23 citations · 26 across the 4 of their papers we have counts for
7 papers
Convergence of online -means
Sanjoy Dasgupta, Gaurav Mahajan, Geelon So
We prove asymptotic convergence for a general class of -means algorithms performed over streaming data from a distribution: the centers asymptotically converge to the set of sta…
Learning what to remember
Robi Bhattacharjee, Gaurav Mahajan
We consider a lifelong learning scenario in which a learner faces a neverending and arbitrary stream of facts and has to decide which ones to retain in its limited memory. We intro…
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon S. Du, Sham M. Kakade, Jason D. Lee +4
This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function a…
Point Location and Active Learning: Learning Halfspaces Almost Optimally
Max Hopkins, Daniel M. Kane, Shachar Lovett +1
Given a finite set and a binary linear classifier , how many queries of the form are required to learn the label of eve…
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity
Simon S. Du, Jason D. Lee, Gaurav Mahajan +1
The current paper studies the problem of agnostic -learning with function approximation in deterministic systems where the optimal -function is approximable by a function in…
Noise-tolerant, Reliable Active Classification with Comparison Queries
Max Hopkins, Daniel Kane, Shachar Lovett +1
With the explosion of massive, widely available unlabeled data in the past years, finding label and time efficient, robust learning algorithms has become ever more important in the…