Saved in:
| Main Authors: | Sarkar, Dhruv, Pandey, Nishant, Chowdhury, Sayak Ray |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.21312 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improved Algorithms for Nash Welfare in Linear Bandits
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
DP-NCB: Privacy Preserving Fair Bandits
by: Sarkar, Dhruv, et al.
Published: (2025)
by: Sarkar, Dhruv, et al.
Published: (2025)
Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025)
by: Gore, Aakash, et al.
Published: (2025)
Is Sliding Window All You Need? An Open Framework for Long-Sequence Recommendation
by: Chakrabarty, Sayak, et al.
Published: (2026)
by: Chakrabarty, Sayak, et al.
Published: (2026)
Constrained Contextual Bandits with Adversarial Contexts
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
Replicable Bandits with UCB based Exploration
by: Deb, Rohan, et al.
Published: (2026)
by: Deb, Rohan, et al.
Published: (2026)
One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise
by: Bhat, Amith, et al.
Published: (2026)
by: Bhat, Amith, et al.
Published: (2026)
A Simple Reduction Scheme for Constrained Contextual Bandits with Adversarial Contexts via Regression
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
Truncated LinUCB for Stochastic Linear Bandits
by: Song, Yanglei, et al.
Published: (2022)
by: Song, Yanglei, et al.
Published: (2022)
Near-optimal Per-Action Regret Bounds for Sleeping Bandits
by: Nguyen, Quan, et al.
Published: (2024)
by: Nguyen, Quan, et al.
Published: (2024)
Why DPO is a Misspecified Estimator and How to Fix It
by: Gopalan, Aditya, et al.
Published: (2025)
by: Gopalan, Aditya, et al.
Published: (2025)
Provably Robust DPO: Aligning Language Models with Noisy Feedback
by: Chowdhury, Sayak Ray, et al.
Published: (2024)
by: Chowdhury, Sayak Ray, et al.
Published: (2024)
Extended UCB Policies for Multi-armed Bandit Problems
by: Liu, Keqin, et al.
Published: (2011)
by: Liu, Keqin, et al.
Published: (2011)
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
by: Bui, Ha Manh, et al.
Published: (2024)
by: Bui, Ha Manh, et al.
Published: (2024)
Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks
by: Ali, Sarwan, et al.
Published: (2024)
by: Ali, Sarwan, et al.
Published: (2024)
Accuracy is Not All You Need
by: Dutta, Abhinav, et al.
Published: (2024)
by: Dutta, Abhinav, et al.
Published: (2024)
Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
by: Paschalidis, Phevos, et al.
Published: (2024)
by: Paschalidis, Phevos, et al.
Published: (2024)
A UCB Bandit Algorithm for General ML-Based Estimators
by: Liu, Yajing, et al.
Published: (2026)
by: Liu, Yajing, et al.
Published: (2026)
Attention is All You Need Until You Need Retention
by: Yaslioglu, M. Murat
Published: (2025)
by: Yaslioglu, M. Murat
Published: (2025)
Revisiting End-To-End Sparse Autoencoder Training: A Short Finetune Is All You Need
by: Karvonen, Adam
Published: (2025)
by: Karvonen, Adam
Published: (2025)
Context is All You Need
by: Delanois, Jean Erik, et al.
Published: (2026)
by: Delanois, Jean Erik, et al.
Published: (2026)
Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
by: Tyukin, Georgy, et al.
Published: (2024)
by: Tyukin, Georgy, et al.
Published: (2024)
Active Preference Optimization for Sample Efficient RLHF
by: Das, Nirjhar, et al.
Published: (2024)
by: Das, Nirjhar, et al.
Published: (2024)
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift
by: Son, Seongho, et al.
Published: (2024)
by: Son, Seongho, et al.
Published: (2024)
Some Attention is All You Need for Retrieval
by: Michalak, Felix, et al.
Published: (2025)
by: Michalak, Felix, et al.
Published: (2025)
Half Search Space is All You Need
by: Rumiantsev, Pavel, et al.
Published: (2025)
by: Rumiantsev, Pavel, et al.
Published: (2025)
Top-$nσ$: Not All Logits Are You Need
by: Tang, Chenxia, et al.
Published: (2024)
by: Tang, Chenxia, et al.
Published: (2024)
Exploitation Is All You Need... for Exploration
by: Rentschler, Micah, et al.
Published: (2025)
by: Rentschler, Micah, et al.
Published: (2025)
Multistep Inverse Is Not All You Need
by: Levine, Alexander, et al.
Published: (2024)
by: Levine, Alexander, et al.
Published: (2024)
Cooperation Is All You Need
by: Adeel, Ahsan, et al.
Published: (2023)
by: Adeel, Ahsan, et al.
Published: (2023)
PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs
by: Hu, Xiaoyan, et al.
Published: (2024)
by: Hu, Xiaoyan, et al.
Published: (2024)
Belief Samples Are All You Need For Social Learning
by: JafariNodeh, Mahyar, et al.
Published: (2024)
by: JafariNodeh, Mahyar, et al.
Published: (2024)
Realizable Learning is All You Need
by: Hopkins, Max, et al.
Published: (2021)
by: Hopkins, Max, et al.
Published: (2021)
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
by: Ahmed, Maheed H., et al.
Published: (2026)
by: Ahmed, Maheed H., et al.
Published: (2026)
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
by: Zhou, Da-Wei, et al.
Published: (2023)
by: Zhou, Da-Wei, et al.
Published: (2023)
Support is All You Need for Certified VAE Training
by: Xu, Changming, et al.
Published: (2025)
by: Xu, Changming, et al.
Published: (2025)
Fusion or Confusion? Multimodal Complexity Is Not All You Need
by: Rheude, Tillmann, et al.
Published: (2025)
by: Rheude, Tillmann, et al.
Published: (2025)
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)
by: Chaudhari, Marmik, et al.
Published: (2026)
More Agents Is All You Need
by: Li, Junyou, et al.
Published: (2024)
by: Li, Junyou, et al.
Published: (2024)
Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
by: Rezaei, Parham, et al.
Published: (2024)
by: Rezaei, Parham, et al.
Published: (2024)
Similar Items
-
Improved Algorithms for Nash Welfare in Linear Bandits
by: Sarkar, Dhruv, et al.
Published: (2026) -
DP-NCB: Privacy Preserving Fair Bandits
by: Sarkar, Dhruv, et al.
Published: (2025) -
Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025) -
Is Sliding Window All You Need? An Open Framework for Long-Sequence Recommendation
by: Chakrabarty, Sayak, et al.
Published: (2026) -
Constrained Contextual Bandits with Adversarial Contexts
by: Sarkar, Dhruv, et al.
Published: (2026)