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ORCIDTommi S. Jaakkola
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- affiliation: MIT, Computer Science and Artificial Intelligence Laboratory
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2020 – today
- 2023
[i107]Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola:
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. CoRR abs/2302.00670 (2023)- [i106]Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. CoRR abs/2302.02277 (2023) - [i105]Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola:
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR abs/2302.04265 (2023) - 2022
- [c147]Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola:
Subspace Diffusion Generative Models. ECCV (23) 2022: 274-289 - [c146]Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. ICLR 2022 - [c145]Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola:
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. ICLR 2022 - [c144]Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Adversarial Support Alignment. ICLR 2022 - [c143]Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola:
Crystal Diffusion Variational Autoencoder for Periodic Material Generation. ICLR 2022 - [c142]Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola:
Controlling Directions Orthogonal to a Classifier. ICLR 2022 - [c141]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Conformal Prediction Sets with Limited False Positives. ICML 2022: 6514-6532 - [c140]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. ICML 2022: 10217-10227 - [c139]Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola:
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. ICML 2022: 20503-20521 - [i104]Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola:
Controlling Directions Orthogonal to a Classifier. CoRR abs/2201.11259 (2022) - [i103]Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, Regina Barzilay:
Syfer: Neural Obfuscation for Private Data Release. CoRR abs/2201.12406 (2022) - [i102]Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola:
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. CoRR abs/2202.05146 (2022) - [i101]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Conformal Prediction Sets with Limited False Positives. CoRR abs/2202.07650 (2022) - [i100]Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Adversarial Support Alignment. CoRR abs/2203.08908 (2022) - [i99]Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola:
Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning. CoRR abs/2204.10348 (2022) - [i98]Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola:
Subspace Diffusion Generative Models. CoRR abs/2205.01490 (2022) - [i97]Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola:
Torsional Diffusion for Molecular Conformer Generation. CoRR abs/2206.01729 (2022) - [i96]Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola:
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. CoRR abs/2206.04119 (2022) - [i95]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement. CoRR abs/2207.06616 (2022) - [i94]Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Calibrated Selective Classification. CoRR abs/2208.12084 (2022) - [i93]Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola:
Poisson Flow Generative Models. CoRR abs/2209.11178 (2022) - [i92]Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola:
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. CoRR abs/2210.01776 (2022) - [i91]Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli
, Tommi S. Jaakkola:
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. CoRR abs/2210.07237 (2022) - [i90]Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola:
Efficiently Controlling Multiple Risks with Pareto Testing. CoRR abs/2210.07913 (2022) - [i89]Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal:
Is Conditional Generative Modeling all you need for Decision-Making? CoRR abs/2211.15657 (2022) - 2021
- [j30]Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay:
Deep learning identifies synergistic drug combinations for treating COVID-19. Proc. Natl. Acad. Sci. USA 118(39): e2105070118 (2021) - [c138]Karren D. Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler:
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis. CVPR 2021: 6688-6698 - [c137]Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Consistent Accelerated Inference via Confident Adaptive Transformers. EMNLP (1) 2021: 4962-4979 - [c136]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission. ICLR 2021 - [c135]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Few-Shot Conformal Prediction with Auxiliary Tasks. ICML 2021: 3329-3339 - [c134]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. ICML 2021: 3480-3491 - [c133]Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov:
Information Obfuscation of Graph Neural Networks. ICML 2021: 6600-6610 - [c132]Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Understanding Interlocking Dynamics of Cooperative Rationalization. NeurIPS 2021: 12822-12835 - [c131]Octavian Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola:
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. NeurIPS 2021: 13757-13769 - [i88]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Few-shot Conformal Prediction with Auxiliary Tasks. CoRR abs/2102.08898 (2021) - [i87]Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Consistent Accelerated Inference via Confident Adaptive Transformers. CoRR abs/2104.08803 (2021) - [i86]Adam Yala, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Manya Ghobadi, Tommi S. Jaakkola, Vinod Vaikuntanathan, Regina Barzilay, Muriel Médard:
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training. CoRR abs/2106.02484 (2021) - [i85]Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola:
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. CoRR abs/2106.07802 (2021) - [i84]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. CoRR abs/2106.15612 (2021) - [i83]Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola:
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. CoRR abs/2110.04624 (2021) - [i82]Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola:
Crystal Diffusion Variational Autoencoder for Periodic Material Generation. CoRR abs/2110.06197 (2021) - [i81]Yilun Xu, Tommi S. Jaakkola:
Learning Representations that Support Robust Transfer of Predictors. CoRR abs/2110.09940 (2021) - [i80]Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Understanding Interlocking Dynamics of Cooperative Rationalization. CoRR abs/2110.13880 (2021) - [i79]Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola:
Fragment-based Sequential Translation for Molecular Optimization. CoRR abs/2111.01009 (2021) - [i78]Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. CoRR abs/2111.07786 (2021) - 2020
- [c130]David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. AISTATS 2020: 1606-1617 - [c129]Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola:
Blank Language Models. EMNLP (1) 2020: 5186-5198 - [c128]Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola:
Self-Supervised Learning of Appliance Usage. ICLR 2020 - [c127]Guang-He Lee, Tommi S. Jaakkola:
Oblique Decision Trees from Derivatives of ReLU Networks. ICLR 2020 - [c126]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
Invariant Rationalization. ICML 2020: 1448-1458 - [c125]Vikas K. Garg, Tommi S. Jaakkola:
Predicting deliberative outcomes. ICML 2020: 3408-3418 - [c124]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. ICML 2020: 3419-3430 - [c123]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Hierarchical Generation of Molecular Graphs using Structural Motifs. ICML 2020: 4839-4848 - [c122]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Multi-Objective Molecule Generation using Interpretable Substructures. ICML 2020: 4849-4859 - [c121]Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Educating Text Autoencoders: Latent Representation Guidance via Denoising. ICML 2020: 8719-8729 - [c120]Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola:
Improving Molecular Design by Stochastic Iterative Target Augmentation. ICML 2020: 10716-10726 - [i77]Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola:
Blank Language Models. CoRR abs/2002.03079 (2020) - [i76]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Hierarchical Generation of Molecular Graphs using Structural Motifs. CoRR abs/2002.03230 (2020) - [i75]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Composing Molecules with Multiple Property Constraints. CoRR abs/2002.03244 (2020) - [i74]Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola:
Improving Molecular Design by Stochastic Iterative Target Augmentation. CoRR abs/2002.04720 (2020) - [i73]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. CoRR abs/2002.06157 (2020) - [i72]Shangyuan Tong, Timur Garipov, Tommi S. Jaakkola:
The Benefits of Pairwise Discriminators for Adversarial Training. CoRR abs/2002.08621 (2020) - [i71]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
Invariant Rationalization. CoRR abs/2003.09772 (2020) - [i70]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Adaptive Invariance for Molecule Property Prediction. CoRR abs/2005.03004 (2020) - [i69]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Domain Extrapolation via Regret Minimization. CoRR abs/2006.03908 (2020) - [i68]Gary Bécigneul, Octavian-Eugen Ganea, Benson Chen, Regina Barzilay, Tommi S. Jaakkola:
Optimal Transport Graph Neural Networks. CoRR abs/2006.04804 (2020) - [i67]Karren D. Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler:
Improved Conditional Flow Models for Molecule to Image Synthesis. CoRR abs/2006.08532 (2020) - [i66]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels. CoRR abs/2007.03114 (2020) - [i65]Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov:
Graph Adversarial Networks: Protecting Information against Adversarial Attacks. CoRR abs/2009.13504 (2020) - [i64]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models. CoRR abs/2011.04651 (2020) - [i63]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR abs/2012.10713 (2020)
2010 – 2019
- 2019
- [j29]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 59(8): 3370-3388 (2019) - [j28]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Correction to Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 59(12): 5304-5305 (2019) - [j27]Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi S. Jaakkola:
High Dimensional Inference With Random Maximum A-Posteriori Perturbations. IEEE Trans. Inf. Theory 65(10): 6539-6560 (2019) - [c119]Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi:
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling. AAAI 2019: 766-773 - [c118]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. AISTATS 2019: 1870-1879 - [c117]Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola:
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. EMNLP/IJCNLP (1) 2019: 4092-4101 - [c116]John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. DGS@ICLR 2019 - [c115]Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola:
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. ICLR (Poster) 2019 - [c114]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust, Locally Linear Deep Networks. ICLR (Poster) 2019 - [c113]Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML 2019: 3723-3733 - [c112]Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola:
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. NeurIPS 2019: 4911-4922 - [c111]Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan:
Direct Optimization through arg max for Discrete Variational Auto-Encoder. NeurIPS 2019: 6200-6211 - [c110]Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. NeurIPS 2019: 8012-8023 - [c109]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
A Game Theoretic Approach to Class-wise Selective Rationalization. NeurIPS 2019: 10055-10065 - [c108]John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. NeurIPS 2019: 15794-15805 - [i62]Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi:
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling. CoRR abs/1902.02037 (2019) - [i61]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) - [i60]Paresh Malalur, Tommi S. Jaakkola:
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition. CoRR abs/1903.06538 (2019) - [i59]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Are Learned Molecular Representations Ready For Prime Time? CoRR abs/1904.01561 (2019) - [i58]Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. CoRR abs/1905.12158 (2019) - [i57]Vikas K. Garg, Tommi S. Jaakkola:
Strategic Prediction with Latent Aggregative Games. CoRR abs/1905.12169 (2019) - [i56]Benson Chen, Regina Barzilay, Tommi S. Jaakkola:
Path-Augmented Graph Transformer Network. CoRR abs/1905.12712 (2019) - [i55]Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Latent Space Secrets of Denoising Text-Autoencoders. CoRR abs/1905.12777 (2019) - [i54]Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola:
A Stratified Approach to Robustness for Randomly Smoothed Classifiers. CoRR abs/1906.04948 (2019) - [i53]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust, Locally Linear Deep Networks. CoRR abs/1907.03207 (2019) - [i52]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules. CoRR abs/1907.11223 (2019) - [i51]Guang-He Lee, Tommi S. Jaakkola:
Locally Constant Networks. CoRR abs/1909.13488 (2019) - [i50]Benson Chen, Tianxiao Shen, Tommi S. Jaakkola, Regina Barzilay:
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis. CoRR abs/1910.09688 (2019) - [i49]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
A Game Theoretic Approach to Class-wise Selective Rationalization. CoRR abs/1910.12853 (2019) - [i48]Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola:
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. CoRR abs/1910.13294 (2019) - [i47]David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. CoRR abs/1911.02536 (2019) - 2018
- [j26]Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola:
Grounding Language for Transfer in Deep Reinforcement Learning. J. Artif. Intell. Res. 63: 849-874 (2018) - [c107]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. AISTATS 2018: 1771-1780 - [c106]David Alvarez-Melis, Tommi S. Jaakkola:
Gromov-Wasserstein Alignment of Word Embedding Spaces. EMNLP 2018: 1881-1890 - [c105]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Junction Tree Variational Autoencoder for Molecular Graph Generation. ICML 2018: 2328-2337 - [c104]David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. NeurIPS 2018: 7786-7795 - [c103]Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum:
The Variational Homoencoder: Learning to learn high capacity generative models from few examples. UAI 2018: 988-997 - [i46]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Junction Tree Variational Autoencoder for Molecular Graph Generation. CoRR abs/1802.04364 (2018) - [i45]Guy Lorberbom, Andreea Gane, Tommi S. Jaakkola, Tamir Hazan:
Direct Optimization through arg max for Discrete Variational Auto-Encoder. CoRR abs/1806.02867 (2018) - [i44]David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. CoRR abs/1806.07538 (2018) - [i43]David Alvarez-Melis, Tommi S. Jaakkola:
On the Robustness of Interpretability Methods. CoRR abs/1806.08049 (2018) - [i42]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. CoRR abs/1806.09277 (2018) - [i41]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Game-Theoretic Interpretability for Temporal Modeling. CoRR abs/1807.00130 (2018) - [i40]Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum:
The Variational Homoencoder: Learning to learn high capacity generative models from few examples. CoRR abs/1807.08919 (2018) - [i39]David Alvarez-Melis, Tommi S. Jaakkola:
Gromov-Wasserstein Alignment of Word Embedding Spaces. CoRR abs/1809.00013 (2018) - [i38]Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola:
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. CoRR abs/1812.01070 (2018) - 2017
- [j25]Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola:
Aspect-augmented Adversarial Networks for Domain Adaptation. Trans. Assoc. Comput. Linguistics 5: 515-528 (2017) - [c102]Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. AISTATS 2017: 1039-1047 - [c101]David Alvarez-Melis, Tommi S. Jaakkola:
A causal framework for explaining the predictions of black-box sequence-to-sequence models. EMNLP 2017: 412-421 - [c100]