Initializing Services in Interactive ML Systems for Diverse Users

Summary

The paper introduces a novel method for initializing machine learning services tailored to diverse user preferences. The work addresses the challenges of non-convex optimization and lack of pre-existing user preference data before running a service; the authors propose a randomized algorithm that adaptively selects a minimal set of users for data collection. The approach guarantees a total loss close to the global optimum under mild assumptions and extends the k-means++ algorithm to a broader problem class. The results also are supported by experiments on real and semi-synthetic datasets.

Strengths

The authors tackle the challenge of service initialization in the context of bandit feedback and non-convex optimization, which has not been extensively studied before. The proposed algorithm is an interesting extension of the k-means++ algorithm, designed to handle general loss families. The analysis of the proposed approach is solid. The theoretical result is supported by experimental study. The paper is well-written and clearly structured.

Weaknesses

The computational complexity is not well studied, especially in large-scale settings.

Questions

Could you provide any data on the time performance of the algorithms in the experimental study?

Rating

7

Confidence

2

Soundness

3

Presentation

3

Contribution

3

Limitations

N/A

Summary

This paper introduces a novel method for initializing services in interactive machine learning (ML) systems tailored to diverse user preferences. The focus is on scenarios where multiple models or services are deployed, allowing users to choose the one that minimizes their personal losses. The authors highlight two primary challenges in determining optimal initial conditions for these services: the absence of user preference data prior to deployment (bandit feedback) and the presence of non-convex loss landscapes that can lead to suboptimal local solutions. To overcome these challenges, the authors propose a randomized algorithm for service initialization. They provide theoretical guarantees, demonstrating an approximation ratio for the algorithm, and present empirical results that showcase the approach's effectiveness on both real and semi-synthetic datasets.

Strengths

- The proposed adaptive randomized algorithm for service initialization in interactive ML systems is a novel contribution. It extends the well-known K-means++ algorithm to a more complex setting involving diverse user preferences. - The paper provides strong theoretical guarantees, including tight bounds on total loss and a generalization of the K-means++ guarantee. This adds significant value by ensuring the robustness of the proposed method. - The empirical results on real and semi-synthetic datasets validate the algorithm's effectiveness in reducing total loss and improving service specialization. The inclusion of fairness considerations further strengthens the practical relevance of the work.

Weaknesses

- The algorithm relies on specific assumptions about the loss functions (e.g., uniqueness of minimizers, approximate triangle inequalities). These assumptions, although reasonable in many cases, may not hold in all practical applications, potentially limiting the algorithm's applicability. - The empirical validation, although convincing, is limited to two datasets. Additional experiments on a broader range of datasets and application domains would provide stronger evidence of the method's effectiveness and generalizability.

Questions

- What is the performance of optimization algorithms after using your proposed initialization method? Have you empirically compared that performance with other existing initialization approaches? - Could you elaborate on the potential impact of violating the assumptions made about the loss functions? How robust is your algorithm to deviations from these assumptions in practice?

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

Yes

Summary

1) This paper introduces a new algorithm to efficiently initialize a system providing K services to N users (K << N) where user preferences are unknown beforehand and the system iteratively learns about user preferences as the services are recommended, example : netflix movie recommendations. 2) The proposed method has been inspired by the k-means++ algorithm and applies it in a more general setting. Authors also provide theoretical proof that the system initialized in this way will achieve a worst-case loss not exceeding a log multiplier on the optimal loss for this system. 3) AcQUIre and it's modification to preserve fairness across subpopulations (Fair AcQUIre) are the main algorithms presented in this paper with experiments ablating the effect of different user selection strategies used in AcQUIre 4) Experiments on census dataset and movielens dataset are provided.

Strengths

1) Paper is well-written and authors have done a great job introducing the problem with sufficient notations and related work. 2) The problem introduced is very relevant and encourages future research in this direction.

Weaknesses

1) In the "Movie Recommendation" experiment users are divided into N=1000 subpopulations based on the similarity of their movie ratings and all experiments are then conducted to achieve minimal excess error w.r.t to these subpopulation groups, however in a typical setting where the proposed method might be applied there's no such prior data to group users conveniently so an ablation on user clustering methods prior to applying AcQUIre would further boost it's effectiveness. 2) The size of the datasets are small enough to make the computations practical but the method is actually expensive depending on the choice of K the system needs to get desired loss on the population of N users, so some discussion around this aspect would be useful where authors go deeper in practical deployments of this method is a system like Netflix recommending movies to a billion users.

Questions

1) It is unclear how line 7 of the proposed method : "New service: Query user l’s preference" would be implemented in a real world setting. In a large scale distributed recommendation system such as netflix where user preferences are being collected on a subset of recommended services in parallel as soon as the system is deployed it's unclear how to get a specific user's preferences so some clarification here would be useful.

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

2

Limitations

There's no negative societal consequences of this work.