12 citations · 23 across the 5 of their papers we have counts for
7 papers
Discrete Sampling using Semigradient-based Product Mixtures
Alkis Gotovos, Hamed Hassani, Andreas Krause +1
We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in m…
A New Coding Paradigm for the Primitive Relay Channel
Marco Mondelli, S. Hamed Hassani, Rüdiger Urbanke
We consider the primitive relay channel, where the source sends a message to the relay and to the destination, and the relay helps the communication by transmitting an additional m…
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
In this paper, we study the problem of \textit{constrained} and \textit{stochastic} continuous submodular maximization. Even though the objective function is not concave (nor conve…
Stochastic Submodular Maximization: The Case of Coverage Functions
Mohammad Reza Karimi, Mario Lucic, Hamed Hassani +1
Stochastic optimization of continuous objectives is at the heart of modern machine learning. However, many important problems are of discrete nature and often involve submodular ob…
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means
Olivier Bachem, Mario Lucic, S. Hamed Hassani +1
Uniform deviation bounds limit the difference between a model's expected loss and its loss on an empirical sample uniformly for all models in a learning problem. As such, they are…
Construction of Polar Codes with Sublinear Complexity
Marco Mondelli, S. Hamed Hassani, Rüdiger Urbanke
Consider the problem of constructing a polar code of block length for the transmission over a given channel . Typically this requires to compute the reliability of all the $…
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting
Lin Chen, Hamed Hassani, Amin Karbasi
In this paper, we consider the problem of actively learning a linear classifier through query synthesis where the learner can construct artificial queries in order to estimate the…