
Scaling up ContinuousTime Markov Chains Helps Resolve Underspecification
Modeling the time evolution of discrete sets of items (e.g., genetic mut...
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What training reveals about neural network complexity
This work explores the hypothesis that the complexity of the function a ...
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Measuring Generalization with Optimal Transport
Understanding the generalization of deep neural networks is one of the m...
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Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Graph Neural Networks (GNNs) have been studied through the lens of expre...
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Counting Substructures with HigherOrder Graph Neural Networks: Possibility and Impossibility Results
While massage passing based Graph Neural Networks (GNNs) have become inc...
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Contrastive Learning with Hard Negative Samples
We consider the question: how can you sample good negative examples for ...
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Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
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Testing Determinantal Point Processes
Determinantal point processes (DPPs) are popular probabilistic models of...
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Estimating Generalization under Distribution Shifts via DomainInvariant Representations
When machine learning models are deployed on a test distribution differe...
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Debiased Contrastive Learning
A prominent technique for selfsupervised representation learning has be...
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IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentra...
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Distributionally Robust Bayesian Optimization
Robustness to distributional shift is one of the key challenges of conte...
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Strength from Weakness: Fast Learning Using Weak Supervision
We study generalization properties of weakly supervised learning. That i...
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Generalization and Representational Limits of Graph Neural Networks
We address two fundamental questions about graph neural networks (GNNs)....
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On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions
Recent advances in randomized incremental methods for minimizing Lsmoot...
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Adaptive Sampling for Stochastic RiskAverse Learning
We consider the problem of training machine learning models in a riskav...
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The Role of Embedding Complexity in Domaininvariant Representations
Unsupervised domain adaptation aims to generalize the hypothesis trained...
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Flexible Modeling of Diversity with Strongly LogConcave Distributions
Strongly logconcave (SLC) distributions are a rich class of discrete pr...
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Are Girls Neko or Shōjo? CrossLingual Alignment of NonIsomorphic Embeddings with Iterative Normalization
Crosslingual word embeddings (CLWE) underlie many multilingual natural ...
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What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
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Minimizing approximately submodular functions
The problem of minimizing a submodular function is well studied; several...
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Distributionally Robust Optimization and Generalization in Kernel Methods
Distributionally robust optimization (DRO) has attracted attention in ma...
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Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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Inorganic Materials Synthesis Planning with LiteratureTrained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of ma...
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Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimiza...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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Discrete Sampling using Semigradientbased Product Mixtures
We consider the problem of inference in discrete probabilistic models, t...
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ResNet with oneneuron hidden layers is a Universal Approximator
We demonstrate that a very deep ResNet with stacked modules with one neu...
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Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences be...
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Representation Learning on Graphs with Jumping Knowledge Networks
Recent deep learning approaches for representation learning on graphs fo...
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Robust GANs against Dishonest Adversaries
Robustness of deep learning models is a property that has recently gaine...
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Distributionally Robust Submodular Maximization
Submodular functions have applications throughout machine learning, but ...
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Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for a...
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GraphSparse Logistic Regression
We introduce GraphSparse Logistic Regression, a new algorithm for class...
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Ensemble Bayesian Optimization
Bayesian Optimization (BO) has been shown to be a very effective paradig...
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Parallel Streaming Wasserstein Barycenters
Efficiently aggregating data from different sources is a challenging pro...
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Polynomial Time Algorithms for Dual Volume Sampling
We study dual volume sampling, a method for selecting k columns from an ...
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Batched Highdimensional Bayesian Optimization via Structural Kernel Learning
Optimization of highdimensional blackbox functions is an extremely cha...
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Maxvalue Entropy Search for Efficient Bayesian Optimization
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and ...
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Robust Budget Allocation via Continuous Submodular Functions
The optimal allocation of resources for maximizing influence, spread of ...
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Deep Metric Learning via Facility Location
Learning the representation and the similarity metric in an endtoend f...
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Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
We study probability measures induced by set functions with constraints....
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Focused ModelLearning and Planning for NonGaussian Continuous StateAction Systems
We introduce a framework for model learning and planning in stochastic d...
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Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes
In this note we consider sampling from (nonhomogeneous) strongly Raylei...
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Gauss quadrature for matrix inverse forms with applications
We present a framework for accelerating a spectrum of machine learning a...
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Optimization as Estimation with Gaussian Processes in Bandit Settings
Recently, there has been rising interest in Bayesian optimization  the...
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Convex Optimization for Parallel Energy Minimization
Energy minimization has been an intensely studied core problem in comput...
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Submodular meets Structured: Finding Diverse Subsets in ExponentiallyLarge Structured Item Sets
To cope with the high level of ambiguity faced in domains such as Comput...
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Weaklysupervised Discovery of Visual Pattern Configurations
The increasing prominence of weakly labeled data nurtures a growing dema...
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Stefanie Jegelka
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Assistant Professor at Massachusetts Institute of Technology (MIT)