Sparsity-Agnostic Linear Bandits with Adaptive Adversaries
Authors: Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on synthetic data, Ada Lin UCB performs better than OFUL. In this section we describe some experiments we performed on synthetic data to verify whether Ada Lin UCB could outperform OFUL in a model selection task. |
| Researcher Affiliation | Academia | Tianyuan Jin Department of Electrical and Computer Engineering National University of Singapore Singapore tianyuan@nus.edu.sg Kyoungseok Jang Dipartimento di Informatica Università degli Studi di Milano Milano, Italy ksajks@gmail.com Nicolò Cesa-Bianchi Università degli Studi di Milano Politecnico di Milano Milano, Italy nicolo.cesa-bianchi@unimi.it |
| Pseudocode | Yes | Algorithm 1 Sparse Lin UCB |
| Open Source Code | Yes | The code used in the experiments can be found in the following repository: https://github.com/ jajajang/sparsity_agnostic_model_selection. |
| Open Datasets | No | The data for our model selection experiments are generated using targets θ with different sparsity levels... We run our experiments with a fixed set of random actions, At = A for all t [T], where |A| = 30 and A is a set of vectors drawn i.i.d. from the unit sphere in R16. The target vector θ is a S-sparse (S = 1, 2, 4, 8, 16) vector whose non-zero coordinates are drawn from the unit sphere in RS. The noise εt is drawn i.i.d. from the uniform distribution over [ 1, 1]. |
| Dataset Splits | No | The data for our model selection experiments are generated using targets θ with different sparsity levels... Each curve is an average over 20 repetitions with T = 104 where, in each repetition, we draw fresh instances of A and θ . |
| Hardware Specification | Yes | Hardware: Lenovo Thinkpad P16s Gen 2 Laptop Type 21HL CPU: 13th Gen Intel(R) Core(TM) i7-1360P 2.20 GHz RAM: 32GB Computation time: total 1338.38 seconds. |
| Software Dependencies | No | No specific software dependencies with version numbers are listed in the provided text. |
| Experiment Setup | Yes | Number of iterations: T = 104. Number of models: n = 6. Radius of confidence sets: α0 = 0, and αi = 2i log t for i = 1, , 5. Prior distribution {qs}s [6] For _Unif,{qs}s [6] = 1/6. For _Theory, {qs}s [6] = C/64 where C = 63/64 is a normalizing constant. The learning rate of Exp3 was set to ηt = 2 q nt , see [5]. |