519 citations · 1.6k across the 49 of their papers we have counts for
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Private Causal Inference
Matt J. Kusner, Yu Sun, Karthik Sridharan +1
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from stati…
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
Alexander Rakhlin, Karthik Sridharan
We study an equivalence of (i) deterministic pathwise statements appearing in the online learning literature (termed \emph{regret bounds}), (ii) high-probability tail bounds for th…
Adaptive Online Learning
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or mo…
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
Alexander Rakhlin, Karthik Sridharan
We study online prediction where regret of the algorithm is measured against a benchmark defined via evolving constraints. This framework captures online prediction on graphs, as w…
Learning with Square Loss: Localization through Offset Rademacher Complexity
Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
We consider regression with square loss and general classes of functions without the boundedness assumption. We introduce a notion of offset Rademacher complexity that provides a t…
Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts
Alexander Rakhlin, Karthik Sridharan
We analyze the problem of sequential probability assignment for binary outcomes with side information and logarithmic loss, where regret---or, redundancy---is measured with respect…