Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Diversified Recommendations for Agents with Adaptive Preferences
Authors: William Brown, Arpit Agarwal
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formalize this problem as an adversarial bandit task. At each step, the Recommender presents a menu of k (out of n) items to the Agent, who selects one item in the menu according to their unknown preference model... We define a class of preference models which are locally learnable... For this class, we give an algorithm for the Recommender which obtains O(T 3/4) regret... We also give a set of negative results justifying our assumptions, in the form of a runtime lower bound for non-local learning and linear regret lower bounds for alternate benchmarks. |
| Researcher Affiliation | Academia | Arpit Agarwal Department of Computer Science Columbia University New York, NY 10027 EMAIL William Brown Department of Computer Science Columbia University New York, NY 10027 EMAIL |
| Pseudocode | Yes | Algorithm 1 (Robust Contracting FKM). Input: sequence of contracting convex decision sets K1, . . . KT containing 0, perturbation vectors ξ1, . . . , ξT where ξt ϵ, parameters δ, η. Set x1 = 0 for t = 1 to T do... Algorithm 2 A no-regret recommendation algorithm for adaptive agents. Input: Item set [n], menu size k, Agent with λ-dispersed memory model M for λ k2 n , where M belongs to an S-locally learnable class M, diversity constraint Hc, horizon T, G-Lipschitz linear losses ρi, . . . , ρT . Let tpad = Θ(1/ϵ3)... |
| Open Source Code | No | The paper does not contain any statements about releasing source code for the described methodology or links to code repositories. |
| Open Datasets | No | This is a theoretical paper presenting algorithms and regret bounds, not empirical results based on datasets. Therefore, no information on public datasets or their access is provided. |
| Dataset Splits | No | This is a theoretical paper presenting algorithms and regret bounds, not empirical results based on datasets. Therefore, no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | This is a theoretical paper that does not report on empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not report on empirical experiments requiring specific software dependencies with version numbers. Therefore, no such details are provided. |
| Experiment Setup | No | This is a theoretical paper that does not report on empirical experiments. Therefore, it does not include specific experimental setup details such as hyperparameters or training configurations. |