Summary of Learning From Aggregate Responses: Instance Level Versus Bag Level Loss Functions, by Adel Javanmard et al.
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses privacy concerns in machine learning by studying two loss functions for learning from aggregate responses: bag-level loss and instance-level loss. In an aggregate learning framework, data is grouped into bags with aggregated responses, providing a summary of individual responses. The authors show that the instance-level loss can be seen as a regularized form of the bag-level loss, enabling comparisons between the two approaches in terms of bias and variance. They also introduce an interpolating estimator that combines both methods and provide a precise characterization of its risk for linear regression tasks. Additionally, they propose a mechanism for differentially private learning from aggregate responses and derive the optimal bag size for a prediction risk-privacy trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that people’s personal information stays private when we’re training artificial intelligence models. One way to do this is by grouping together similar data points, so only an average or summary of what people said is shared. The researchers looked at two different ways to train these models: one that focuses on the overall group response and another that tries to predict each individual’s answer. They showed that both methods have their own strengths and weaknesses and introduced a new way to combine the best of both worlds. This paper also talks about how to make sure this process is fair and private, while still getting good results from our models. |
Keywords
* Artificial intelligence * Linear regression * Machine learning
