David Alvarez-Melis

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 56

Top Topics

Text Generation

Synthetic Datasets

Embedding Space

Top Venues

Publications

Kevin Gu, Eva Tuecke, Dmitriy Katz, Raya Horesh, David Alvarez-Melis, Mikhail Yurochkin
CharED: Character-wise Ensemble Decoding for Large Language Models. CoRR (2024)
Athina Sotiropoulou, David Alvarez-Melis
Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces. CoRR (2024)
Tian Qin, Zhiwei Deng, David Alvarez-Melis
A Label is Worth a Thousand Images in Dataset Distillation. CoRR (2024)
Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolò Fusi
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains. CoRR (2024)
Tian Qin, Zhiwei Deng, David Alvarez-Melis
Distributional Dataset Distillation with Subtask Decomposition. CoRR (2024)
Sara Kangaslahti, David Alvarez-Melis
Continuous Language Model Interpolation for Dynamic and Controllable Text Generation. CoRR (2024)
Kianoush Falahkheirkhah, Alex Xijie Lu, David Alvarez-Melis, Grace Huynh
Domain adaptation using optimal transport for invariant learning using histopathology datasets. MIDL (2023)
Jiaojiao Fan, David Alvarez-Melis
Generating Synthetic Datasets by Interpolating along Generalized Geodesics. UAI (2023)
Jiaojiao Fan, David Alvarez-Melis
Generating Synthetic Datasets by Interpolating along Generalized Geodesics. CoRR (2023)
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
InfoOT: Information Maximizing Optimal Transport. ICML (2023)
Kianoush Falahkheirkhah, Alex Lu, David Alvarez-Melis, Grace Huynh
Domain adaptation using optimal transport for invariant learning using histopathology datasets. CoRR (2023)
Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh
Hierarchical Optimal Transport for Comparing Histopathology Datasets. CoRR (2022)
David Alvarez-Melis, Nicolò Fusi, Lester Mackey, Tal Wagner
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport. CoRR (2022)
Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano
Transfer RL via the Undo Maps Formalism. CoRR (2022)
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)
David Alvarez-Melis, Vikas K. Garg, Adam Kalai
Are GANs overkill for NLP? NeurIPS (2022)
Neha Hulkund, Nicolò Fusi, Jennifer Wortman Vaughan, David Alvarez-Melis
Interpretable Distribution Shift Detection using Optimal Transport. CoRR (2022)
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 (2022)
David Alvarez-Melis, Vikas K. Garg, Adam Tauman Kalai
Why GANs are overkill for NLP. CoRR (2022)
Anna Yeaton, Rahul G. Krishnan, Rebecca J. Mieloszyk, David Alvarez-Melis, Grace Huynh
Hierarchical Optimal Transport for Comparing Histopathology Datasets. MIDL (2022)
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
InfoOT: Information Maximizing Optimal Transport. CoRR (2022)
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)
David Alvarez-Melis, Yair Schiff, Youssef Mroueh
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks. CoRR (2021)
David Alvarez-Melis, Nicolò Fusi
Dataset Dynamics via Gradient Flows in Probability Space. ICML (2021)
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 (2021)
David Alvarez-Melis, Nicolò Fusi
Geometric Dataset Distances via Optimal Transport. NeurIPS (2020)
David Alvarez-Melis, Nicoló Fusi
Geometric Dataset Distances via Optimal Transport. CoRR (2020)
David Alvarez-Melis, Nicolò Fusi
Gradient Flows in Dataset Space. CoRR (2020)
David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. AISTATS (2020)
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka
Learning Generative Models across Incomparable Spaces. ICML (2019)
Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
Functional Transparency for Structured Data: a Game-Theoretic Approach. CoRR (2019)
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Towards Optimal Transport with Global Invariances. AISTATS (2019)
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Towards Robust, Locally Linear Deep Networks. ICLR (Poster) (2019)
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna M. Wallach
Weight of Evidence as a Basis for Human-Oriented Explanations. CoRR (2019)
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Towards Robust, Locally Linear Deep Networks. CoRR (2019)
Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML (2019)
David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. CoRR (2019)
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka
Learning Generative Models across Incomparable Spaces. CoRR (2019)
Hailey James-Sorenson, David Alvarez-Melis
Probabilistic Bias Mitigation in Word Embeddings. CoRR (2019)
David Alvarez-Melis, Tommi S. Jaakkola
On the Robustness of Interpretability Methods. CoRR (2018)
Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra
Distributional Adversarial Networks. ICLR (Workshop) (2018)
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Towards Optimal Transport with Global Invariances. CoRR (2018)
David Alvarez-Melis, Tommi S. Jaakkola
Gromov-Wasserstein Alignment of Word Embedding Spaces. CoRR (2018)
David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
Structured Optimal Transport. AISTATS (2018)
David Alvarez-Melis, Tommi S. Jaakkola
Gromov-Wasserstein Alignment of Word Embedding Spaces. EMNLP (2018)
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Game-Theoretic Interpretability for Temporal Modeling. CoRR (2018)
David Alvarez-Melis, Tommi S. Jaakkola
Towards Robust Interpretability with Self-Explaining Neural Networks. CoRR (2018)
David Alvarez-Melis, Tommi S. Jaakkola
Towards Robust Interpretability with Self-Explaining Neural Networks. NeurIPS (2018)
Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra
Distributional Adversarial Networks. CoRR (2017)
David Alvarez-Melis, Tommi S. Jaakkola
A causal framework for explaining the predictions of black-box sequence-to-sequence models. CoRR (2017)
David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
Structured Optimal Transport. CoRR (2017)
David Alvarez-Melis, Tommi S. Jaakkola
A causal framework for explaining the predictions of black-box sequence-to-sequence models. EMNLP (2017)
David Alvarez-Melis, Tommi S. Jaakkola
Tree-structured decoding with doubly-recurrent neural networks. ICLR (Poster) (2017)
David Alvarez-Melis, Martin Saveski
Topic Modeling in Twitter: Aggregating Tweets by Conversations. ICWSM (2016)
Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola
Word Embeddings as Metric Recovery in Semantic Spaces. Trans. Assoc. Comput. Linguistics 4 (2016)
Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola
Word, graph and manifold embedding from Markov processes. CoRR (2015)