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Adji B. Dieng
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2020 – today
- 2025
- [i28]Mohammad Reza Rezaei, Adji Bousso Dieng:
The α-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performance. CoRR abs/2502.04593 (2025) - [i27]Amey P. Pasarkar, Adji Bousso Dieng:
The Vendiscope: An Algorithmic Microscope For Data Collections. CoRR abs/2502.10828 (2025) - [i26]Mohammad Reza Rezaei, Adji Bousso Dieng:
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs. CoRR abs/2502.11228 (2025) - [i25]Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova, Wahid Bhimji, Wei-Lun Chao, Chris Harris, Shih-Chieh Hsu, Hilmar Lapp, Mark S. Neubauer, Josephine M. Namayanja, Aneesh Subramanian, Philip C. Harris, Advaith Anand, David Edward Carlyn, Subhankar Ghosh
, Christopher G. Lawrence, Eric A. Moreno, Ryan Raikman, Jiaman Wu, Ziheng Zhang, Bayu Adhi, Mohammad Ahmadi Gharehtoragh
Building Machine Learning Challenges for Anomaly Detection in Science. CoRR abs/2503.02112 (2025) - 2024
- [c10]Amey P. Pasarkar, Adji Bousso Dieng:
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning. AISTATS 2024: 3808-3816 - [c9]Quan Nguyen, Adji Bousso Dieng:
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design. ICML 2024 - [c8]Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, Shachi Dave:
Beyond Aesthetics: Cultural Competence in Text-to-Image Models. NeurIPS 2024 - [i24]Anjian Li, Zihan Ding
Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model. CoRR abs/2403.05571 (2024) - [i23]Quan Nguyen, Adji Bousso Dieng:
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design. CoRR abs/2405.02449 (2024) - [i22]Mohammad Reza Rezaei, Adji Bousso Dieng:
Alternators For Sequence Modeling. CoRR abs/2405.11848 (2024) - [i21]Anjian Li, Zihan Ding
Constraint-Aware Diffusion Models for Trajectory Optimization. CoRR abs/2406.00990 (2024) - [i20]Yulong Yang, Bowen Feng, Keqin Wang, Naomi Ehrich Leonard, Adji Bousso Dieng, Christine Allen-Blanchette:
Relational Reasoning On Graphs Using Opinion Dynamics. CoRR abs/2406.14746 (2024) - [i19]Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, Shachi Dave:
Beyond Aesthetics: Cultural Competence in Text-to-Image Models. CoRR abs/2407.06863 (2024) - [i18]Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers, Adji Bousso Dieng:
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction. CoRR abs/2411.00177 (2024) - 2023
- [j2]Dan Friedman, Adji Bousso Dieng:
The Vendi Score: A Diversity Evaluation Metric for Machine Learning. Trans. Mach. Learn. Res. 2023 (2023) - [i17]Amey P. Pasarkar, Adji Bousso Dieng:
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning. CoRR abs/2310.12952 (2023) - [i16]Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions. CoRR abs/2310.14029 (2023) - [i15]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:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - 2022
- [c7]Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim:
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. NeurIPS 2022 - [i14]Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim:
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. CoRR abs/2206.06295 (2022) - [i13]Dan Friedman, Adji Bousso Dieng:
The Vendi Score: A Diversity Evaluation Metric for Machine Learning. CoRR abs/2210.02410 (2022) - 2021
- [c6]Samarth Sinha, Adji Bousso Dieng:
Consistency Regularization for Variational Auto-Encoders. NeurIPS 2021: 12943-12954 - [i12]Adji B. Dieng:
Deep Probabilistic Graphical Modeling. CoRR abs/2104.12053 (2021) - [i11]Samarth Sinha, Adji B. Dieng:
Consistency Regularization for Variational Auto-Encoders. CoRR abs/2105.14859 (2021) - 2020
- [b1]Adji Bousso Dieng:
Deep Probabilistic Graphical Modeling. Columbia University, USA, 2020 - [j1]Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei:
Topic Modeling in Embedding Spaces. Trans. Assoc. Comput. Linguistics 8: 439-453 (2020) - [e1]Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang:
Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. Proceedings of Machine Learning Research 118, PMLR 2020 [contents]
2010 – 2019
- 2019
- [c5]Adji B. Dieng, Yoon Kim, Alexander M. Rush
Avoiding Latent Variable Collapse with Generative Skip Models. AISTATS 2019: 2397-2405 - [i10]Adji B. Dieng, John W. Paisley:
Reweighted Expectation Maximization. CoRR abs/1906.05850 (2019) - [i9]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei:
Topic Modeling in Embedding Spaces. CoRR abs/1907.04907 (2019) - [i8]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei:
The Dynamic Embedded Topic Model. CoRR abs/1907.05545 (2019) - [i7]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias:
Prescribed Generative Adversarial Networks. CoRR abs/1910.04302 (2019) - 2018
- [c4]Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML 2018: 1251-1260 - [c3]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. ICML 2018: 4400-4409 - [i6]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. CoRR abs/1802.04220 (2018) - [i5]Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR abs/1805.01500 (2018) - [i4]Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei:
Avoiding Latent Variable Collapse With Generative Skip Models. CoRR abs/1807.04863 (2018) - 2017
- [c2]Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley:
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. ICLR (Poster) 2017 - [c1]Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
Variational Inference via \chi Upper Bound Minimization. NIPS 2017: 2732-2741 - 2016
- [i3]Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei:
Edward: A library for probabilistic modeling, inference, and criticism. CoRR abs/1610.09787 (2016) - [i2]Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
The $χ$-Divergence for Approximate Inference. CoRR abs/1611.00328 (2016) - [i1]Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley:
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. CoRR abs/1611.01702 (2016)
Coauthor Index
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last updated on 2025-05-03 01:21 CEST by the dblp team
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