published: (2024)
Machine learning approach to brain tumor detection and classification
by: Oh, Alice, et al.
Published: (2024)
by: Oh, Alice, et al.
Published: (2024)
Calibeating for general proper losses: A Bregman divergence approach
by: Fichtl, Maximilian, et al.
Published: (2026)
by: Fichtl, Maximilian, et al.
Published: (2026)
Training-efficient density quantum machine learning
by: Coyle, Brian, et al.
Published: (2024)
by: Coyle, Brian, et al.
Published: (2024)
Text classification using machine learning methods
by: Oancea, Bogdan
Published: (2025)
by: Oancea, Bogdan
Published: (2025)
A comparative study on machine learning approaches for rock mass classification using drilling data
by: Hansen, Tom F., et al.
Published: (2024)
by: Hansen, Tom F., et al.
Published: (2024)
DynGMA: a robust approach for learning stochastic differential equations from data
by: Zhu, Aiqing, et al.
Published: (2024)
by: Zhu, Aiqing, et al.
Published: (2024)
Similar Items
-
Improving Generalization via Meta-Learning on Hard Samples
by: Jain, Nishant, et al.
Published: (2024) -
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
by: Varma, Harshit, et al.
Published: (2024) -
Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
by: Nanavati, Praharsh, et al.
Published: (2025) -
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
by: Dasgupta, Arpan, et al.
Published: (2024) -
Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
by: Anil, Gautham Govind, et al.
Published: (2025)