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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
- 2024
[j37]Ryotaro Okabe
, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan C. Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li:
Virtual node graph neural network for full phonon prediction. Nat. Comput. Sci. 4(7): 522-531 (2024)- [j36]Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola:
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024) - [c175]Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang:
Correcting Diffusion Generation Through Resampling. CVPR 2024: 8713-8723 - [c174]Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang:
Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective. EMNLP 2024: 8708-8731 - [c173]Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith:
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. ICLR 2024 - [c172]Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola:
Deep Confident Steps to New Pockets: Strategies for Docking Generalization. ICLR 2024 - [c171]Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola:
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. ICLR 2024 - [c170]Bowen Jing, Tommi S. Jaakkola, Bonnie Berger:
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms. ICLR 2024 - [c169]Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi S. Jaakkola, Regina Barzilay, Ila R. Fiete:
Improving protein optimization with smoothed fitness landscapes. ICLR 2024 - [c168]Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay:
Conformal Language Modeling. ICLR 2024 - [c167]Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola:
Removing Biases from Molecular Representations via Information Maximization. ICLR 2024 - [c166]Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola:
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. ICML 2024 - [c165]Bowen Jing, Bonnie Berger, Tommi S. Jaakkola:
AlphaFold Meets Flow Matching for Generating Protein Ensembles. ICML 2024 - [c164]Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola:
Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design. ICML 2024 - [c163]Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola:
Dirichlet Flow Matching with Applications to DNA Sequence Design. ICML 2024 - [c162]Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis:
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents. ICML 2024 - [i147]Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola:
Improved motif-scaffolding with SE(3) flow matching. CoRR abs/2401.04082 (2024) - [i146]Menghua Wu, Yujia Bao, Regina Barzilay, Tommi S. Jaakkola:
Sample, estimate, aggregate: A recipe for causal discovery foundation models. CoRR abs/2402.01929 (2024) - [i145]Bowen Jing, Bonnie Berger, Tommi S. Jaakkola:
AlphaFold Meets Flow Matching for Generating Protein Ensembles. CoRR abs/2402.04845 (2024) - [i144]Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola:
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. CoRR abs/2402.04997 (2024) - [i143]Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola:
Dirichlet Flow Matching with Applications to DNA Sequence Design. CoRR abs/2402.05841 (2024) - [i142]Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola:
Deep Confident Steps to New Pockets: Strategies for Docking Generalization. CoRR abs/2402.18396 (2024) - [i141]Ezra Erives, Bowen Jing, Tommi S. Jaakkola:
Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models. CoRR abs/2405.02805 (2024) - [i140]Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka:
In-Context Symmetries: Self-Supervised Learning through Contextual World Models. CoRR abs/2405.18193 (2024) - [i139]Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola:
A Recipe for Charge Density Prediction. CoRR abs/2405.19276 (2024) - [i138]Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis:
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents. CoRR abs/2407.03300 (2024) - [i137]Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, Mingda Li:
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates. CoRR abs/2407.04557 (2024) - [i136]Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang:
Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective. CoRR abs/2407.16997 (2024) - [i135]Bowen Jing, Hannes Stärk, Tommi S. Jaakkola, Bonnie Berger:
Generative Modeling of Molecular Dynamics Trajectories. CoRR abs/2409.17808 (2024) - [i134]Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi S. Jaakkola:
Predicting perturbation targets with causal differential networks. CoRR abs/2410.03380 (2024) - [i133]Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi S. Jaakkola, Rafael Gómez-Bombarelli:
Think While You Generate: Discrete Diffusion with Planned Denoising. CoRR abs/2410.06264 (2024) - [i132]Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev:
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. CoRR abs/2410.13643 (2024) - [i131]Yujian Liu, Shiyu Chang, Tommi S. Jaakkola, Yang Zhang:
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning. CoRR abs/2410.19290 (2024) - [i130]Peter Holderrieth, Yilun Xu, Tommi S. Jaakkola:
Hamiltonian Score Matching and Generative Flows. CoRR abs/2410.20470 (2024) - [i129]Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt:
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. CoRR abs/2410.20516 (2024) - [i128]Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman:
Generator Matching: Generative modeling with arbitrary Markov processes. CoRR abs/2410.20587 (2024) - [i127]Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler:
An Information Criterion for Controlled Disentanglement of Multimodal Data. CoRR abs/2410.23996 (2024) - 2023
- [j35]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. Trans. Mach. Learn. Res. 2023 (2023) - [j34]Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola:
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c161]Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal:
Is Conditional Generative Modeling all you need for Decision Making? ICLR 2023 - [c160]Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola:
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. ICLR 2023 - [c159]Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola:
Efficiently Controlling Multiple Risks with Pareto Testing. ICLR 2023 - [c158]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. ICLR 2023 - [c157]Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola:
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. ICLR 2023 - [c156]Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola:
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML 2023: 38566-38591 - [c155]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. ICML 2023: 40001-40039 - [c154]Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang:
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. ICML 2023: 41164-41193 - [c153]Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal:
Compositional Foundation Models for Hierarchical Planning. NeurIPS 2023 - [c152]Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola:
Compositional Sculpting of Iterative Generative Processes. NeurIPS 2023 - [c151]Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola:
Restart Sampling for Improving Generative Processes. NeurIPS 2023 - [i126]Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola:
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. CoRR abs/2302.00670 (2023) - [i125]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) - [i124]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) - [i123]Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira, Andrea J. D. Jaba, Victor Quach, Ken R. Duffy, Tommi S. Jaakkola, Vinod Vaikuntanathan, Manya Ghobadi, Regina Barzilay, Muriel Médard:
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels. CoRR abs/2304.00047 (2023) - [i122]Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi S. Jaakkola:
EigenFold: Generative Protein Structure Prediction with Diffusion Models. CoRR abs/2304.02198 (2023) - [i121]Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark:
GenPhys: From Physical Processes to Generative Models. CoRR abs/2304.02637 (2023) - [i120]Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang:
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. CoRR abs/2304.03322 (2023) - [i119]Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stärk, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola:
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models. CoRR abs/2304.03889 (2023) - [i118]Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay:
Conformal Language Modeling. CoRR abs/2306.10193 (2023) - [i117]Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola:
Restart Sampling for Improving Generative Processes. CoRR abs/2306.14878 (2023) - [i116]Andrew Kirjner, Jason Yim, Raman Samusevich, Tommi S. Jaakkola, Regina Barzilay, Ila Fiete:
Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing. CoRR abs/2307.00494 (2023) - [i115]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i114]Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Josh Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal:
Compositional Foundation Models for Hierarchical Planning. CoRR abs/2309.08587 (2023) - [i113]Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola:
Compositional Sculpting of Iterative Generative Processes. CoRR abs/2309.16115 (2023) - [i112]Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola:
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design. CoRR abs/2310.05764 (2023) - [i111]Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith:
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. CoRR abs/2310.10732 (2023) - [i110]Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola:
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. CoRR abs/2310.13102 (2023) - [i109]Xiang Fu, Albert Musaelian, Anders Johansson, Tommi S. Jaakkola, Boris Kozinsky:
Learning Interatomic Potentials at Multiple Scales. CoRR abs/2310.13756 (2023) - [i108]Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola:
Removing Biases from Molecular Representations via Information Maximization. CoRR abs/2312.00718 (2023) - [i107]Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola:
Risk-Controlling Model Selection via Guided Bayesian Optimization. CoRR abs/2312.01692 (2023) - [i106]Bowen Jing, Tommi S. Jaakkola, Bonnie Berger:
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms. CoRR abs/2312.04323 (2023) - [i105]Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang:
Correcting Diffusion Generation through Resampling. CoRR abs/2312.06038 (2023) - 2022
- [j33]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23: 340:1-340:49 (2022) - [j32]Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Calibrated Selective Classification. Trans. Mach. Learn. Res. 2022 (2022) - [c150]Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola:
Subspace Diffusion Generative Models. ECCV (23) 2022: 274-289 - [c149]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 - [c148]Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola:
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. ICLR 2022 - [c147]Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Adversarial Support Alignment. ICLR 2022 - [c146]Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola:
Crystal Diffusion Variational Autoencoder for Periodic Material Generation. ICLR 2022 - [c145]Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola:
Controlling Directions Orthogonal to a Classifier. ICLR 2022 - [c144]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Conformal Prediction Sets with Limited False Positives. ICML 2022: 6514-6532 - [c143]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. ICML 2022: 10217-10227 - [c142]Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola:
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. ICML 2022: 20503-20521 - [c141]Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola:
Torsional Diffusion for Molecular Conformer Generation. NeurIPS 2022 - [c140]Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola:
Poisson Flow Generative Models. NeurIPS 2022 - [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
- [j31]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) - [c139]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 - [c138]Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Consistent Accelerated Inference via Confident Adaptive Transformers. EMNLP (1) 2021: 4962-4979 - [c137]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission. ICLR 2021 - [c136]Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Few-Shot Conformal Prediction with Auxiliary Tasks. ICML 2021: 3329-3339 - [c135]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. ICML 2021: 3480-3491 - [c134]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 - [c133]Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola:
Understanding Interlocking Dynamics of Cooperative Rationalization. NeurIPS 2021: 12822-12835 - [c132]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
- [c131]David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola:
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. AISTATS 2020: 1606-1617 - [c130]Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola:
Blank Language Models. EMNLP (1) 2020: 5186-5198 - [c129]Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola:
Self-Supervised Learning of Appliance Usage. ICLR 2020 - [c128]Guang-He Lee, Tommi S. Jaakkola:
Oblique Decision Trees from Derivatives of ReLU Networks. ICLR 2020 - [c127]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
Invariant Rationalization. ICML 2020: 1448-1458 - [c126]Vikas K. Garg, Tommi S. Jaakkola:
Predicting deliberative outcomes. ICML 2020: 3408-3418 - [c125]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. ICML 2020: 3419-3430 - [c124]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Hierarchical Generation of Molecular Graphs using Structural Motifs. ICML 2020: 4839-4848 - [c123]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Multi-Objective Molecule Generation using Interpretable Substructures. ICML 2020: 4849-4859 - [c122]Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Educating Text Autoencoders: Latent Representation Guidance via Denoising. ICML 2020: 8719-8729 - [c121]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
- [j30]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) - [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:
Correction to Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 59(12): 5304-5305 (2019) - [j28]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) - [c120]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 - [c119]David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. AISTATS 2019: 1870-1879 - [c118]Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola:
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. EMNLP/IJCNLP (1) 2019: 4092-4101 - [c117]John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. DGS@ICLR 2019 - [c116]Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola:
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. ICLR (Poster) 2019 - [c115]Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust, Locally Linear Deep Networks. ICLR (Poster) 2019 - [c114]Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML 2019: 3723-3733 - [c113]Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola:
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. NeurIPS 2019: 4911-4922 - [c112]Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan:
Direct Optimization through arg max for Discrete Variational Auto-Encoder. NeurIPS 2019: 6200-6211 - [c111]Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. NeurIPS 2019: 8012-8023 - [c110]Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola:
A Game Theoretic Approach to Class-wise Selective Rationalization. NeurIPS 2019: 10055-10065 - [c109]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
- [j27]Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola:
Grounding Language for Transfer in Deep Reinforcement Learning. J. Artif. Intell. Res. 63: 849-874 (2018) - [c108]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. AISTATS 2018: 1771-1780 - [c107]David Alvarez-Melis, Tommi S. Jaakkola:
Gromov-Wasserstein Alignment of Word Embedding Spaces. EMNLP 2018: 1881-1890 - [c106]Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Junction Tree Variational Autoencoder for Molecular Graph Generation. ICML 2018: 2328-2337 - [c105]David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. NeurIPS 2018: 7786-7795 - [c104]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
- [j26]Connor W. Coley, Regina Barzilay, William H. Green Jr., Tommi S. Jaakkola, Klavs F. Jensen:
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. J. Chem. Inf. Model. 57(8): 1757-1772 (2017) - [j25]Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola:
Aspect-augmented Adversarial Networks for Domain Adaptation. Trans. Assoc. Comput. Linguistics 5: 515-528 (2017) - [c103]Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. AISTATS 2017: 1039-1047 - [c102]David Alvarez-Melis, Tommi S. Jaakkola:
A causal framework for explaining the predictions of black-box sequence-to-sequence models. EMNLP 2017: 412-421 - [c101]David Alvarez-Melis, Tommi S. Jaakkola:
Tree-structured decoding with doubly-recurrent neural networks. ICLR (Poster) 2017 - [c100]Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Deriving Neural Architectures from Sequence and Graph Kernels. ICML 2017: 2024-2033 - [c99]Jonas Mueller, David K. Gifford, Tommi S. Jaakkola:
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. ICML 2017: 2536-2544 - [c98]Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi:
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. ICML 2017: 4100-4109 - [c97]Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola:
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. NIPS 2017: 2607-2616 - [c96]Vikas K. Garg, Tommi S. Jaakkola:
Local Aggregative Games. NIPS 2017: 5341-5351 - [c95]Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Style Transfer from Non-Parallel Text by Cross-Alignment. NIPS 2017: 6830-6841 - [i37]Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola:
Aspect-augmented Adversarial Networks for Domain Adaptation. CoRR abs/1701.00188 (2017) - [i36]Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Deriving Neural Architectures from Sequence and Graph Kernels. CoRR abs/1705.09037 (2017) - [i35]Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Style Transfer from Non-Parallel Text by Cross-Alignment. CoRR abs/1705.09655 (2017) - [i34]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) - [i33]Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola:
Deep Transfer in Reinforcement Learning by Language Grounding. CoRR abs/1708.00133 (2017) - [i32]Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola:
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. CoRR abs/1709.04555 (2017) - [i31]David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. CoRR abs/1712.06199 (2017) - 2016
- [j24]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) - [c94]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS 2016: 305-313 - [c93]Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Rationalizing Neural Predictions. EMNLP 2016: 107-117 - [c92]Youyang Gu, Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Learning to refine text based recommendations. EMNLP 2016: 2103-2108 - [c91]Tatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola:
Learning Population-Level Diffusions with Generative RNNs. ICML 2016: 2417-2426 - [c90]Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez:
Semi-supervised Question Retrieval with Gated Convolutions. HLT-NAACL 2016: 1279-1289 - [c89]Yuan Zhang, David Gaddy, Regina Barzilay, Tommi S. Jaakkola:
Ten Pairs to Tag - Multilingual POS Tagging via Coarse Mapping between Embeddings. HLT-NAACL 2016: 1307-1317 - [c88]Vikas K. Garg, Tommi S. Jaakkola:
Learning Tree Structured Potential Games. NIPS 2016: 1552-1560 - [c87]Jean Honorio, Tommi S. Jaakkola:
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. UAI 2016 - [i30]Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi S. Jaakkola:
High Dimensional Inference with Random Maximum A-Posteriori Perturbations. CoRR abs/1602.03571 (2016) - [i29]Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Rationalizing Neural Predictions. CoRR abs/1606.04155 (2016) - [i28]Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. CoRR abs/1606.05027 (2016) - 2015
- [j23]Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola:
An Unsupervised Method for Uncovering Morphological Chains. Trans. Assoc. Comput. Linguistics 3: 157-167 (2015) - [c86]Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
Metric recovery from directed unweighted graphs. AISTATS 2015 - [c85]Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Molding CNNs for text: non-linear, non-consecutive convolutions. EMNLP 2015: 1565-1575 - [c84]Jonas Mueller, Tommi S. Jaakkola:
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. NIPS 2015: 1702-1710 - [c83]Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
From random walks to distances on unweighted graphs. NIPS 2015: 3429-3437 - [i27]Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola:
An Unsupervised Method for Uncovering Morphological Chains. CoRR abs/1503.02335 (2015) - [i26]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. CoRR abs/1506.07609 (2015) - [i25]Jean Honorio, Tommi S. Jaakkola:
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. CoRR abs/1508.00945 (2015) - [i24]Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Molding CNNs for text: non-linear, non-consecutive convolutions. CoRR abs/1508.04112 (2015) - [i23]Tamir Hazan, Tommi S. Jaakkola:
Steps Toward Deep Kernel Methods from Infinite Neural Networks. CoRR abs/1508.05133 (2015) - [i22]Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola:
Word, graph and manifold embedding from Markov processes. CoRR abs/1509.05808 (2015) - [i21]Jonas Mueller, Tommi S. Jaakkola:
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. CoRR abs/1510.08956 (2015) - [i20]Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
From random walks to distances on unweighted graphs. CoRR abs/1511.00573 (2015) - [i19]Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez i Villodre:
Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models. CoRR abs/1512.05726 (2015) - [i18]Steve Hanneke, Tommi S. Jaakkola, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. CoRR abs/1512.08064 (2015) - 2014
- [c82]Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola, Amir Globerson:
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees. ACL (1) 2014: 197-207 - [c81]Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola:
Low-Rank Tensors for Scoring Dependency Structures. ACL (1) 2014: 1381-1391 - [c80]Andreea Gane, Tamir Hazan, Tommi S. Jaakkola:
Learning with Maximum A-Posteriori Perturbation Models. AISTATS 2014: 247-256 - [c79]Jean Honorio, Tommi S. Jaakkola:
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. AISTATS 2014: 384-392 - [c78]Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola:
Active Boundary Annotation using Random MAP Perturbations. AISTATS 2014: 604-613 - [c77]Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola:
Greed is Good if Randomized: New Inference for Dependency Parsing. EMNLP 2014: 1013-1024 - [c76]Jean Honorio, Tommi S. Jaakkola:
A Unified Framework for Consistency of Regularized Loss Minimizers. ICML 2014: 136-144 - [c75]Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola:
On Measure Concentration of Random Maximum A-Posteriori Perturbations. ICML 2014: 432-440 - [c74]Yu Xin, Tommi S. Jaakkola:
Controlling privacy in recommender systems. NIPS 2014: 2618-2626 - [i17]Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
Metric recovery from directed unweighted graphs. CoRR abs/1411.5720 (2014) - 2013
- [c73]Jean Honorio, Tommi S. Jaakkola:
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. ICML (3) 2013: 459-467 - [c72]Tamir Hazan, Subhransu Maji, Tommi S. Jaakkola:
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations. NIPS 2013: 1268-1276 - [c71]Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi S. Jaakkola:
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions. NIPS 2013: 1887-1895 - [c70]Jean Honorio, Tommi S. Jaakkola:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. UAI 2013 - [i16]Adrian Corduneanu, Tommi S. Jaakkola:
Continuation Methods for Mixing Heterogenous Sources. CoRR abs/1301.0562 (2013) - [i15]Harald Steck, Tommi S. Jaakkola:
Unsupervised Active Learning in Large Domains. CoRR abs/1301.0602 (2013) - [i14]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
A New Class of Upper Bounds on the Log Partition Function. CoRR abs/1301.0610 (2013) - [i13]Tony Jebara, Tommi S. Jaakkola:
Feature Selection and Dualities in Maximum Entropy Discrimination. CoRR abs/1301.3865 (2013) - [i12]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian Learning of Tree Belief Networks. CoRR abs/1301.3875 (2013) - [i11]Tommi S. Jaakkola, Michael I. Jordan:
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks. CoRR abs/1302.3586 (2013) - [i10]Jean Honorio, Tommi S. Jaakkola:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. CoRR abs/1309.6838 (2013) - [i9]Tamir Hazan, Subhransu Maji, Tommi S. Jaakkola:
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations. CoRR abs/1309.7598 (2013) - [i8]Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola:
On Measure Concentration of Random Maximum A-Posteriori Perturbations. CoRR abs/1310.4227 (2013) - 2012
- [j22]Tatsunori B. Hashimoto, Tommi S. Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David K. Gifford:
Lineage-based identification of cellular states and expression programs. Bioinform. 28(12): 250-257 (2012) - [j21]Teemu Roos, Petri Myllymäki, Tommi S. Jaakkola:
Special Issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). Int. J. Approx. Reason. 53(9): 1303-1304 (2012) - [c69]Tamir Hazan, Tommi S. Jaakkola:
On the Partition Function and Random Maximum A-Posteriori Perturbations. ICML 2012 - [c68]Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS 2012: 3023-3031 - [c67]Yu Xin, Tommi S. Jaakkola:
Primal-Dual methods for sparse constrained matrix completion. AISTATS 2012: 1323-1331 - [c66]J. Zico Kolter, Tommi S. Jaakkola:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. AISTATS 2012: 1472-1482 - [i7]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. CoRR abs/1206.3288 (2012) - [i6]Amir Globerson, Tommi S. Jaakkola:
Convergent Propagation Algorithms via Oriented Trees. CoRR abs/1206.5243 (2012) - [i5]Fahiem Bacchus, Tommi S. Jaakkola:
Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005). CoRR abs/1208.5159 (2012) - [i4]Adrian Corduneanu, Tommi S. Jaakkola:
On Information Regularization. CoRR abs/1212.2466 (2012) - 2011
- [i3]Tommi S. Jaakkola, Michael I. Jordan:
Variational Probabilistic Inference and the QMR-DT Network. CoRR abs/1105.5462 (2011) - 2010
- [j20]Yuchun Guo, Georgios Papachristoudis, Robert C. Altshuler, Georg K. Gerber, Tommi S. Jaakkola, David K. Gifford, Shaun Mahony:
Discovering homotypic binding events at high spatial resolution. Bioinform. 26(24): 3028-3034 (2010) - [c65]Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi S. Jaakkola:
Collaborative future event recommendation. CIKM 2010: 819-828 - [c64]Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola:
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP 2010: 1-11 - [c63]Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag:
Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP 2010: 1288-1298 - [c62]Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson:
Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790 - [c61]David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189 - [c60]Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila:
Learning Bayesian Network Structure using LP Relaxations. AISTATS 2010: 358-365
2000 – 2009
- 2009
- [c59]David A. Sontag, Tommi S. Jaakkola:
Tree Block Coordinate Descent for MAP in Graphical Models. AISTATS 2009: 544-551 - 2008
- [c58]David A. Sontag, Amir Globerson, Tommi S. Jaakkola:
Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544 - [c57]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510 - 2007
- [j19]Georg K. Gerber, Robin D. Dowell, Tommi S. Jaakkola, David K. Gifford:
Automated Discovery of Functional Generality of Human Gene Expression Programs. PLoS Comput. Biol. 3(8) (2007) - [c56]Amir Globerson, Tommi S. Jaakkola:
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007: 553-560 - [c55]David A. Sontag, Tommi S. Jaakkola:
New Outer Bounds on the Marginal Polytope. NIPS 2007: 1393-1400 - [c54]Amir Globerson, Tommi S. Jaakkola:
Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140 - [c53]Amir Globerson, Tommi S. Jaakkola:
Approximate inference using conditional entropy decompositions. AISTATS 2007: 130-138 - [c52]Harald Steck, Tommi S. Jaakkola:
Predictive Discretization during Model Selection. AISTATS 2007: 532-539 - 2006
- [j18]Chen-Hsiang Yeang, Tommi S. Jaakkola:
Modeling the Combinatorial Functions of Multiple Transcription Factors. J. Comput. Biol. 13(2): 463-480 (2006) - [j17]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian learning of tree belief networks. Stat. Comput. 16(1): 77-92 (2006) - [c51]Yuan (Alan) Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford, Hui Ge:
Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo. ISMB (Supplement of Bioinformatics) 2006: 417-423 - [c50]Amir Globerson, Tommi S. Jaakkola:
Approximate inference using planar graph decomposition. NIPS 2006: 473-480 - [c49]Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi S. Jaakkola:
Game Theoretic Algorithms for Protein-DNA binding. NIPS 2006: 1081-1088 - [c48]Yuan (Alan) Qi, Tommi S. Jaakkola:
Parameter Expanded Variational Bayesian Methods. NIPS 2006: 1097-1104 - [p3]Adrian Corduneanu, Tommi S. Jaakkola:
Data-Dependent Regularization. Semi-Supervised Learning 2006: 169-190 - 2005
- [j16]Chen-Hsiang Yeang, Tommi S. Jaakkola:
Time Series Analysis of Gene Expression and Location Data. Int. J. Artif. Intell. Tools 14(5): 755-770 (2005) - [j15]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
A new class of upper bounds on the log partition function. IEEE Trans. Inf. Theory 51(7): 2313-2335 (2005) - [j14]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
MAP estimation via agreement on trees: message-passing and linear programming. IEEE Trans. Inf. Theory 51(11): 3697-3717 (2005) - [c47]Rómer Rosales, Tommi S. Jaakkola:
Focused Inference. AISTATS 2005: 317-324 - [c46]Chen-Hsiang Yeang, Tommi S. Jaakkola:
Modeling the Combinatorial Functions of Multiple Transcription Factors. RECOMB 2005: 506-521 - [c45]Jason D. M. Rennie, Tommi S. Jaakkola:
Using term informativeness for named entity detection. SIGIR 2005: 353-360 - [i2]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
MAP estimation via agreement on (hyper)trees: Message-passing and linear programming. CoRR abs/cs/0508070 (2005) - 2004
- [j13]Chen-Hsiang Yeang, Trey Ideker, Tommi S. Jaakkola:
Physical Network Models. J. Comput. Biol. 11(2/3): 243-262 (2004) - [j12]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations. Stat. Comput. 14(2): 143-166 (2004) - [c44]Karen Sachs, Omar D. Perez, Dana Pe'er, Garry P. Nolan, David K. Gifford, Tommi S. Jaakkola, Douglas A. Lauffenburger:
Analysis of Signaling Pathways in Human T-Cells Using Bayesian Network Modeling of Single Cell Data. CSB 2004: 644 - [c43]Harald Steck, Tommi S. Jaakkola:
Predictive Discretization During Model Selection. DAGM-Symposium 2004: 1-8 - [c42]Adrian Corduneanu, Tommi S. Jaakkola:
Distributed Information Regularization on Graphs. NIPS 2004: 297-304 - [c41]Nathan Srebro, Noga Alon, Tommi S. Jaakkola:
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS 2004: 1321-1328 - [c40]Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola:
Maximum-Margin Matrix Factorization. NIPS 2004: 1329-1336 - 2003
- [j11]Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi S. Jaakkola:
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. Bioinform. 19(9): 1070-1078 (2003) - [j10]Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon:
Continuous Representations of Time-Series Gene Expression Data. J. Comput. Biol. 10(3/4): 341-356 (2003) - [j9]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Tree-based reparameterization framework for analysis of sum-product and related algorithms. IEEE Trans. Inf. Theory 49(5): 1120-1146 (2003) - [c39]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matching. AISTATS 2003: 308-315 - [c38]Chen-Hsiang Yeang, Tommi S. Jaakkola:
Time Series Analysis of Gene Expression and Location Data. BIBE 2003: 305-312 - [c37]Nathan Srebro, Tommi S. Jaakkola:
Weighted Low-Rank Approximations. ICML 2003: 720-727 - [c36]Nathan Srebro, Tommi S. Jaakkola:
Linear Dependent Dimensionality Reduction. NIPS 2003: 145-152 - [c35]Harald Steck, Tommi S. Jaakkola:
Bias-Corrected Bootstrap and Model Uncertainty. NIPS 2003: 521-528 - [c34]Claire Monteleoni, Tommi S. Jaakkola:
Online Learning of Non-stationary Sequences. NIPS 2003: 1093-1100 - [c33]Chen-Hsiang Yeang, Tommi S. Jaakkola:
Physical network models and multi-source data integration. RECOMB 2003: 312-321 - [c32]Adrian Corduneanu, Tommi S. Jaakkola:
On Information Regularization. UAI 2003: 151-158 - 2002
- [j8]Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young:
Bayesian Methods for Elucidating Genetic Regulatory Networks. IEEE Intell. Syst. 17(2): 37-43 (2002) - [c31]Harald Steck, Tommi S. Jaakkola:
On the Dirichlet Prior and Bayesian Regularization. NIPS 2002: 697-704 - [c30]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Exact MAP Estimates by (Hyper)tree Agreement. NIPS 2002: 809-816 - [c29]Martin Szummer, Tommi S. Jaakkola:
Information Regularization with Partially Labeled Data. NIPS 2002: 1025-1032 - [c28]Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young:
Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. Pacific Symposium on Biocomputing 2002: 437-449 - [c27]Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon:
A new approach to analyzing gene expression time series data. RECOMB 2002: 39-48 - [c26]Adrian Corduneanu, Tommi S. Jaakkola:
Continuation Methods for Mixing Heterogenous Sources. UAI 2002: 111-118 - [c25]Harald Steck, Tommi S. Jaakkola:
Unsupervised Active Learning in Large Domains. UAI 2002: 469-476 - [c24]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
A New Class of upper Bounds on the Log Partition Function. UAI 2002: 536-543 - [c23]Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi S. Jaakkola, Nathan Srebro:
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. WABI 2002: 506-520 - 2001
- [c22]Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola:
Fast optimal leaf ordering for hierarchical clustering. ISMB (Supplement of Bioinformatics) 2001: 22-29 - [c21]Tommi S. Jaakkola, Hava T. Siegelmann:
Active Information Retrieval. NIPS 2001: 777-784 - [c20]Martin Szummer, Tommi S. Jaakkola:
Partially labeled classification with Markov random walks. NIPS 2001: 945-952 - [c19]Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Tree-based reparameterization for approximate inference on loopy graphs. NIPS 2001: 1001-1008 - [c18]Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young:
Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. Pacific Symposium on Biocomputing 2001: 422-433 - [e1]Thomas S. Richardson, Tommi S. Jaakkola:
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001, Key West, Florida, USA, January 4-7, 2001. Society for Artificial Intelligence and Statistics 2001 [contents] - 2000
- [j7]Tommi S. Jaakkola, Mark Diekhans, David Haussler:
A Discriminative Framework for Detecting Remote Protein Homologies. J. Comput. Biol. 7(1-2): 95-114 (2000) - [j6]Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári:
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Mach. Learn. 38(3): 287-308 (2000) - [j5]Tommi S. Jaakkola, Michael I. Jordan:
Bayesian parameter estimation via variational methods. Stat. Comput. 10(1): 25-37 (2000) - [c17]Brendan J. Frey, Relu Patrascu, Tommi S. Jaakkola, Jodi Moran:
Sequentially Fitting "Inclusive" Trees for Inference in Noisy-OR Networks. NIPS 2000: 493-499 - [c16]Martin Szummer, Tommi S. Jaakkola:
Kernel Expansions with Unlabeled Examples. NIPS 2000: 626-632 - [c15]Tony Jebara, Tommi S. Jaakkola:
Feature Selection and Dualities in Maximum Entropy Discrimination. UAI 2000: 291-300 - [c14]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian Learning of Tree Belief Networks. UAI 2000: 380-388
1990 – 1999
- 1999
- [j4]Tommi S. Jaakkola, Michael I. Jordan:
Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. 10: 291-322 (1999) - [j3]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Mach. Learn. 37(2): 183-233 (1999) - [c13]Tommi S. Jaakkola, David Haussler:
Probabilistic kernel regression models. AISTATS 1999 - [c12]Tommi S. Jaakkola, Mark Diekhans, David Haussler:
Using the Fisher Kernel Method to Detect Remote Protein Homologies. ISMB 1999: 149-158 - [c11]Tommi S. Jaakkola, Marina Meila, Tony Jebara:
Maximum Entropy Discrimination. NIPS 1999: 470-476 - 1998
- [c10]Tommi S. Jaakkola, David Haussler:
Exploiting Generative Models in Discriminative Classifiers. NIPS 1998: 487-493 - [p2]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models 1998: 105-161 - [p1]Tommi S. Jaakkola, Michael I. Jordan:
Improving the Mean Field Approximation Via the Use of Mixture Distributions. Learning in Graphical Models 1998: 163-173 - 1997
- [c9]Tommi S. Jaakkola, Michael I. Jordan:
A Variational Approach to Bayesian Logistic Regression Models and their Extensions. AISTATS 1997: 283-294 - [c8]Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan:
Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997: 416-422 - 1996
- [j2]Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. 4: 61-76 (1996) - [c7]Tommi S. Jaakkola, Michael I. Jordan:
Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS 1996: 487-493 - [c6]Tommi S. Jaakkola, Michael I. Jordan:
Computing upper and lower bounds on likelihoods in intractable networks. UAI 1996: 340-348 - [i1]Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks. CoRR cs.AI/9603102 (1996) - 1995
- [c5]Tommi S. Jaakkola, Lawrence K. Saul, Michael I. Jordan:
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534 - 1994
- [j1]Tommi S. Jaakkola, Michael I. Jordan, Satinder P. Singh:
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Comput. 6(6): 1185-1201 (1994) - [c4]Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan:
Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994: 284-292 - [c3]Tommi S. Jaakkola, Satinder Singh, Michael I. Jordan:
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS 1994: 345-352 - [c2]Satinder Singh, Tommi S. Jaakkola, Michael I. Jordan:
Reinforcement Learning with Soft State Aggregation. NIPS 1994: 361-368 - 1993
- [c1]Tommi S. Jaakkola, Michael I. Jordan, Satinder Singh:
Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS 1993: 703-710
Coauthor Index
[j36] [c172] [c171] [c169] [c168] [c166] [c164] [c163] [i147] [i146] [i144] [i143] [i142] [i134] [c160] [c159] [c158] [c155] [i125] [i123] [i119] [i118] [i116] [i115] [i112] [i110] [i107] [j32] [c149] [c148] [c146] [c144] [c143] [c142] [c141] [i103] [i102] [i101] [i97] [i96] [i95] [i94] [i92] [i90] [j31] [c139] [c138] [c137] [c136] [c132] [i88] [i87] [i86] [i85] [i83] [i82] [i79] [i78] [c130] [c124] [c123] [c122] [c121] [i77] [i76] [i75] [i74] [i70] [i69] [i68] [i67] [i66] [i64] [j30] [j29] [c117] [c116] [c109] [i59] [i56] [i55] [i52] [i50] [j27] [c106] [i46] [i38] [j26] [j25] [c100] [c97] [c95] [i37] [i36] [i35] [i33] [i32] [c93] [c92] [c90] [c89] [i29] [j23] [c85] [i27] [i24] [i19] [c82] [c81] [c77]
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