Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1806.04613
Cited By
Improving Regression Performance with Distributional Losses
12 June 2018
Ehsan Imani
Martha White
UQCV
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Improving Regression Performance with Distributional Losses"
48 / 48 papers shown
From Regression to Classification: Exploring the Benefits of Categorical Representations of Energy in MLIPs
Ahmad Ali
80
0
0
01 Dec 2025
A Markov Decision Process for Variable Selection in Branch & Bound
Paul Strang
Zacharie Alès
Côme Bissuel
Olivier Juan
S. Kedad-Sidhoum
Emmanuel Rachelson
170
1
0
22 Oct 2025
D2 Actor Critic: Diffusion Actor Meets Distributional Critic
Lunjun Zhang
Shuo Han
Hanrui Lyu
Bradly C. Stadie
OffRL
382
1
0
03 Oct 2025
Rectifying Regression in Reinforcement Learning
Alex Ayoub
David Szepesvári
Alireza Baktiari
Csaba Szepesvári
Dale Schuurmans
OffRL
247
1
0
01 Oct 2025
Noise-Guided Transport for Imitation Learning
Lionel Blondé
Joao A. Candido Ramos
Alexandros Kalousis
OT
257
0
0
30 Sep 2025
XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Daniel Palenicek
Florian Vogt
Joe Watson
Ingmar Posner
Jan Peters
208
5
0
29 Sep 2025
Relative Entropy Pathwise Policy Optimization
C. Voelcker
Axel Brunnbauer
Marcel Hussing
Michal Nauman
Pieter Abbeel
Eric Eaton
Radu Grosu
Amir-massoud Farahmand
Igor Gilitschenski
506
1
0
15 Jul 2025
Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
Dongyoon Hwang
Hojoon Lee
Jaegul Choo
D. Park
Jongho Park
ReLM
OffRL
LRM
311
4
0
01 Jul 2025
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh
Hamidreza Kamkari
Valentin Thomas
Benson Li
Junwei Ma
Jesse C. Cresswell
Rahul G. Krishnan
CML
266
19
0
09 Jun 2025
Horizon Reduction Makes RL Scalable
Seohong Park
Kevin Frans
Deepinder Mann
Benjamin Eysenbach
Aviral Kumar
Sergey Levine
OffRL
739
25
0
04 Jun 2025
Risk Bounds For Distributional Regression
Carlos Misael Madrid Padilla
Oscar Hernan Madrid Padilla
S. Chatterjee
362
1
0
14 May 2025
Simulating the Real World: A Unified Survey of Multimodal Generative Models
Yuqi Hu
Longguang Wang
Xian Liu
L. Chen
Yuwei Guo
Yukai Shi
Ce Liu
Anyi Rao
Zeyu Wang
Hui Xiong
VGen
SyDa
255
10
0
06 Mar 2025
Improving Value-based Process Verifier via Structural Prior Injection
Zetian Sun
Dongfang Li
Baotian Hu
Jun Yu
Min Zhang
366
0
0
21 Feb 2025
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee
Youngdo Lee
Takuma Seno
Donghu Kim
Peter Stone
Jaegul Choo
558
21
0
21 Feb 2025
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Bryce Ferenczi
Michael G. Burke
Tom Drummond
292
0
0
11 Oct 2024
The Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang
Nathan Kallus
Wen Sun
OffRL
744
14
0
19 Sep 2024
Distribution Learning for Molecular Regression
Nima Shoghi
Pooya Shoghi
Anuroop Sriram
Abhishek Das
OOD
331
1
0
30 Jul 2024
Efficient World Models with Context-Aware Tokenization
Vincent Micheli
Eloi Alonso
François Fleuret
OffRL
VLM
274
25
0
27 Jun 2024
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Xicheng Zhang
Fuchun Sun
Bin Zhao
Junchi Yan
Xiu Li
Xuelong Li
OffRL
364
11
0
22 Jun 2024
Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?
Denis Tarasov
Kirill Brilliantov
Dmitrii Kharlapenko
OffRL
221
4
0
10 Jun 2024
Winner-takes-all learners are geometry-aware conditional density estimators
International Conference on Machine Learning (ICML), 2024
Victor Letzelter
David Perera
Cédric Rommel
Mathieu Fontaine
S. Essid
Gael Richard
Patrick Pérez
274
6
0
07 Jun 2024
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
Michal Nauman
M. Ostaszewski
Krzysztof Jankowski
Piotr Milo's
Marek Cygan
OffRL
356
87
0
25 May 2024
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Dongseong Hwang
ODL
794
18
0
21 May 2024
Point Cloud Models Improve Visual Robustness in Robotic Learners
Skand Peri
Iain Lee
Chanho Kim
Fuxin Li
Tucker Hermans
Stefan Lee
3DPC
334
16
0
29 Apr 2024
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother
Jordi Orbay
Q. Vuong
Adrien Ali Taïga
Yevgen Chebotar
...
Sergey Levine
Pablo Samuel Castro
Aleksandra Faust
Aviral Kumar
Rishabh Agarwal
OffRL
315
113
0
06 Mar 2024
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
H. V. Hasselt
Razvan Pascanu
James Martens
Will Dabney
AI4CE
418
66
0
29 Feb 2024
Investigating the Histogram Loss in Regression
Ehsan Imani
Kai Luedemann
Sam Scholnick-Hughes
Esraa Elelimy
Martha White
UQCV
252
10
0
20 Feb 2024
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
Neural Information Processing Systems (NeurIPS), 2023
Victor Letzelter
Mathieu Fontaine
Mickaël Chen
Patrick Pérez
S. Essid
Ga¨el Richard
371
14
0
02 Nov 2023
Combining Behaviors with the Successor Features Keyboard
Neural Information Processing Systems (NeurIPS), 2023
Wilka Carvalho
Andre Saraiva
Angelos Filos
Andrew Kyle Lampinen
Loic Matthey
Richard L. Lewis
Honglak Lee
Satinder Singh
Danilo Jimenez Rezende
Daniel Zoran
289
11
0
24 Oct 2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
International Conference on Machine Learning (ICML), 2023
Mark Rowland
Yunhao Tang
Clare Lyle
Rémi Munos
Marc G. Bellemare
Will Dabney
236
13
0
28 May 2023
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang
L. Pan
Ricky T. Q. Chen
Aaron Courville
Yoshua Bengio
354
33
0
11 Feb 2023
Mastering Diverse Domains through World Models
Danijar Hafner
J. Pašukonis
Jimmy Ba
Timothy Lillicrap
491
1,018
0
10 Jan 2023
Neural Regression For Scale-Varying Targets
Adam Khakhar
Jacob Buckman
400
3
0
14 Nov 2022
Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Swapnil Dey
Vipul Arora
S. Tripathi
OOD
257
3
0
02 Oct 2022
Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation
Thomas Gonzalez
Antoine Blais
Nicolas P. Couellan
Christian Ruiz
211
5
0
03 Jun 2022
Disturbing Target Values for Neural Network Regularization
Yongho Kim
Hanna Lukashonak
Paweena Tarepakdee
Klavdia Zavalich
Mofassir ul Islam Arif
159
0
0
11 Oct 2021
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
Ke Sun
Yingnan Zhao
Shangling Jui
Linglong Kong
OOD
431
22
0
17 Sep 2021
Human Pose Regression with Residual Log-likelihood Estimation
IEEE International Conference on Computer Vision (ICCV), 2021
Jiefeng Li
Siyuan Bian
Ailing Zeng
Can Wang
Bo Pang
Wentao Liu
Cewu Lu
479
285
0
23 Jul 2021
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
Muhammad Rizki Maulana
W. Lee
204
2
0
05 Jul 2021
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Roberto Vega
Pouneh Gorji
Zichen Zhang
Xuebin Qin
A. Hareendranathan
J. Kapur
Jacob L. Jaremko
Russell Greiner
155
4
0
11 Feb 2021
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
ACM Transactions on Graphics (TOG), 2020
Albert Matveev
Ruslan Rakhimov
Alexey Artemov
G. Bobrovskikh
Vage Egiazarian
Emil Bogomolov
Daniele Panozzo
Denis Zorin
Evgeny Burnaev
3DPC
364
18
0
30 Nov 2020
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms
Adaptive Agents and Multi-Agent Systems (AAMAS), 2020
Shangtong Zhang
Romain Laroche
H. V. Seijen
Shimon Whiteson
Rémi Tachet des Combes
540
15
0
02 Oct 2020
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
IEEE Signal Processing Letters (IEEE SPL), 2020
Jun Qi
Jun Du
Sabato Marco Siniscalchi
Xiaoli Ma
Chin-Hui Lee
207
286
0
12 Aug 2020
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
Simone Totaro
Ioannis Boukas
Anders Jonsson
Bertrand Cornélusse
135
36
0
16 May 2020
Deep Ordinal Regression for Pledge Specificity Prediction
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
Shivashankar Subramanian
Trevor Cohn
Timothy Baldwin
251
9
0
31 Aug 2019
Stochastically Dominant Distributional Reinforcement Learning
International Conference on Machine Learning (ICML), 2019
John D. Martin
Michal Lyskawinski
Xiaohu Li
Brendan Englot
370
26
0
17 May 2019
A Comparative Analysis of Expected and Distributional Reinforcement Learning
AAAI Conference on Artificial Intelligence (AAAI), 2019
Clare Lyle
Pablo Samuel Castro
Marc G. Bellemare
OffRL
290
91
0
30 Jan 2019
Gradient Harmonized Single-stage Detector
Buyu Li
Yu Liu
Xiaogang Wang
ObjD
268
581
0
13 Nov 2018
1
Page 1 of 1
