Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective | Paper Citation Record

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective write newline

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective G., Guo, Z

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Teboulle, M

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective C., Lecu \'e , G., and Tsybakov, A

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This paper cites A tutorial on learning from preferences and choices with Gaussian Processes.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective A tutorial on learning from preferences and choices with Gaussian Processes

Reference 7

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Learning choice functions with gaussian processes

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Preference-based online learning with dueling bandits: A survey

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective R., Kumar, R., and Tomkins, A

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective J., Ritov, Y., and Tsybakov, A

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Balzano, L

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Distributed optimization and statistical learning via the alternating direction method of multipliers

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective B., Soares, C., and Da Costa, J

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Learning to rank using gradient descent

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Davenport, M

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Tao, T

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Tao, T

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Learning an agent's utility function by observing behavior

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Keerthi, S

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Singer, Y

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective The Llama 3 Herd of Models

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Information theory and statistics

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Ok, E

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Understanding dataset difficulty with-usable information

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Improved optimistic algorithms for logistic bandits

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Hall, M

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective u rnkranz, J. and H \

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective u rnkranz, J. and H \

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Scaling laws for reward model overoptimization

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Preference elicitation via theory refinement

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Minimax-optimal inference from partial rankings

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Statistical learning with sparsity

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Accelerated Preference Optimization for Large Language Model Alignment

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Neural collaborative filtering

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective M., and Zhang, T

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Optimizing search engines using clickthrough data

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective A software package for sequential quadratic programming

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Prediction of ordinal classes using regression trees

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Scalable agent alignment via reward modeling: a research direction

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective J., and Liu, J

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Contextual multi-armed bandits

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Generative Reward Models

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective The measurement of urban travel demand

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Modeling the choice of residential location

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Remarks on the method of paired comparisons: I

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Choice functions over a finite set: a summary

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Iterative ranking from pair-wise comparisons

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Rank centrality: Ranking from pairwise comparisons

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Observation 9e876192-4d49-4bc1-9639-4b296391716a · outbound

This paper cites Dueling posterior sampling for preference-based reinforcement learning.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Dueling posterior sampling for preference-based reinforcement learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.848623Z

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Observation 75a62409-a27f-484a-8b74-9407efe65ee2 · outbound

This paper cites Training language models to follow instructions with human feedback.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Training language models to follow instructions with human feedback

Reference 61

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Observation 24eb2a2f-3a01-4507-98d8-ef79d8fa9179 · outbound

This paper cites Dueling RL: Reinforcement Learning with Trajectory Preferences.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Dueling RL: Reinforcement Learning with Trajectory Preferences

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.266988Z

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Observation a0f5b234-59a2-4516-a372-33fe39526132 · outbound

This paper cites Preference completion: Large-scale collaborative ranking from pairwise comparisons.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Preference completion: Large-scale collaborative ranking from pairwise comparisons

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.821355Z

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Observation d17075d8-e4e8-42fe-b15d-a69c8af81ada · outbound

This paper cites Block Coordinate Descent on Smooth Manifolds: Convergence Theory and Twenty-One Examples.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Block Coordinate Descent on Smooth Manifolds: Convergence Theory and Twenty-One Examples

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.278266Z

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Observation 81afef91-0d20-4f2a-958e-e2127da5f668 · outbound

This paper cites and H \"u llermeier, E.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and H \"u llermeier, E

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.802554Z

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Observation 1b3f4971-734e-4a61-b878-81ba87982878 · outbound

This paper cites D., Ermon, S., and Finn, C.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective D., Ermon, S., and Finn, C

Reference 66

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This paper cites and Agarwal, S.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Agarwal, S

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.773487Z

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Observation e3e9749d-4149-45c9-b305-0a57bbe58db8 · outbound

This paper cites an unresolved cited work.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.757429Z

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Observation 1da57973-b68d-4e7f-930a-91147dbec051 · outbound

This paper cites J., and Yu, B.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective J., and Yu, B

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.739744Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ba78df3c-4d22-406f-bd93-766391c8da3f · outbound

This paper cites and Gastpar, M.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Gastpar, M

Reference 70

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T00:12:14.733152Z

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Observation 93bc810a-af50-4149-8e4a-7554a80da290 · outbound

This paper cites Bpr: Bayesian personalized ranking from implicit feedback.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Bpr: Bayesian personalized ranking from implicit feedback

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.720770Z

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Observation 2d1740ca-3730-4eb9-8054-ce4508f85d10 · outbound

This paper cites and Varian, H.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Varian, H

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.697836Z

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Observation 23b3517e-2202-4509-8c6b-0ba108cf2830 · outbound

This paper cites High-Dimensional Statistics.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective High-Dimensional Statistics

Reference 73

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This paper cites and Tsybakov, A.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Tsybakov, A

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.680181Z

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Observation 94b6c035-8239-4111-b84b-e7919f46eb99 · outbound

This paper cites Predicting choice with set-dependent aggregation.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Predicting choice with set-dependent aggregation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.661829Z

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Observation c0acdeed-7609-4ba4-a9fe-4d0514300415 · outbound

This paper cites Optimal algorithms for stochastic contextual preference bandits.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Optimal algorithms for stochastic contextual preference bandits

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.643357Z

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Observation 6944ddc6-cdab-47dd-988a-b899e25e9613 · outbound

This paper cites Dueling rl: Reinforcement learning with trajectory preferences.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Dueling rl: Reinforcement learning with trajectory preferences

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.617548Z

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Observation 86c9cdc2-e058-4288-a78b-310c378e490c · outbound

This paper cites an unresolved cited work.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.587526Z

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Observation 18ee70d2-78f0-46c8-8be5-72c06aae1544 · outbound

This paper cites Discovering context effects from raw choice data.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Discovering context effects from raw choice data

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.557081Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f143f8b7-0303-4bde-917a-1baecb33cb80 · outbound

This paper cites Learning rich rankings.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Learning rich rankings

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.530794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ca270ff-9d8c-4f4d-b78b-a4734e342c25 · outbound

This paper cites B., Balakrishnan, S., Bradley, J., Parekh, A., Ramch, K., Wainwright, M.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective B., Balakrishnan, S., Bradley, J., Parekh, A., Ramch, K., Wainwright, M

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.506675Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e35a01d-ef7c-450e-9f70-5a6d1def3106 · outbound

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.479702Z

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Observation 8ba214c6-f0b5-43c0-bcce-6bff7d246e43 · outbound

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Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 83

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This paper cites Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.381133Z

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Observation 5fea6f87-6eb9-4d83-ab92-8677632905b1 · outbound

This paper cites Connectionist learning of expert preferences by comparison training.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Connectionist learning of expert preferences by comparison training

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.440420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e374af75-06f7-4358-9aad-7ad87f1b4e8b · outbound

This paper cites an unresolved cited work.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.417256Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1b11f201-7e3f-47c5-a23d-d0f57004ed5a · outbound

This paper cites Regression shrinkage and selection via the lasso.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Regression shrinkage and selection via the lasso

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.396151Z

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Observation a79c957a-2c88-41c3-b2e0-3ae9252c526a · outbound

This paper cites and Benson, A.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Benson, A

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.363685Z

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Observation 2a3d3caf-bb06-48c2-bd8b-646731243197 · outbound

This paper cites and Benson, A.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Benson, A

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.340241Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bac929cc-f323-48b0-9379-36145749dabb · outbound

This paper cites an unresolved cited work.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.304635Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d9312243-62c6-44aa-9eaa-5695359183b7 · outbound

This paper cites On accelerated proximal gradient methods for convex-concave optimization.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective On accelerated proximal gradient methods for convex-concave optimization

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.272410Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 54abf7e1-cf1d-4f3d-b4ad-e0f9628fe321 · outbound

This paper cites Choice modelling in the age of machine learning-discussion paper.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Choice modelling in the age of machine learning-discussion paper

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.244803Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 054bdf4a-d37f-411d-9e9a-f48745274d8c · outbound

This paper cites Estimation in high dimensions: a geometric perspective.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Estimation in high dimensions: a geometric perspective

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.221528Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a128dfe2-0e8f-4d57-9851-87100393961e · outbound

This paper cites Minimax risks for sparse regressions: Ultra-high dimensional phenomenons.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Minimax risks for sparse regressions: Ultra-high dimensional phenomenons

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.203510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b678f612-e792-4ad4-a44b-e04871402f11 · outbound

This paper cites E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt , S.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt , S

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.437883Z

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Unavailable: canonical work link unavailable.

Observation 9103a5ae-9134-413f-8236-791eb0d086d1 · outbound

This paper cites and Morgenstern, O.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Morgenstern, O

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.172246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 790382d5-ae3c-42d3-9dc9-adbbb58e156c · outbound

This paper cites an unresolved cited work.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-10T00:12:15.156258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 851b166a-73d8-40d1-8f1e-d835b52704ec · outbound

This paper cites Aligning Large Language Models with Human: A Survey.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Aligning Large Language Models with Human: A Survey

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-10T00:11:59.452649Z

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Observation 129f175b-5ff9-47bd-9cb1-664256ffcede · outbound

This paper cites Efficient ranking from pairwise comparisons.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Efficient ranking from pairwise comparisons

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.137289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3cb0c0e2-a29c-4487-8c93-05c3f951cd0f · outbound

This paper cites and Ma, Y.

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective and Ma, Y

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:12:15.115078Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Outbound references

Pith citing papers