11 accepted papers
Given a training dataset, the goal of dataset distillation is to derive a synthetic dataset such that models trained on the latter perform as well as those trained on the training dataset. In this work, we develop and analyze an efficient dataset distillation algorithm for supervised learning, speci…
Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP) are useful learning frameworks, where the training data is partitioned into disjoint sets or bags, and only an aggregate label, i.e., bag-label for each bag is available to the learner. In the case of MIR, the bag-label is
Fine-Tuning of LLMs using RLHF / RLAIF has been shown as a critical step to improve the performance of LLMs in complex generation tasks. These methods typically use response-level human or model feedback for alignment. Recent works indicate that finer sentence or span-level labels provide more accur…
Multiple instance regression, introduced by Ray and Page [2001], is a generalisation of supervised regression in which the training data is available as a bag of feature-vectors (instances) and for each bag there is a bag-label which matches the label of one (unknown) primary instance from that bag.
In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label derived from the instance-labels in the bag. In learning from label proportions (LLP) the aggregate
In learning from aggregate labels, the training data consists of sets or “bags” of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually $\\{0,1\\}$-valued) labels of its constituent instances. In *learning from label proportions* (LLP), the aggregate label
Multiple instance regression (MIR) was introduced by Ray and Page (2001) as an analogue of multiple instance learning (MIL) in which we are given bags of feature-vectors (instances) and for each bag there is a bag-label which matches the label of one (unknown) primary instance from that bag. The goa…
Learning from label proportions (LLP) is a generalization of supervised learning in which the training data is available as sets or bags of feature-vectors (instances) along with the average instance-label of each bag. The goal is to train a good instance classifier. While most previous works on LLP…
We study the learnability of linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the feature-vector classifier is learnt from bags of feature-vectors and their corresponding observed label proportions which are satisfied by (i.e., consistent with) some…
In the framework of learning from label proportions (LLP) the goal is to learn a good instance-level label predictor from the observed label proportions of bags of instances. Most of the LLP algorithms either explicitly or implicitly assume the nature of bag distributions with respect to the actual…
We study the problem of properly learning linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the learning is on a collection of bags of feature-vectors with only the proportion of labels available for each bag. First, we provide an algorithm that, giv…