9 citations · 14 across the 11 of their papers we have counts for
8 papers · 1 filter
What Does Preference Learning Recover from Pairwise Comparison Data?
Rattana Pukdee, Maria-Florina Balcan, Pradeep Ravikumar
Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical dataset consists of triplets $(x…
Learning from Interval Targets
Rattana Pukdee, Ziqi Ke, Chirag Gupta
We study the problem of regression with interval targets, where only upper and lower bounds on target values are available in the form of intervals. This problem arises when the ex…
Bayesian Neural Networks with Domain Knowledge Priors
Dylan Sam, Rattana Pukdee, Daniel P. Jeong +2
Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty. However, specifying a prior for BNNs that captures relevant doma…
Reliable learning in challenging environments
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee +1
The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic g…
Learning with Explanation Constraints
Rattana Pukdee, Dylan Sam, J. Zico Kolter +2
As larger deep learning models are hard to interpret, there has been a recent focus on generating explanations of these black-box models. In contrast, we may have apriori explanati…
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar +1
Adversarial training is a standard technique for training adversarially robust models. In this paper, we study adversarial training as an alternating best-response strategy in a 2-…
Label Propagation with Weak Supervision
Rattana Pukdee, Dylan Sam, Maria-Florina Balcan +1
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In…
Improving Transformation Invariance in Contrastive Representation Learning
Adam Foster, Rattana Pukdee, Tom Rainforth
We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance a…