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Oracle-Efficient and Parameter-Free Agnostic Smoothed Online Learning
Sasha Voitovych, Adam Block, Alexander Rakhlin +1
Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially. This generality, however,…
Exact simulation of diffusions and improved algorithms for log-concave sampling
Fan Chen, Sinho Chewi, Alexander Rakhlin +1
We study exact simulation of diffusions via rejection sampling on path space using unbiased estimators of the density ratio obtained from Girsanov's theorem. When applied to the un…
Learning with Simulators: No Regret in a Computationally Bounded World
Sasha Voitovych, Abhishek Shetty, Noah Golowich +1
Understanding the minimal assumptions necessary for generalization is the fundamental question in learning theory. Unfortunately, most results rely heavily on independence (or some…
The Sample Complexity of Multiclass and Sparse Contextual Bandits
Liad Erez, Fan Chen, Alon Cohen +4
We study contextual bandits in the stochastic i.i.d.\ setting, where a learner observes contexts drawn from an unknown distribution, selects actions from a finite set , and aims…
Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
Fan Chen, Jian Qian, Alexander Rakhlin +1
Self-normalized martingale inequalities lie at the heart of confidence ellipsoids for online least squares and, more broadly, many bandit and reinforcement-learning results. Yet ex…
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo
We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting w…
Demonstration Experiments
Guido Imbens, Lorenzo Masoero, Alexander Rakhlin +2
Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatme…
Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
Moïse Blanchard, Abhishek Shetty, Alexander Rakhlin
Understanding minimal assumptions that enable learning and generalization is perhaps the central question of learning theory. Several celebrated results in statistical learning the…
High-accuracy log-concave sampling with stochastic queries
Fan Chen, Sinho Chewi, Constantinos Daskalakis +1
We show that high-accuracy guarantees for log-concave sampling -- that is, iteration and query complexities which scale as , where is the desired target…
High-accuracy sampling for diffusion models and log-concave distributions
Fan Chen, Sinho Chewi, Constantinos Daskalakis +1
We present algorithms for diffusion model sampling which obtain -error in steps, given access to -accurate score estimates in . Thi…