Publications by Jan Hazla | EPFL Graph Search

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We study the phenomenon of intransitivity in models of dice and voting. First, we follow a recent thread of research for n-sided dice with pairwise ordering induced by the probability, relative to 1/2, that a throw from one die is higher than the other. We ...

We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior be ...

This paper considers "delta-almost Reed-Muller codes", i.e., linear codes spanned by evaluations of all but a delta fraction of monomials of degree at most d. It is shown that for any delta > 0 and any epsilon > 0, there exists a family of delta-almost Ree ...

How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stat ...

We strengthen the results from a recent work by the second author, achieving bounds on the weight distribution of binary linear codes that are successful under block-MAP (as well as bit-MAP) decoding on the BEC. We conclude that a linear code that is succe ...

We consider two problems regarding arithmetic progressions in symmetric sets in the finite field (product space) model. ...

This paper introduces the notion of "Initial Alignment" (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent on a fu ...

Jan Hazla

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The probability of intransitivity in dice and close elections

Bayesian Decision Making in Groups is Hard

Almost-Reed-Muller Codes Achieve Constant Rates for Random Errors

A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs

On Codes Decoding a Constant Fraction of Errors on the BSC

On arithmetic progressions in symmetric sets in finite field model

An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn