← Authors

Tommi Jaakkola

101 papers · 2010–2025 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (25) 🌍 Conference Polyglot (10) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (15) 🏠 Conference Loyalist (27) 🌟 Keyword Trendsetter Combo (4) 🀝 Dynamic Duo (25) πŸ‘‘ Triple Crown πŸ† Keyword Champion 🌱 Topic Pioneer πŸ”¬ Deep Specialist (14) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (14) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ’Ž Century Club (101) πŸ—ƒοΈ Keyword Collector (84)

Conferences

ICML (31) NIPS (27) EMNLP (13) AISTATS (11) ICLR (10) CVPR (3) ACL (2) NAACL (2) ECCV (1) IJCNLP (1)

Top co-authors

Regina Barzilay (25) Shiyu Chang (10) Wengong Jin (10) Tao Lei (9) Yang Zhang (9) Dr.Regina Barzilay (8) Vikas Garg (7) Bowen Jing (7) David Alvarez-Melis (6) Tamir Hazan (6)

Research topics

Core Methods (1) Architectures (1) Applications (1) Privacy (1)

Keywords

graph neural network (10) generative model (8) diffusion model (8) optimal transport (5) feature selection (5) drug discovery (5) image generation (5) game theory (5) molecular graph (4) adversarial learning (4) molecular generation (3) selective rationalization (3) structured prediction (3) equivariant neural network (3) representation learning (3) gibbs distribution (3) conformal prediction (3) maximum a posteriori (3) variational autoencoder (3) map inference (2)

Papers

LEAPS: A discrete neural sampler via locally equivariant networks ICML 2025
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning ICLR 2025
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design ICLR 2025
An Information Criterion for Controlled Disentanglement of Multimodal Data ICLR 2025
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids ICLR 2025
Data Distillation for extrapolative protein design through exact preference optimization ICLR 2025
Generator Matching: Generative modeling with arbitrary Markov processes ICLR 2025
Scaling Inference Time Compute for Diffusion Models CVPR 2025
Composing Unbalanced Flows for Flexible Docking and Relaxation ICLR 2025
Think while You Generate: Discrete Diffusion with Planned Denoising ICLR 2025
Identifying biological perturbation targets through causal differential networks ICML 2025
Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations ICML 2025
Thought calibration: Efficient and confident test-time scaling EMNLP 2025
Generative Modeling of Molecular Dynamics Trajectories NIPS 2024
Hamiltonian Score Matching and Generative Flows NIPS 2024
Correcting Diffusion Generation through Resampling CVPR 2024
AlphaFold Meets Flow Matching for Generating Protein Ensembles ICML 2024
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design ICML 2024
Revisiting Who’s Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective EMNLP 2024
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents ICML 2024
Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design ICML 2024
Dirichlet Flow Matching with Applications to DNA Sequence Design ICML 2024
A Recipe for Charge Density Prediction NIPS 2024
Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations NIPS 2024
In-Context Symmetries: Self-Supervised Learning through Contextual World Models NIPS 2024
Restart Sampling for Improving Generative Processes NIPS 2023
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models ICML 2023
SE(3) diffusion model with application to protein backbone generation ICML 2023
Compositional Sculpting of Iterative Generative Processes NIPS 2023
Compositional Foundation Models for Hierarchical Planning NIPS 2023
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models ICML 2023
Subspace Diffusion Generative Models ECCV 2022
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement ICML 2022
Conformal Prediction Sets with Limited False Positives ICML 2022
Poisson Flow Generative Models NIPS 2022
Torsional Diffusion for Molecular Conformer Generation NIPS 2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction ICML 2022
Information Obfuscation of Graph Neural Networks ICML 2021
Learning Task Informed Abstractions ICML 2021
Few-Shot Conformal Prediction with Auxiliary Tasks ICML 2021
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis CVPR 2021
Consistent Accelerated Inference via Confident Adaptive Transformers EMNLP 2021
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles NIPS 2021
Understanding Interlocking Dynamics of Cooperative Rationalization NIPS 2021
Predicting deliberative outcomes ICML 2020
Invariant Rationalization ICML 2020
Multi-Objective Molecule Generation using Interpretable Substructures ICML 2020
Blank Language Models EMNLP 2020
Educating Text Autoencoders: Latent Representation Guidance via Denoising ICML 2020
Improving Molecular Design by Stochastic Iterative Target Augmentation ICML 2020
Self-Supervised Learning of Appliance Usage ICLR 2020
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces AISTATS 2020
Generalization and Representational Limits of Graph Neural Networks ICML 2020
Hierarchical Generation of Molecular Graphs using Structural Motifs ICML 2020
Solving graph compression via optimal transport NIPS 2019
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder NIPS 2019
A Game Theoretic Approach to Class-wise Selective Rationalization NIPS 2019
Generative Models for Graph-Based Protein Design NIPS 2019
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers NIPS 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control EMNLP 2019
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization ICLR 2019
Functional Transparency for Structured Data: a Game-Theoretic Approach ICML 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control IJCNLP 2019
Towards Robust Interpretability with Self-Explaining Neural Networks NIPS 2018
Structured Optimal Transport AISTATS 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation ICML 2018
Gromov-Wasserstein Alignment of Word Embedding Spaces EMNLP 2018
A causal framework for explaining the predictions of black-box sequence-to-sequence models EMNLP 2017
Deriving Neural Architectures from Sequence and Graph Kernels ICML 2017
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures ICML 2017
Local Aggregative Games NIPS 2017
Learning Optimal Interventions AISTATS 2017
Style Transfer from Non-Parallel Text by Cross-Alignment NIPS 2017
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network NIPS 2017
Learning to refine text based recommendations EMNLP 2016
Rationalizing Neural Predictions EMNLP 2016
Learning Population-Level Diffusions with Generative RNNs ICML 2016
Semi-supervised Question Retrieval with Gated Convolutions NAACL 2016
CRAFT: ClusteR-specific Assorted Feature selecTion AISTATS 2016
Learning Tree Structured Potential Games NIPS 2016
Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings NAACL 2016
From random walks to distances on unweighted graphs NIPS 2015
Metric recovery from directed unweighted graphs AISTATS 2015
Molding CNNs for text: non-linear, non-consecutive convolutions EMNLP 2015
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions NIPS 2015
Active Boundary Annotation using Random MAP Perturbations AISTATS 2014
Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014
Low-Rank Tensors for Scoring Dependency Structures ACL 2014
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees ACL 2014
A Unified Framework for Consistency of Regularized Loss Minimizers ICML 2014
On Measure Concentration of Random Maximum A-Posteriori Perturbations ICML 2014
Greed is Good if Randomized: New Inference for Dependency Parsing EMNLP 2014
Controlling privacy in recommender systems NIPS 2014
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees AISTATS 2014
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions NIPS 2013
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations NIPS 2013
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation AISTATS 2012
Primal-Dual methods for sparse constrained matrix completion AISTATS 2012
Dual Decomposition for Parsing with Non-Projective Head Automata EMNLP 2010
Learning Bayesian Network Structure using LP Relaxations AISTATS 2010
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing EMNLP 2010