Stem regret$ (all stems)

38 papers:

VLDB-2015-AslayLB0L #social

Viral Marketing Meets Social Advertising: Ad Allocation with Minimum Regret (ÇA, WL, FB, AG, LVSL), pp. 822–833.

VLDB-2015-FaulknerBL #query

k-Regret Queries with Nonlinear Utilities (TKF, WB, AL), pp. 2098–2109.

STOC-2015-ZhuLO #matrix #multi

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates (ZAZ, ZL, LO), pp. 237–245.

ICML-2015-Bou-AmmarTE #learning #policy #sublinear

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.

ICML-2015-CarpentierV #infinity

Simple regret for infinitely many armed bandits (AC, MV), pp. 1133–1141.

ICML-2015-HugginsT

Risk and Regret of Hierarchical Bayesian Learners (JH, JT), pp. 1442–1451.

ICML-2015-KomiyamaHN #analysis #multi #probability #problem

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays (JK, JH, HN), pp. 1152–1161.

ICML-2015-LakshmananOR #bound #learning

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.

VLDB-2014-ChesterTVW #set

Computing k-Regret Minimizing Sets (SC, AT, SV, SW), pp. 389–400.

STOC-2014-DekelDKP

Bandits with switching costs: T2/3 regret (OD, JD, TK, YP), pp. 459–467.

STOC-2014-FriggstadS #algorithm #approximate #bound

Approximation algorithms for regret-bounded vehicle routing and applications to distance-constrained vehicle routing (ZF, CS), pp. 744–753.

ICML-c1-2014-CombesP #algorithm #bound

Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms (RC, AP), pp. 521–529.

ICML-c2-2014-DworkinKN

Pursuit-Evasion Without Regret, with an Application to Trading (LD, MK, YN), pp. 1521–1529.

ICML-c2-2014-KricheneDB #convergence #learning #on the

On the convergence of no-regret learning in selfish routing (WK, BD, AMB), pp. 163–171.

CHI-2013-SleeperCKUACS #quote #twitter

“I read my Twitter the next morning and was astonished”: a conversational perspective on Twitter regrets (MS, JC, PGK, BU, AA, LFC, NMS), pp. 3277–3286.

ICML-c1-2013-HallW #modelling #online #programming

Dynamical Models and tracking regret in online convex programming (ECH, RW), pp. 579–587.

ICML-c1-2013-MaillardNOR #bound #learning #representation

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.

CAV-2013-EssenJ #program repair

Program Repair without Regret (CvE, BJ), pp. 896–911.

SIGMOD-2012-NanongkaiLSM #interactive

Interactive regret minimization (DN, AL, ADS, KM), pp. 109–120.

ICML-2012-BowlingZ #on the

On Local Regret (MB, MZ), p. 56.

ICML-2012-DekelTA #adaptation #learning #online #policy

Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.

ICML-2012-FreitasSZ #bound #exponential #process

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (NdF, AJS, MZ), p. 125.

ICML-2012-LanctotGBB #game studies #learning

No-Regret Learning in Extensive-Form Games with Imperfect Recall (ML, RGG, NB, MB), p. 135.

KDD-2012-OuyangG #adaptation #named #performance

NASA: achieving lower regrets and faster rates via adaptive stepsizes (HO, AGG), pp. 159–167.

ICML-2011-Scott #bound #classification

Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs (CS), pp. 153–160.

VLDB-2010-NanongkaiSLLX #database

Regret-Minimizing Representative Databases (DN, ADS, AL, RJL, J(X), pp. 1114–1124.

ICML-2010-SrinivasKKS #design #optimisation #process

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (NS, AK, SK, MWS), pp. 1015–1022.

STOC-2009-Even-DarMN #convergence #game studies #on the

On the convergence of regret minimization dynamics in concave games (EED, YM, UN), pp. 523–532.

STOC-2009-KleinbergPT #game studies #learning #multi

Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract (RK, GP, ÉT), pp. 533–542.

ICML-2009-ReidW #bound

Surrogate regret bounds for proper losses (MDR, RCW), pp. 897–904.

KDD-2009-Delage #library #online #ranking

Regret-based online ranking for a growing digital library (ED), pp. 229–238.

MLDM-2009-Calliess #on the

On Fixed Convex Combinations of No-Regret Learners (JPC), pp. 494–504.

RecSys-2009-ViappianiB #recommendation #set

Regret-based optimal recommendation sets in conversational recommender systems (PV, CB), pp. 101–108.

STOC-2008-BlumHLR

Regret minimization and the price of total anarchy (AB, MH, KL, AR), pp. 373–382.

ICML-2008-GordonGM #game studies #learning

No-regret learning in convex games (GJG, AG, CM), pp. 360–367.

ICML-2005-ChangK #learning

Hedged learning: regret-minimization with learning experts (YHC, LPK), pp. 121–128.

ICML-2001-JafariGGE #equilibrium #game studies #learning #nash #on the

On No-Regret Learning, Fictitious Play, and Nash Equilibrium (AJ, AG, DG, GE), pp. 226–233.

ICML-1998-Cesa-BianchiF #bound #finite #multi #problem

Finite-Time Regret Bounds for the Multiarmed Bandit Problem (NCB, PF), pp. 100–108.