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04/08/2023
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Understanding how proteins structurally interact is crucial to modern bi...
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10/26/2021
Understanding Interlocking Dynamics of Cooperative Rationalization
Selective rationalization explains the prediction of complex neural netw...
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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 ...
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12/19/2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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03/22/2020
Invariant Rationalization
Selective rationalization improves neural network interpretability by id...
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11/06/2019
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
This paper focuses on the problem of unsupervised alignment of hierarchi...
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10/29/2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Selective rationalization has become a common mechanism to ensure that p...
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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...
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10/21/2019
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthet...
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09/30/2019
Locally Constant Networks
We show how neural models can be used to realize piece-wise constant fun...
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07/07/2019
Towards Robust, Locally Linear Deep Networks
Deep networks realize complex mappings that are often understood by thei...
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06/12/2019
A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Strong theoretical guarantees of robustness can be given for ensembles o...
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02/26/2019
Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transpare...
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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...
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08/31/2018
Gromov-Wasserstein Alignment of Word Embedding Spaces
Cross-lingual or cross-domain correspondences play key roles in tasks ra...
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06/30/2018
Game-Theoretic Interpretability for Temporal Modeling
Interpretability has arisen as a key desideratum of machine learning mod...
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06/25/2018
Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences be...
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06/21/2018
On the Robustness of Interpretability Methods
We argue that robustness of explanations---i.e., that similar inputs sho...
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06/20/2018
Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models ...
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12/17/2017
Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for a...
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11/02/2015
From random walks to distances on unweighted graphs
Large unweighted directed graphs are commonly used to capture relations ...
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09/18/2015
Word, graph and manifold embedding from Markov processes
Continuous vector representations of words and objects appear to carry s...
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11/20/2014
Metric recovery from directed unweighted graphs
We analyze directed, unweighted graphs obtained from x_i∈R^d by connecti...
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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...
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02/13/2013
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
We present deterministic techniques for computing upper and lower bounds...
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01/16/2013
Tractable Bayesian Learning of Tree Belief Networks
In this paper we present decomposable priors, a family of priors over st...
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01/16/2013
Feature Selection and Dualities in Maximum Entropy Discrimination
Incorporating feature selection into a classification or regression meth...
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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...
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12/12/2012
Unsupervised Active Learning in Large Domains
Active learning is a powerful approach to analyzing data effectively. We...
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12/12/2012
Continuation Methods for Mixing Heterogenous Sources
A number of modern learning tasks involve estimation from heterogeneous ...
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10/19/2012
On Information Regularization
We formulate a principle for classification with the knowledge of the ma...
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06/20/2012
Convergent Propagation Algorithms via Oriented Trees
Inference problems in graphical models are often approximated by casting...
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06/13/2012