9 citations · 14 across the 11 of their papers we have counts for
11 papers
Reward Learning from Best-of- Preference Data: Targets, Tradeoffs, and Design Principles
Rattana Pukdee, Maria-Florina Balcan, Pradeep Ravikumar
Best-of- sampling is widely used to construct pairwise preference data: candidates are drawn from a base distribution, and the best is paired with a rejected response. Despi…
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…
Spectrally Transformed Kernel Regression
Runtian Zhai, Rattana Pukdee, Roger Jin +2
Unlabeled data is a key component of modern machine learning. In general, the role of unlabeled data is to impose a form of smoothness, usually from the similarity information enco…
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…
Sharp asymptotics on the compression of two-layer neural networks
Mohammad Hossein Amani, Simone Bombari, Marco Mondelli +2
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which…
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…