Adji B. Dieng

According to our database, Adji B. Dieng authored at least 25 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Beyond Aesthetics: Cultural Competence in Text-to-Image Models.

Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, Shachi Dave

CoRR, 2024

Relational Reasoning On Graphs Using Opinion Dynamics.

Yulong Yang, Bowen Feng, Keqin Wang, Naomi Ehrich Leonard, Adji Bousso Dieng, Christine Allen-Blanchette

CoRR, 2024

Constraint-Aware Diffusion Models for Trajectory Optimization.

Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson

CoRR, 2024

Alternators For Sequence Modeling.

Mohammad Reza Rezaei, Adji Bousso Dieng

CoRR, 2024

Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model.

Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson

CoRR, 2024

Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design.

Quan Nguyen, Adji Bousso Dieng

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning.

Amey P. Pasarkar, Adji Bousso Dieng

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Dan Friedman, Adji Bousso Dieng

Trans. Mach. Learn. Res., 2023

DMLR: Data-centric Machine Learning Research - Past, Present and Future.

Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson

CoRR, 2023

LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions.

Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso Dieng

CoRR, 2023

2022

Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients.

Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Deep Probabilistic Graphical Modeling.

Adji B. Dieng

CoRR, 2021

Consistency Regularization for Variational Auto-Encoders.

Samarth Sinha, Adji Bousso Dieng

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Deep Probabilistic Graphical Modeling.

Adji Bousso Dieng

PhD thesis, 2020

Topic Modeling in Embedding Spaces.

Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei

Trans. Assoc. Comput. Linguistics, 2020

2019

Prescribed Generative Adversarial Networks.

Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias

CoRR, 2019

The Dynamic Embedded Topic Model.

Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

CoRR, 2019

Reweighted Expectation Maximization.

Adji B. Dieng, John W. Paisley

CoRR, 2019

Avoiding Latent Variable Collapse with Generative Skip Models.

Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions.

Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

Proceedings of the 35th International Conference on Machine Learning, 2018

Noisin: Unbiased Regularization for Recurrent Neural Networks.

Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Variational Inference via \chi Upper Bound Minimization.

Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency.

Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Edward: A library for probabilistic modeling, inference, and criticism.

Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei

CoRR, 2016

The $χ$-Divergence for Approximate Inference.

Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

CoRR, 2016