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20102024
most citedApprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods

157 citations · 352 across the 23 of their papers we have counts for

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Showing 2016Show all

6 papers · 1 filter

cs.LG201619 cited

SDP Relaxation with Randomized Rounding for Energy Disaggregation

Kiarash Shaloudegi, András György, Csaba Szepesvári +1

We develop a scalable, computationally efficient method for the task of energy disaggregation for home appliance monitoring. In this problem the goal is to estimate the energy cons…

cs.LG2016

Sequential Learning without Feedback

Manjesh Hanawal, Csaba Szepesvari, Venkatesh Saligrama

In many security and healthcare systems a sequence of features/sensors/tests are used for detection and diagnosis. Each test outputs a prediction of the latent state, and carries w…

stat.ML20167 cited

The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits

Tor Lattimore, Csaba Szepesvari

Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing te…

stat.ML201612 cited

Multiclass Classification Calibration Functions

Bernardo Ávila Pires, Csaba Szepesvári

In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for eas…

stat.ML20162 cited

Chaining Bounds for Empirical Risk Minimization

Gábor Balázs, András György, Csaba Szepesvári

This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise…

cs.LG20169 cited

Stochastic Rank-1 Bandits

Sumeet Katariya, Branislav Kveton, Csaba Szepesvari +2

We propose stochastic rank- bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of t…