5 citations · 10 across the 22 of their papers we have counts for
7 papers · 1 filter
On the Hardness of Learning Regular Expressions
Idan Attias, Lev Reyzin, Nathan Srebro +1
Despite the theoretical significance and wide practical use of regular expressions, the computational complexity of learning them has been largely unexplored. We study the computat…
Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning
Idan Attias, Steve Hanneke, Arvind Ramaswami
We study online and transductive online learning when the learner interacts with the concept class only via Empirical Risk Minimization (ERM) or weak consistency oracles on arbitra…
Learning-Augmented Algorithms for Boolean Satisfiability
Idan Attias, Xing Gao, Lev Reyzin
Learning-augmented algorithms are a prominent recent development in beyond worst-case analysis. In this framework, a problem instance is provided with a prediction (``advice'') fro…
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
Alexander Ryabchenko, Idan Attias, Daniel M. Roy
We study online learning with oblivious losses and delays under a novel ``capacity constraint'' that limits how many past rounds can be tracked simultaneously for delayed feedback.…
PAC Learning with Improvements
Idan Attias, Avrim Blum, Keziah Naggita +3
One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes samples to learn to error (and more, if the…
On Traceability in Stochastic Convex Optimization
Sasha Voitovych, Mahdi Haghifam, Idan Attias +3
In this paper, we investigate the necessity of traceability for accurate learning in stochastic convex optimization (SCO) under geometries. Informally, we say a learning a…
Fixed Point Computation: Beating Brute Force with Smoothed Analysis
Idan Attias, Yuval Dagan, Constantinos Daskalakis +2
We propose a new algorithm that finds an -approximate fixed point of a smooth function from the -dimensional unit ball to itself. We use the general framew…