1 citations · 2 across the 8 of their papers we have counts for
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The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization
Nived Rajaraman, Yanjun Han
We establish improved lower bounds on the minimax expected regret of stochastic bandit convex optimization for -Lipschitz functions on the -dimensional Euclidean ball. For ti…
Interactive Learning of Single-Index Models via Stochastic Gradient Descent
Nived Rajaraman, Yanjun Han
Stochastic gradient descent (SGD) is a cornerstone algorithm for high-dimensional optimization, renowned for its empirical successes. Recent theoretical advances have provided a de…
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
Zhangyi Liu, Huaizhi Qu, Xiaowei Yin +4
Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited bud…
Universal priors: solving empirical Bayes via Bayesian inference and pretraining
Nick Cannella, Anzo Teh, Yanjun Han +1
We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achieves strong performance on empirical B…