Tommi S. Jaakkola

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MIT

Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society.

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of v...
02/06/2019 ∙ by Hao Wang, et al. ∙ 24 ∙ share

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A Game Theoretic Approach to Class-wise Selective Rationalization

Selection of input features such as relevant pieces of text has become a...
10/28/2019 ∙ by Shiyu Chang, et al. ∙ 13 ∙ share

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Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

We propose a new model for making generalizable and diverse retrosynthet...
10/21/2019 ∙ by Benson Chen, et al. ∙ 9 ∙ share

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Invariant Rationalization

Selective rationalization improves neural network interpretability by id...
03/22/2020 ∙ by Shiyu Chang, et al. ∙ 5 ∙ share

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Towards Robust, Locally Linear Deep Networks

Deep networks realize complex mappings that are often understood by thei...
07/07/2019 ∙ by Guang-He Lee, et al. ∙ 2 ∙ share

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A Stratified Approach to Robustness for Randomly Smoothed Classifiers

Strong theoretical guarantees of robustness can be given for ensembles o...
06/12/2019 ∙ by Guang-He Lee, et al. ∙ 1 ∙ share

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From random walks to distances on unweighted graphs

Large unweighted directed graphs are commonly used to capture relations ...
11/02/2015 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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Computing Upper and Lower Bounds on Likelihoods in Intractable Networks

We present deterministic techniques for computing upper and lower bounds...
02/13/2013 ∙ by Tommi S. Jaakkola, et al. ∙ 0 ∙ share

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Metric recovery from directed unweighted graphs

We analyze directed, unweighted graphs obtained from x_i∈R^d by connecti...
11/20/2014 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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Tightening LP Relaxations for MAP using Message Passing

Linear Programming (LP) relaxations have become powerful tools for findi...
06/13/2012 ∙ by David Sontag, et al. ∙ 0 ∙ share

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Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models

We propose maximum likelihood estimation for learning Gaussian graphical...
09/26/2013 ∙ by Jean Honorio, et al. ∙ 0 ∙ share

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Tractable Bayesian Learning of Tree Belief Networks

In this paper we present decomposable priors, a family of priors over st...
01/16/2013 ∙ by Marina Meila, et al. ∙ 0 ∙ share

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Feature Selection and Dualities in Maximum Entropy Discrimination

Incorporating feature selection into a classification or regression meth...
01/16/2013 ∙ by Tony S. Jebara, et al. ∙ 0 ∙ share

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A New Class of Upper Bounds on the Log Partition Function

Bounds on the log partition function are important in a variety of conte...
12/12/2012 ∙ by Martin Wainwright, et al. ∙ 0 ∙ share

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Unsupervised Active Learning in Large Domains

Active learning is a powerful approach to analyzing data effectively. We...
12/12/2012 ∙ by Harald Steck, et al. ∙ 0 ∙ share

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Continuation Methods for Mixing Heterogenous Sources

A number of modern learning tasks involve estimation from heterogeneous ...
12/12/2012 ∙ by Adrian Corduneanu, et al. ∙ 0 ∙ share

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On Information Regularization

We formulate a principle for classification with the knowledge of the ma...
10/19/2012 ∙ by Adrian Corduneanu, et al. ∙ 0 ∙ share

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Convergent Propagation Algorithms via Oriented Trees

Inference problems in graphical models are often approximated by casting...
06/20/2012 ∙ by Amir Globerson, et al. ∙ 0 ∙ share

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Word, graph and manifold embedding from Markov processes

Continuous vector representations of words and objects appear to carry s...
09/18/2015 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for a...
12/17/2017 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models ...
06/20/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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On the Robustness of Interpretability Methods

We argue that robustness of explanations---i.e., that similar inputs sho...
06/21/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...
06/25/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Game-Theoretic Interpretability for Temporal Modeling

Interpretability has arisen as a key desideratum of machine learning mod...
06/30/2018 ∙ by Guang-He Lee, et al. ∙ 0 ∙ share

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Gromov-Wasserstein Alignment of Word Embedding Spaces

Cross-lingual or cross-domain correspondences play key roles in tasks ra...
08/31/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Functional Transparency for Structured Data: a Game-Theoretic Approach

We provide a new approach to training neural models to exhibit transpare...
02/26/2019 ∙ by Guang-He Lee, et al. ∙ 0 ∙ share

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Locally Constant Networks

We show how neural models can be used to realize piece-wise constant fun...
09/30/2019 ∙ by Guang-He Lee, et al. ∙ 0 ∙ share

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Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

Selective rationalization has become a common mechanism to ensure that p...
10/29/2019 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchi...
11/06/2019 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Tommi S. Jaakkola

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