Jaakkola, Tommi

134 publications

ICLR 2025 An Information Criterion for Controlled Disentanglement of Multimodal Data Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler

ICLR 2025 Composing Unbalanced Flows for Flexible Docking and Relaxation Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, Andreas Krause

ICLRW 2025 Continuously Tempered Diffusion Samplers Ezra Erives, Bowen Jing, Peter Holderrieth, Tommi Jaakkola

ICLR 2025 Data Distillation for Extrapolative Protein Design Through Exact Preference Optimization Mostafa Karimi, Sharmi Banerjee, Tommi Jaakkola, Bella Dubrov, Shang Shang, Ron Benson

ICLR 2025 Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning Yujian Liu, Shiyu Chang, Tommi Jaakkola, Yang Zhang

ICLR 2025 Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Avantika Lal, Tommi Jaakkola, Sergey Levine, Aviv Regev, Hanchen, Tommaso Biancalani

ICLR 2025 Generator Matching: Generative Modeling with Arbitrary Markov Processes Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman

ICLRW 2025 Hierarchical Protein Backbone Generation with Latent and Structure Diffusion Jason Yim, Marouane Jaakik, Ge Liu, Jacob Gershon, Karsten Kreis, David Baker, Regina Barzilay, Tommi Jaakkola

ICML 2025 Identifying Biological Perturbation Targets Through Causal Differential Networks Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola

ICML 2025 LEAPS: A Discrete Neural Sampler via Locally Equivariant Networks Peter Holderrieth, Michael Samuel Albergo, Tommi Jaakkola

ICLRW 2025 LEAPS: A Discrete Neural Sampler via Locally Equivariant Networks Peter Holderrieth, Michael Samuel Albergo, Tommi Jaakkola

NeurIPS 2025 Learning Diffusion Models with Flexible Representation Guidance Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, Tommi Jaakkola

NeurIPS 2025 Next Semantic Scale Prediction via Hierarchical Diffusion Language Models Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, Tommi Jaakkola

ICLR 2025 ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, Karsten Kreis

TMLR 2025 Sample, Estimate, Aggregate: A Recipe for Causal Discovery Foundation Models Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola

CVPR 2025 Scaling Inference Time Compute for Diffusion Models Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, Saining Xie

ICML 2025 Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, Tommi Jaakkola

ICLR 2025 Think While You Generate: Discrete Diffusion with Planned Denoising Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stark, Yilun Xu, Tommi Jaakkola, Rafael Gomez-Bombarelli

NeurIPSW 2024 A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Tommi Jaakkola, Tess Smidt

NeurIPS 2024 A Recipe for Charge Density Prediction Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola

ICMLW 2024 A Recipe for Charge Density Prediction Xiang Fu, Andrew Scott Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, Tommi Jaakkola

ICML 2024 AlphaFold Meets Flow Matching for Generating Protein Ensembles Bowen Jing, Bonnie Berger, Tommi Jaakkola

NeurIPSW 2024 An Information Criterion for Controlled Disentanglement of Multimodal Data Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler

CVPR 2024 Correcting Diffusion Generation Through Resampling Yujian Liu, Yang Zhang, Tommi Jaakkola, Shiyu Chang

ICMLW 2024 Diffusion Domain Expansion: Learning to Coordinate Pre-Trained Diffusion Models Egor Lifar, Semyon Savkin, Timur Garipov, Shangyuan Tong, Tommi Jaakkola

ICML 2024 Dirichlet Flow Matching with Applications to DNA Sequence Design Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Dirichlet Flow Matching with Applications to DNA Sequence Design Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola

ICML 2024 DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis

ICMLW 2024 Extrapolative Protein Design Through Triplet-Based Preference Learning Mostafa Karimi, Sharmi Banerjee, Tommi Jaakkola, Bella Dubrov, Shang Shang, Ron Benson

NeurIPSW 2024 Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen, Aviv Regev

ICMLW 2024 Flexible Docking via Unbalanced Flow Matching Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, Andreas Krause

ICMLW 2024 Flexible Docking via Unbalanced Flow Matching Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, Andreas Krause

ICML 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola

ICLRW 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola

NeurIPS 2024 Generative Modeling of Molecular Dynamics Trajectories Bowen Jing, Hannes Stärk, Tommi Jaakkola, Bonnie Berger

ICMLW 2024 Generative Modeling of Molecular Dynamics Trajectories Bowen Jing, Hannes Stark, Tommi Jaakkola, Bonnie Berger

NeurIPS 2024 Hamiltonian Score Matching and Generative Flows Peter Holderrieth, Yilun Xu, Tommi Jaakkola

ICML 2024 Harmonic Self-Conditioned Flow Matching for Joint Multi-Ligand Docking and Binding Site Design Hannes Stark, Bowen Jing, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Hessian Reparametrization for Coarse-Grained Energy Minimization Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, Tommi Jaakkola

TMLR 2024 Improved Motif-Scaffolding with SE(3) Flow Matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, Jose Jimenez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noe, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Improved Motif-Scaffolding with SE(3) Flow Matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, Jose Jimenez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noe, Regina Barzilay, Tommi Jaakkola

NeurIPS 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

ICMLW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPSW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPSW 2024 In-Context Symmetries: Self-Supervised Learning Through Contextual World Models Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka

NeurIPS 2024 Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, Tommi Jaakkola

TMLR 2024 Risk-Controlling Model Selection via Guided Bayesian Optimization Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Sample, Estimate, Aggregate: A Recipe for Causal Discovery Foundation Models Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models Ezra Erives, Bowen Jing, Tommi Jaakkola

NeurIPSW 2023 AlphaFold Meets Flow Matching for Generating Protein Ensembles Bowen Jing, Bonnie Berger, Tommi Jaakkola

NeurIPSW 2023 AlphaFold Meets Flow Matching for Generating Protein Ensembles Bowen Jing, Bonnie Berger, Tommi Jaakkola

NeurIPSW 2023 AlphaFold Meets Flow Matching for Generating Protein Ensembles Bowen Jing, Bonnie Berger, Tommi Jaakkola

NeurIPS 2023 Compositional Foundation Models for Hierarchical Planning Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie P. Kaelbling, Akash Srivastava, Pulkit Agrawal

NeurIPSW 2023 Compositional Foundation Models for Hierarchical Planning Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Joshua Tenenbaum, Leslie Kaelbling, Akash Srivastava, Pulkit Agrawal

NeurIPS 2023 Compositional Sculpting of Iterative Generative Processes Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi Jaakkola

TMLR 2023 Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola

NeurIPSW 2023 Harmonic Prior Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design Hannes Stark, Bowen Jing, Regina Barzilay, Tommi Jaakkola

NeurIPSW 2023 Harmonic Prior Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design Hannes Stark, Bowen Jing, Regina Barzilay, Tommi Jaakkola

NeurIPSW 2023 Learning Interatomic Potentials at Multiple Scales Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky

NeurIPSW 2023 Learning Interatomic Potentials at Multiple Scales Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, Boris Kozinsky

NeurIPSW 2023 Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms Bowen Jing, Tommi Jaakkola, Bonnie Berger

NeurIPSW 2023 Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms Bowen Jing, Tommi Jaakkola, Bonnie Berger

NeurIPSW 2023 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi Jaakkola, Jake Allen Smith

NeurIPSW 2023 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi Jaakkola, Jake Allen Smith

ICML 2023 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola

NeurIPSW 2023 Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola

NeurIPSW 2023 Removing Biases from Molecular Representations via Information Maximization Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola

NeurIPS 2023 Restart Sampling for Improving Generative Processes Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

ICML 2023 SE(3) Diffusion Model with Application to Protein Backbone Generation Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola

NeurIPSW 2023 The Discovery of Binding Modes Requires Rethinking Docking Generalization Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi Jaakkola

ICML 2023 Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi Jaakkola, Shiyu Chang

ICML 2022 Antibody-Antigen Docking and Design via Hierarchical Structure Refinement Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2022 Conformal Prediction Sets with Limited False Positives Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay

ICML 2022 EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola

NeurIPS 2022 Poisson Flow Generative Models Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

ECCV 2022 Subspace Diffusion Generative Models Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi Jaakkola

NeurIPS 2022 Torsional Diffusion for Molecular Conformer Generation Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola

ICML 2021 Few-Shot Conformal Prediction with Auxiliary Tasks Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay

NeurIPS 2021 GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, Tommi Jaakkola

ICML 2021 Information Obfuscation of Graph Neural Networks Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov

ICML 2021 Learning Task Informed Abstractions Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola

CVPR 2021 Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis Karren Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler

NeurIPS 2021 Understanding Interlocking Dynamics of Cooperative Rationalization Mo Yu, Yang Zhang, Shiyu Chang, Tommi Jaakkola

ICML 2020 Educating Text Autoencoders: Latent Representation Guidance via Denoising Tianxiao Shen, Jonas Mueller, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2020 Generalization and Representational Limits of Graph Neural Networks Vikas Garg, Stefanie Jegelka, Tommi Jaakkola

ICML 2020 Hierarchical Generation of Molecular Graphs Using Structural Motifs Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2020 Improving Molecular Design by Stochastic Iterative Target Augmentation Kevin Yang, Wengong Jin, Kyle Swanson, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2020 Invariant Rationalization Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola

ICML 2020 Multi-Objective Molecule Generation Using Interpretable Substructures Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2020 Predicting Deliberative Outcomes Vikas Garg, Tommi Jaakkola

ICLR 2020 Self-Supervised Learning of Appliance Usage Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi Jaakkola

AISTATS 2020 Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces David Alvarez-Melis, Youssef Mroueh, Tommi Jaakkola

NeurIPS 2019 A Game Theoretic Approach to Class-Wise Selective Rationalization Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola

NeurIPS 2019 Direct Optimization Through $\arg \max$ for Discrete Variational Auto-Encoder Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan

ICML 2019 Functional Transparency for Structured Data: A Game-Theoretic Approach Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola

NeurIPS 2019 Generative Models for Graph-Based Protein Design John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola

ICLRW 2019 Generative Models for Graph-Based Protein Design John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

ICLR 2019 Learning Multimodal Graph-to-Graph Translation for Molecule Optimization Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

NeurIPS 2019 Solving Graph Compression via Optimal Transport Vikas Garg, Tommi Jaakkola

NeurIPS 2019 Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi Jaakkola

ICML 2018 Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin, Regina Barzilay, Tommi Jaakkola

NeurIPS 2018 Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez Melis, Tommi Jaakkola

ICML 2017 Deriving Neural Architectures from Sequence and Graph Kernels Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola

NeurIPS 2017 Local Aggregative Games Vikas Garg, Tommi Jaakkola

NeurIPS 2017 Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network Wengong Jin, Connor Coley, Regina Barzilay, Tommi Jaakkola

ICML 2017 Sequence to Better Sequence: Continuous Revision of Combinatorial Structures Jonas Mueller, David Gifford, Tommi Jaakkola

NeurIPS 2017 Style Transfer from Non-Parallel Text by Cross-Alignment Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola

ICML 2016 Learning Population-Level Diffusions with Generative RNNs Tatsunori Hashimoto, David Gifford, Tommi Jaakkola

NeurIPS 2016 Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola

NeurIPS 2015 From Random Walks to Distances on Unweighted Graphs Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

NeurIPS 2015 Principal Differences Analysis: Interpretable Characterization of Differences Between Distributions Jonas W Mueller, Tommi Jaakkola

ICML 2014 A Unified Framework for Consistency of Regularized Loss Minimizers Jean Honorio, Tommi Jaakkola

NeurIPS 2014 Controlling Privacy in Recommender Systems Yu Xin, Tommi Jaakkola

ICML 2014 On Measure Concentration of Random Maximum A-Posteriori Perturbations Francesco Orabona, Tamir Hazan, Anand Sarwate, Tommi Jaakkola

NeurIPS 2013 Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola

NeurIPS 2013 On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola

AISTATS 2012 Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation J. Zico Kolter, Tommi Jaakkola

AISTATS 2012 Primal-Dual Methods for Sparse Constrained Matrix Completion Yu Xin, Tommi Jaakkola

AISTATS 2010 Learning Bayesian Network Structure Using LP Relaxations Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila

AISTATS 2009 Tree Block Coordinate Descent for MAP in Graphical Models David Sontag, Tommi Jaakkola

AISTATS 2007 Approximate Inference Using Conditional Entropy Decompositions Amir Globerson, Tommi Jaakkola

NeurIPS 2001 Active Information Retrieval Tommi Jaakkola, Hava T. Siegelmann

NeurIPS 2001 Partially Labeled Classification with Markov Random Walks Martin Szummer, Tommi Jaakkola

NeurIPS 2001 Tree-Based Reparameterization for Approximate Inference on Loopy Graphs Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky

NeurIPS 2000 Kernel Expansions with Unlabeled Examples Martin Szummer, Tommi Jaakkola

NeurIPS 2000 Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-or Networks Brendan J. Frey, Relu Patrascu, Tommi Jaakkola, Jodi Moran

NeurIPS 1999 Maximum Entropy Discrimination Tommi Jaakkola, Marina Meila, Tony Jebara

NeurIPS 1998 Exploiting Generative Models in Discriminative Classifiers Tommi Jaakkola, David Haussler

NeurIPS 1997 Approximating Posterior Distributions in Belief Networks Using Mixtures Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan

NeurIPS 1996 Recursive Algorithms for Approximating Probabilities in Graphical Models Tommi Jaakkola, Michael I. Jordan

NeurIPS 1995 Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan

NeurIPS 1994 Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan

NeurIPS 1994 Reinforcement Learning with Soft State Aggregation Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan

NeurIPS 1993 Convergence of Stochastic Iterative Dynamic Programming Algorithms Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh