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1802.09127
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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
International Conference on Learning Representations (ICLR), 2018
26 February 2018
C. Riquelme
George Tucker
Jasper Snoek
BDL
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Papers citing
"Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling"
50 / 231 papers shown
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320
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Zheng Wen
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Jesse H. Krijthe
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294
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Farhad Pourpanah
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Dana Rezazadegan
Tianpeng Liu
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Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
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12 Nov 2020
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Roger C. Grosse
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Bayesian Deep Learning via Subnetwork Inference
International Conference on Machine Learning (ICML), 2020
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Eric T. Nalisnick
J. Allingham
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José Miguel Hernández-Lobato
UQCV
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28 Oct 2020
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
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Jasper Snoek
Andrew L. Beam
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Neural Thompson Sampling
International Conference on Learning Representations (ICLR), 2020
Weitong Zhang
Dongruo Zhou
Lihong Li
Quanquan Gu
279
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02 Oct 2020
Uncertainty Sets for Image Classifiers using Conformal Prediction
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29 Sep 2020
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
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R. Awasthi
K. K. Guliani
Saif Ahmad Khan
Aniket Vashishtha
M. S. Gill
Arshita Bhatt
A. Nagori
Aniket Gupta
Ponnurangam Kumaraguru
Tavpritesh Sethi
364
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14 Sep 2020
β
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Dionysis Manousakas
Cecilia Mascolo
210
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31 Aug 2020
A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples
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Alexandru Paul Condurache
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218
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21 Aug 2020
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
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296
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17 Aug 2020
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
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S. Ktena
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Alykhan Tejani
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195
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03 Aug 2020
Greedy Bandits with Sampled Context
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40
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27 Jul 2020
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UQCV
163
13
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12 Jul 2020
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
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Tong Yu
Branislav Kveton
Zheng Wen
Ruiyi Zhang
Ole J. Mengshoel
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203
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09 Jul 2020
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits
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Aditya A. Ramesh
Paulo E. Rauber
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Jürgen Schmidhuber
137
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09 Jul 2020
Hedging using reinforcement learning: Contextual
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Giuseppe Nuti
M. Sala
O. Szehr
OffRL
218
18
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03 Jul 2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
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Ahmed Alaa
Zhaozhi Qian
M. Schaar
UQCV
BDL
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216
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26 Jun 2020
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
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Cooper Lorsung
Yaniv Yacoby
Finale Doshi-Velez
Weiwei Pan
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241
4
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21 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
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Zi Lin
Shreyas Padhy
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Tania Bedrax-Weiss
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831
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17 Jun 2020
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Jackie Baek
Vivek F. Farias
188
9
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11 Jun 2020
Gaussian Gated Linear Networks
Neural Information Processing Systems (NeurIPS), 2020
David Budden
Adam H. Marblestone
Eren Sezener
Tor Lattimore
Greg Wayne
J. Veness
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AI4CE
211
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10 Jun 2020
Meta-Learning Bandit Policies by Gradient Ascent
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Martin Mladenov
Chih-Wei Hsu
Manzil Zaheer
Csaba Szepesvári
Craig Boutilier
183
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09 Jun 2020
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
Qin Ding
Cho-Jui Hsieh
James Sharpnack
324
43
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07 Jun 2020
A Linear Bandit for Seasonal Environments
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Vito Bellini
Giovanni Zappella
166
7
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28 Apr 2020
Deep Reinforcement Learning with Weighted Q-Learning
Andrea Cini
Carlo DÉramo
Jan Peters
Cesare Alippi
OffRL
172
10
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20 Mar 2020
Self-Supervised Contextual Bandits in Computer Vision
A. Deshmukh
Abhimanu Kumar
Levi Boyles
Denis Xavier Charles
Eren Manavoglu
Ürün Dogan
SSL
148
3
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18 Mar 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Journal of Computational Physics (JCP), 2020
Liu Yang
Xuhui Meng
George Karniadakis
PINN
363
978
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13 Mar 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
International Conference on Machine Learning (ICML), 2020
Agustinus Kristiadi
Matthias Hein
Philipp Hennig
BDL
UQCV
367
327
0
24 Feb 2020
On Thompson Sampling with Langevin Algorithms
International Conference on Machine Learning (ICML), 2020
Eric Mazumdar
Aldo Pacchiano
Yi-An Ma
Peter L. Bartlett
Sai Li
254
12
0
23 Feb 2020
Online Learning in Contextual Bandits using Gated Linear Networks
Neural Information Processing Systems (NeurIPS), 2020
Eren Sezener
Marcus Hutter
David Budden
Jianan Wang
J. Veness
165
10
0
21 Feb 2020
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Neural Information Processing Systems (NeurIPS), 2020
Mao Ye
Zhaolin Ren
Qiang Liu
175
8
0
21 Feb 2020
Bayesian Meta-Prior Learning Using Empirical Bayes
Management Sciences (MS), 2020
Sareh Nabi
Houssam Nassif
Joseph Hong
H. Mamani
Guido Imbens
343
22
0
04 Feb 2020
Machine learning based co-creative design framework
Brian Quanz
Wei-Ling Sun
Ajay A. Deshpande
Dhruv Shah
Jae-eun Park
HAI
98
12
0
23 Jan 2020
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
N. Brosse
C. Riquelme
Alice Martin
Sylvain Gelly
Eric Moulines
BDL
OOD
UQCV
163
36
0
22 Jan 2020
Incentivising Exploration and Recommendations for Contextual Bandits with Payments
Priyank Agrawal
Theja Tulabandhula
OffRL
137
5
0
22 Jan 2020
Continuous Meta-Learning without Tasks
Neural Information Processing Systems (NeurIPS), 2019
James Harrison
Apoorva Sharma
Chelsea Finn
Marco Pavone
CLL
OOD
297
80
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18 Dec 2019
Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober
C. Rasmussen
BDL
192
46
0
18 Dec 2019
Bayesian Linear Regression on Deep Representations
J. Moberg
Lennart Svensson
Juliano Pinto
H. Wymeersch
BDL
UQCV
84
2
0
14 Dec 2019
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging
J. Zech
Jessica Zosa Forde
Michael L. Littman
203
6
0
08 Dec 2019
A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data
Niklas Rindtorff
Mingyu Lu
Nisarg A. Patel
Huahua Zheng
Alexander DÁmour
96
5
0
11 Nov 2019
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Sam Daulton
Shaun Singh
Vashist Avadhanula
Drew Dimmery
E. Bakshy
147
14
0
02 Nov 2019
Thompson Sampling via Local Uncertainty
International Conference on Machine Learning (ICML), 2019
Zhendong Wang
Mingyuan Zhou
170
21
0
30 Oct 2019
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Sharan Vaswani
Abbas Mehrabian
A. Durand
Branislav Kveton
213
31
0
11 Oct 2019
AutoML for Contextual Bandits
Praneet Dutta
Man Kit Cheuk
Jonathan S Kim
M. Mascaro
OffRL
91
7
0
07 Sep 2019
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