Tommi S. Jaakkola

research

∙ 10/26/2021

Understanding Interlocking Dynamics of Cooperative Rationalization

Selective rationalization explains the prediction of complex neural netw...

Mo Yu, et al. ∙

research

∙ 06/04/2021

NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training

Balancing the needs of data privacy and predictive utility is a central ...

Adam Yala, et al. ∙

research

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

Han Zhao, et al. ∙

research

∙ 03/22/2020

Invariant Rationalization

Selective rationalization improves neural network interpretability by id...

Shiyu Chang, et al. ∙

research

∙ 11/06/2019

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchi...

David Alvarez-Melis, et al. ∙

research

∙ 10/29/2019

Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

Selective rationalization has become a common mechanism to ensure that p...

Mo Yu, et al. ∙

research

∙ 10/28/2019

A Game Theoretic Approach to Class-wise Selective Rationalization

Selection of input features such as relevant pieces of text has become a...

Shiyu Chang, et al. ∙

research

∙ 10/21/2019

Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

We propose a new model for making generalizable and diverse retrosynthet...

Benson Chen, et al. ∙

research

∙ 09/30/2019

Locally Constant Networks

We show how neural models can be used to realize piece-wise constant fun...

Guang-He Lee, et al. ∙

research

∙ 07/07/2019

Towards Robust, Locally Linear Deep Networks

Deep networks realize complex mappings that are often understood by thei...

Guang-He Lee, et al. ∙

research

∙ 06/12/2019

A Stratified Approach to Robustness for Randomly Smoothed Classifiers

Strong theoretical guarantees of robustness can be given for ensembles o...

Guang-He Lee, et al. ∙

research

∙ 02/26/2019

Functional Transparency for Structured Data: a Game-Theoretic Approach

We provide a new approach to training neural models to exhibit transpare...

Guang-He Lee, et al. ∙

research

∙ 02/06/2019

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...

Hao Wang, et al. ∙

research

∙ 08/31/2018

Gromov-Wasserstein Alignment of Word Embedding Spaces

Cross-lingual or cross-domain correspondences play key roles in tasks ra...

David Alvarez-Melis, et al. ∙

research

∙ 06/30/2018

Game-Theoretic Interpretability for Temporal Modeling

Interpretability has arisen as a key desideratum of machine learning mod...

Guang-He Lee, et al. ∙

research

∙ 06/25/2018

Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...

David Alvarez-Melis, et al. ∙

research

∙ 06/21/2018

On the Robustness of Interpretability Methods

We argue that robustness of explanations---i.e., that similar inputs sho...

David Alvarez-Melis, et al. ∙

research

∙ 06/20/2018

Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models ...

David Alvarez-Melis, et al. ∙

research

∙ 12/17/2017

Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for a...

David Alvarez-Melis, et al. ∙

research

∙ 11/02/2015

From random walks to distances on unweighted graphs

Large unweighted directed graphs are commonly used to capture relations ...

Tatsunori B. Hashimoto, et al. ∙

research

∙ 09/18/2015

Word, graph and manifold embedding from Markov processes

Continuous vector representations of words and objects appear to carry s...

Tatsunori B. Hashimoto, et al. ∙

research

∙ 11/20/2014

Metric recovery from directed unweighted graphs

We analyze directed, unweighted graphs obtained from x_i∈R^d by connecti...

Tatsunori B. Hashimoto, et al. ∙

research

∙ 09/26/2013

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...

Jean Honorio, et al. ∙

research

∙ 02/13/2013

Computing Upper and Lower Bounds on Likelihoods in Intractable Networks

We present deterministic techniques for computing upper and lower bounds...

Tommi S. Jaakkola, et al. ∙

research

∙ 01/16/2013

Tractable Bayesian Learning of Tree Belief Networks

In this paper we present decomposable priors, a family of priors over st...

Marina Meila, et al. ∙

research

∙ 01/16/2013

Feature Selection and Dualities in Maximum Entropy Discrimination

Incorporating feature selection into a classification or regression meth...

Tony S. Jebara, et al. ∙

research

∙ 12/12/2012

A New Class of Upper Bounds on the Log Partition Function

Bounds on the log partition function are important in a variety of conte...

Martin Wainwright, et al. ∙

research

∙ 12/12/2012

Unsupervised Active Learning in Large Domains

Active learning is a powerful approach to analyzing data effectively. We...

Harald Steck, et al. ∙

research

∙ 12/12/2012

Continuation Methods for Mixing Heterogenous Sources

A number of modern learning tasks involve estimation from heterogeneous ...

Adrian Corduneanu, et al. ∙

research

∙ 10/19/2012

On Information Regularization

We formulate a principle for classification with the knowledge of the ma...

Adrian Corduneanu, et al. ∙

research

∙ 06/20/2012

Convergent Propagation Algorithms via Oriented Trees

Inference problems in graphical models are often approximated by casting...

Amir Globerson, et al. ∙

research

∙ 06/13/2012

Tightening LP Relaxations for MAP using Message Passing

Linear Programming (LP) relaxations have become powerful tools for findi...

David Sontag, et al. ∙

Tommi S. Jaakkola

Featured Co-authors