
Fewshot Conformal Prediction with Auxiliary Tasks
We develop a novel approach to conformal prediction when the target task...
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Modeling Drug Combinations based on Molecular Structures and Biological Targets
Drug combinations play an important role in therapeutics due to its bett...
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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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Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels
Providing a small set of promising candidates in place of a single predi...
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Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under differe...
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Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum ...
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Domain Extrapolation via Regret Minimization
Many real prediction tasks such as molecular property prediction require...
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Adaptive Invariance for Molecule Property Prediction
Effective property prediction methods can help accelerate the search for...
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The Benefits of Pairwise Discriminators for Adversarial Training
Adversarial training methods typically align distributions by solving tw...
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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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Improving Molecular Design by Stochastic Iterative Target Augmentation
Generative models in molecular design tend to be richly parameterized, d...
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Composing Molecules with Multiple Property Constraints
Drug discovery aims to find novel compounds with specified chemical prop...
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Hierarchical Generation of Molecular Graphs using Structural Motifs
Graph generation techniques are increasingly being adopted for drug disc...
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Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences ...
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Latent Space Secrets of Denoising TextAutoencoders
While neural language models have recently demonstrated impressive perfo...
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PathAugmented Graph Transformer Network
Much of the recent work on learning molecular representations has been b...
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Strategic Prediction with Latent Aggregative Games
We introduce a new class of context dependent, incomplete information ga...
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Solving graph compression via optimal transport
We propose a new approach to graph compression by appeal to optimal tran...
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Are Learned Molecular Representations Ready For Prime Time?
Advancements in neural machinery have led to a wide range of algorithmic...
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Alignment Based Matching Networks for OneShot Classification and OpenSet Recognition
Deep learning for object classification relies heavily on convolutional ...
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Learning Multimodal GraphtoGraph Translation for Molecular Optimization
We view molecular optimization as a graphtograph translation problem. ...
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The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Hierarchical Bayesian methods can unify many related tasks (e.g. kshot ...
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Direct Optimization through for Discrete Variational AutoEncoder
Reparameterization of variational autoencoders with continuous latent s...
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Junction Tree Variational Autoencoder for Molecular Graph Generation
We seek to automate the design of molecules based on specific chemical p...
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Predicting Organic Reaction Outcomes with WeisfeilerLehman Network
The prediction of organic reaction outcomes is a fundamental problem in ...
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Deep Transfer in Reinforcement Learning by Language Grounding
In this paper, we explore the utilization of natural language to drive t...
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Style Transfer from NonParallel Text by CrossAlignment
This paper focuses on style transfer on the basis of nonparallel text. ...
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Deriving Neural Architectures from Sequence and Graph Kernels
The design of neural architectures for structured objects is typically g...
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Aspectaugmented Adversarial Networks for Domain Adaptation
We introduce a neural method for transfer learning between two (source a...
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Learning Optimal Interventions
Our goal is to identify beneficial interventions from observational data...
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Rationalizing Neural Predictions
Prediction without justification has limited applicability. As a remedy,...
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High Dimensional Inference with Random Maximum APosteriori Perturbations
This paper presents a new approach, called perturbmax, for highdimensi...
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Statistical Learning under Nonstationary Mixing Processes
We study a special case of the problem of statistical learning without t...
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Semisupervised Question Retrieval with Gated Convolutions
Question answering forums are rapidly growing in size with no effective ...
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Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
We introduce principal differences analysis (PDA) for analyzing differen...
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Steps Toward Deep Kernel Methods from Infinite Neural Networks
Contemporary deep neural networks exhibit impressive results on practica...
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Molding CNNs for text: nonlinear, nonconsecutive convolutions
The success of deep learning often derives from wellchosen operational ...
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Structured Prediction: From Gaussian Perturbations to LinearTime Principled Algorithms
Marginbased structured prediction commonly uses a maximum loss over all...
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CRAFT: ClusteRspecific Assorted Feature selecTion
We present a framework for clustering with clusterspecific feature sele...
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An Unsupervised Method for Uncovering Morphological Chains
Most stateoftheart systems today produce morphological analysis based...
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Proceedings of the TwentyFirst Conference on Uncertainty in Artificial Intelligence (2005)
This is the Proceedings of the TwentyFirst Conference on Uncertainty in...
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On the Statistical Efficiency of ℓ_1,p MultiTask Learning of Gaussian Graphical Models
In this paper, we present ℓ_1,p multitask structure learning for Gaussi...
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On the Partition Function and Random Maximum APosteriori Perturbations
In this paper we relate the partition function to the maxstatistics of ...
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Tommi Jaakkola
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Professor of Electrical Engineering and Computer Science at Massachusetts Institute of Technology