254 citations · 882 across the 66 of their papers we have counts for
15 papers · 1 filter
RLSbench: Domain Adaptation Under Relaxed Label Shift
Saurabh Garg, Nick Erickson, James Sharpnack +3
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.…
Domain Adaptation under Missingness Shift
Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable. In this pa…
Domain Adaptation under Open Set Label Shift
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, bu…
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton +2
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops. In this work…
Mixture Proportion Estimation and PU Learning: A Modern Approach
Saurabh Garg, Yifan Wu, Alex Smola +2
Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifie…
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan +1
We consider the task of heavy-tailed statistical estimation given streaming -dimensional samples. This could also be viewed as stochastic optimization under heavy-tailed distrib…
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter +1
To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on th…
On Proximal Policy Optimization's Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto +6
Modern policy gradient algorithms such as Proximal Policy Optimization (PPO) rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful l…
A Unified View of Label Shift Estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan +1
Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label margin…
Path Length Bounds for Gradient Descent and Flow
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
We derive bounds on the path length of gradient descent (GD) and gradient flow (GF) curves for various classes of smooth convex and nonconvex functions. Among other results, we…
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation…
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
We study methods for aggregating pairwise comparison data in order to estimate outcome probabilities for future comparisons among a collection of n items. Working within a flexible…
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley +3
Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordi…
Efficient Active Algorithms for Hierarchical Clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu +1
Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag beh…
Sparse Additive Functional and Kernel CCA
Sivaraman Balakrishnan, Kriti Puniyani, John Lafferty
Canonical Correlation Analysis (CCA) is a classical tool for finding correlations among the components of two random vectors. In recent years, CCA has been widely applied to the an…