3 citations · 3 across the 8 of their papers we have counts for
10 papers
Optimal Inference with Black-box Predictions
Lucas Kania, Abhinav Chakraborty, Edward Kennedy +2
Powerful black-box predictive models have motivated many proposals for combining observed data with predictions to perform valid statistical inference. Despite this progress, the f…
The Fundamental Limits of Valid Transport Map Estimation
Sivaraman Balakrishnan
Many modern generative modeling methods, including diffusion models, normalizing flows, and flow matching, estimate transport maps or plans between distributions without explicitly…
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
Lujing Zhang, Daniel Hsu, Sivaraman Balakrishnan
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools…
On the Equivalence between Neyman Orthogonality and Pathwise Differentiability
Yuxi Chen, Edward H. Kennedy, Sivaraman Balakrishnan
It has been frequently observed that Neyman orthogonality, the central device underlying double/debiased machine learning (Chernozhukov et al., 2018), and pathwise differentiabilit…
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu +2
Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views…
Testing Imprecise Hypotheses
Lucas Kania, Tudor Manole, Larry Wasserman +1
Many scientific applications involve testing theories that are only partially specified. This task often amounts to testing the goodness-of-fit of a candidate distribution while al…
On Robust Hypothesis Testing with respect to the Hellinger Distance
Eeshan Modak, Sivaraman Balakrishnan, Ananda Theertha Suresh
We study a variant of the simple hypothesis testing problem where observed samples do not necessarily come from either of the specified distributions, but rather from a close varia…
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
Sivaraman Balakrishnan, Tudor Manole, Larry Wasserman
In many applications of optimal transport (OT), the object of primary interest is the optimal transport map. This map rearranges mass from one probability distribution to another i…
Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications
Sivaraman Balakrishnan, Tudor Manole
We study estimators of the optimal transport (OT) map between two probability distributions. We focus on plugin estimators derived from the OT map between estimates of the underlyi…
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
Saurabh Garg, Amrith Setlur, Zachary Chase Lipton +3
Self-training and contrastive learning have emerged as leading techniques for incorporating unlabeled data, both under distribution shift (unsupervised domain adaptation) and when…