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ORCIDDavid Alvarez-Melis
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2020 – today
- 2024
[c22]Junhong Shen, Neil A. Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolò Fusi:
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains. ICML 2024- [c21]Tian Qin, Zhiwei Deng, David Alvarez-Melis:
A Label is Worth A Thousand Images in Dataset Distillation. NeurIPS 2024 - [i39]Junhong Shen, Neil A. Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolò Fusi:
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains. CoRR abs/2402.05140 (2024) - [i38]Tian Qin, Zhiwei Deng, David Alvarez-Melis:
Distributional Dataset Distillation with Subtask Decomposition. CoRR abs/2403.00999 (2024) - [i37]Sara Kangaslahti, David Alvarez-Melis:
Continuous Language Model Interpolation for Dynamic and Controllable Text Generation. CoRR abs/2404.07117 (2024) - [i36]Tian Qin, Zhiwei Deng, David Alvarez-Melis:
A Label is Worth a Thousand Images in Dataset Distillation. CoRR abs/2406.10485 (2024) - [i35]Kevin Gu, Eva Tuecke, Dmitriy Katz, Raya Horesh, David Alvarez-Melis, Mikhail Yurochkin:
CharED: Character-wise Ensemble Decoding for Large Language Models. CoRR abs/2407.11009 (2024) - [i34]Athina Sotiropoulou, David Alvarez-Melis:
Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces. CoRR abs/2407.14957 (2024) - [i33]Alex Rojas, David Alvarez-Melis:
Understanding the Role of Functional Diversity in Weight-Ensembling with Ingredient Selection and Multidimensional Scaling. CoRR abs/2409.02347 (2024) - [i32]Paula Rodriguez Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe:
What is the Right Notion of Distance between Predict-then-Optimize Tasks? CoRR abs/2409.06997 (2024) - [i31]Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu, Nikhil Vyas, Nikhil Anand, David Alvarez-Melis, Yuanzhi Li, Sham M. Kakade, Eran Malach:
Mixture of Parrots: Experts improve memorization more than reasoning. CoRR abs/2410.19034 (2024) - [i30]Zhili Feng, Tanya Marwah, Nicolò Fusi, David Alvarez-Melis, Lester Mackey:
Adapting Language Models via Token Translation. CoRR abs/2411.00593 (2024) - [i29]Tian Qin, Naomi Saphra, David Alvarez-Melis:
Sometimes I am a Tree: Data Drives Unstable Hierarchical Generalization. CoRR abs/2412.04619 (2024) - 2023
- [c20]Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. ICML 2023: 6228-6242 - [c19]Kianoush Falahkheirkhah, Alex Xijie Lu, David Alvarez-Melis, Grace Huynh:
Domain adaptation using optimal transport for invariant learning using histopathology datasets. MIDL 2023: 1765-1782 - [c18]Jiaojiao Fan, David Alvarez-Melis:
Generating Synthetic Datasets by Interpolating along Generalized Geodesics. UAI 2023: 571-581 - [i28]Kianoush Falahkheirkhah, Alex Xijie Lu, David Alvarez-Melis, Grace Huynh:
Domain adaptation using optimal transport for invariant learning using histopathology datasets. CoRR abs/2303.02241 (2023) - [i27]Jiaojiao Fan, David Alvarez-Melis:
Generating Synthetic Datasets by Interpolating along Generalized Geodesics. CoRR abs/2306.06866 (2023) - 2022
- [j2]David Alvarez-Melis, Yair Schiff, Youssef Mroueh:
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks. Trans. Mach. Learn. Res. 2022 (2022) - [c17]Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh:
Hierarchical Optimal Transport for Comparing Histopathology Datasets. MIDL 2022: 1459-1469 - [c16]David Alvarez-Melis, Vikas Garg, Adam Kalai:
Are GANs overkill for NLP? NeurIPS 2022 - [i26]Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh:
Hierarchical Optimal Transport for Comparing Histopathology Datasets. CoRR abs/2204.08324 (2022) - [i25]David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai:
Why GANs are overkill for NLP. CoRR abs/2205.09838 (2022) - [i24]Neha Hulkund, Nicolò Fusi, Jennifer Wortman Vaughan, David Alvarez-Melis:
Interpretable Distribution Shift Detection using Optimal Transport. CoRR abs/2208.02896 (2022) - [i23]Frederike Lübeck, Charlotte Bunne, Gabriele Gut, Jacobo Sarabia del Castillo, Lucas Pelkmans, David Alvarez-Melis:
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings. CoRR abs/2209.15621 (2022) - [i22]Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. CoRR abs/2210.03164 (2022) - [i21]David Alvarez-Melis, Nicolò Fusi, Lester Mackey
, Tal Wagner:
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport. CoRR abs/2210.13630 (2022) - [i20]Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano:
Transfer RL via the Undo Maps Formalism. CoRR abs/2211.14469 (2022) - 2021
- [c15]David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan:
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence. HCOMP 2021: 35-47 - [c14]David Alvarez-Melis, Nicolò Fusi:
Dataset Dynamics via Gradient Flows in Probability Space. ICML 2021: 219-230 - [i19]David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan:
A Human-Centered Interpretability Framework Based on Weight of Evidence. CoRR abs/2104.13299 (2021) - [i18]David Alvarez-Melis, Yair Schiff, Youssef Mroueh:
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks. CoRR abs/2106.00774 (2021) - 2020
- [c13]David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. AISTATS 2020: 1606-1617 - [c12]David Alvarez-Melis, Nicolò Fusi:
Geometric Dataset Distances via Optimal Transport. NeurIPS 2020 - [i17]David Alvarez-Melis, Nicoló Fusi:
Geometric Dataset Distances via Optimal Transport. CoRR abs/2002.02923 (2020) - [i16]David Alvarez-Melis, Nicolò Fusi:
Gradient Flows in Dataset Space. CoRR abs/2010.12760 (2020)
2010 – 2019
- 2019
- [c11]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. AISTATS 2019: 1870-1879 - [c10]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust, Locally Linear Deep Networks. ICLR (Poster) 2019 - [c9]Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. ICML 2019: 851-861 - [c8]Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML 2019: 3723-3733 - [i15]Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. CoRR abs/1902.09737 (2019) - [i14]Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. CoRR abs/1905.05461 (2019) - [i13]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust, Locally Linear Deep Networks. CoRR abs/1907.03207 (2019) - [i12]David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna M. Wallach:
Weight of Evidence as a Basis for Human-Oriented Explanations. CoRR abs/1910.13503 (2019) - [i11]Hailey James-Sorenson, David Alvarez-Melis:
Probabilistic Bias Mitigation in Word Embeddings. CoRR abs/1910.14497 (2019) - [i10]David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. CoRR abs/1911.02536 (2019) - 2018
- [c7]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. AISTATS 2018: 1771-1780 - [c6]David Alvarez-Melis, Tommi S. Jaakkola:
Gromov-Wasserstein Alignment of Word Embedding Spaces. EMNLP 2018: 1881-1890 - [c5]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. ICLR (Workshop) 2018 - [c4]David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. NeurIPS 2018: 7786-7795 - [i9]David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. CoRR abs/1806.07538 (2018) - [i8]David Alvarez-Melis, Tommi S. Jaakkola:
On the Robustness of Interpretability Methods. CoRR abs/1806.08049 (2018) - [i7]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. CoRR abs/1806.09277 (2018) - [i6]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Game-Theoretic Interpretability for Temporal Modeling. CoRR abs/1807.00130 (2018) - [i5]David Alvarez-Melis, Tommi S. Jaakkola:
Gromov-Wasserstein Alignment of Word Embedding Spaces. CoRR abs/1809.00013 (2018) - 2017
- [c3]David Alvarez-Melis, Tommi S. Jaakkola:
A causal framework for explaining the predictions of black-box sequence-to-sequence models. EMNLP 2017: 412-421 - [c2]David Alvarez-Melis, Tommi S. Jaakkola:
Tree-structured decoding with doubly-recurrent neural networks. ICLR (Poster) 2017 - [i4]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. CoRR abs/1706.09549 (2017) - [i3]David Alvarez-Melis, Tommi S. Jaakkola:
A causal framework for explaining the predictions of black-box sequence-to-sequence models. CoRR abs/1707.01943 (2017) - [i2]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. CoRR abs/1712.06199 (2017) - 2016
- [j1]Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola:
Word Embeddings as Metric Recovery in Semantic Spaces. Trans. Assoc. Comput. Linguistics 4: 273-286 (2016) - [c1]David Alvarez-Melis, Martin Saveski:
Topic Modeling in Twitter: Aggregating Tweets by Conversations. ICWSM 2016: 519-522 - 2015
- [i1]Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola:
Word, graph and manifold embedding from Markov processes. CoRR abs/1509.05808 (2015)
Coauthor Index
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