
Casebased Reasoning for Natural Language Queries over Knowledge Bases
It is often challenging for a system to solve a new complex problem from...
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Exact and Approximate Hierarchical Clustering Using A*
Hierarchical clustering is a critical task in numerous domains. Many app...
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ModelAgnostic Graph Regularization for FewShot Learning
In many domains, relationships between categories are encoded in the kno...
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MetaThompson Sampling
Efficient exploration in multiarmed bandits is a fundamental online lea...
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NonStationary Latent Bandits
Users of recommender systems often behave in a nonstationary fashion, d...
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Latent Programmer: Discrete Latent Codes for Program Synthesis
In many sequence learning tasks, such as program synthesis and document ...
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Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
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Federated Composite Optimization
Federated Learning (FL) is a distributed learning paradigm which scales ...
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Scalable BottomUp Hierarchical Clustering
Bottomup algorithms such as the classic hierarchical agglomerative clus...
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Probabilistic Casebased Reasoning for OpenWorld Knowledge Graph Completion
A casebased reasoning (CBR) system solves a new problem by retrieving `...
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Unsupervised Abstractive Dialogue Summarization for TeteaTetes
Highquality dialoguesummary paired data is expensive to produce and do...
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Revisiting LSTM Networks for SemiSupervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text ...
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Big Bird: Transformers for Longer Sequences
Transformersbased models, such as BERT, have been one of the most succe...
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A Simple Approach to CaseBased Reasoning in Knowledge Bases
We present a surprisingly simple yet accurate approach to reasoning in k...
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Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm...
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PiecewiseStationary OffPolicy Optimization
Offpolicy learning is a framework for evaluating and optimizing policie...
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Differentiable MetaLearning in Contextual Bandits
We study a contextual bandit setting where the learning agent has access...
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Robust LargeMargin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning ...
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Anchor Transform: Learning Sparse Representations of Discrete Objects
Learning continuous representations of discrete objects such as text, us...
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Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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Towards Modular Algorithm Induction
We present a modular neural network architecture Main that learns algori...
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Differentiable Reasoning over a Virtual Knowledge Base
We consider the task of answering complex multihop questions using a co...
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Differentiable Bandit Exploration
We learn bandit policies that maximize the average reward over bandit in...
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FedDANE: A Federated NewtonType Method
Federated learning aims to jointly learn statistical models over massive...
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Multistep Entitycentric Information Retrieval for MultiHop Question Answering
Multihop question answering (QA) requires an information retrieval (IR)...
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Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generatin...
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The Myths of Our Time: Fake News
While the purpose of most fake news is misinformation and political prop...
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Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLMT...
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Multistep RetrieverReader Interaction for Scalable Opendomain Question Answering
This paper introduces a new framework for opendomain question answering...
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On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
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Hallucinating Point Cloud into 3D Sculptural Object
Our team of artists and machine learning researchers designed a creative...
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Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance ...
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Towards Gradient Free and Projection Free Stochastic Optimization
This paper focuses on the problem of constrainedstochastic optimization....
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Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Open Domain Question Answering (QA) is evolving from complex pipelined s...
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Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation u...
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Investigating the Working of Text Classifiers
Text classification is one of the most widely studied task in natural la...
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Compressed Video Action Recognition
Training robust deep video representations has proven to be much more ch...
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State Space LSTM Models with Particle MCMC Inference
Long ShortTerm Memory (LSTM) is one of the most powerful sequence model...
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Knowledge bases (KB), both automatically and manually constructed, are o...
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A Generic Approach for Escaping Saddle points
A central challenge to using firstorder methods for optimizing nonconve...
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Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
Existing question answering methods infer answers either from a knowledg...
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Spectral Methods for Nonparametric Models
Nonparametric models are versatile, albeit computationally expensive, to...
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Deep Sets
In this paper, we study the problem of designing objective functions for...
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Manzil Zaheer
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