
NonStationary Latent Bandits
Users of recommender systems often behave in a nonstationary fashion, d...
read it

Latent Programmer: Discrete Latent Codes for Program Synthesis
In many sequence learning tasks, such as program synthesis and document ...
read it

Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
read it

Federated Composite Optimization
Federated Learning (FL) is a distributed learning paradigm which scales ...
read it

Scalable BottomUp Hierarchical Clustering
Bottomup algorithms such as the classic hierarchical agglomerative clus...
read it

Probabilistic Casebased Reasoning for OpenWorld Knowledge Graph Completion
A casebased reasoning (CBR) system solves a new problem by retrieving `...
read it

Unsupervised Abstractive Dialogue Summarization for TeteaTetes
Highquality dialoguesummary paired data is expensive to produce and do...
read it

Revisiting LSTM Networks for SemiSupervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text ...
read it

Big Bird: Transformers for Longer Sequences
Transformersbased models, such as BERT, have been one of the most succe...
read it

A Simple Approach to CaseBased Reasoning in Knowledge Bases
We present a surprisingly simple yet accurate approach to reasoning in k...
read it

Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm...
read it

PiecewiseStationary OffPolicy Optimization
Offpolicy learning is a framework for evaluating and optimizing policie...
read it

Differentiable MetaLearning in Contextual Bandits
We study a contextual bandit setting where the learning agent has access...
read it

Robust LargeMargin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning ...
read it

Anchor Transform: Learning Sparse Representations of Discrete Objects
Learning continuous representations of discrete objects such as text, us...
read it

Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
read it

Towards Modular Algorithm Induction
We present a modular neural network architecture Main that learns algori...
read it

Differentiable Reasoning over a Virtual Knowledge Base
We consider the task of answering complex multihop questions using a co...
read it

Differentiable Bandit Exploration
We learn bandit policies that maximize the average reward over bandit in...
read it

FedDANE: A Federated NewtonType Method
Federated learning aims to jointly learn statistical models over massive...
read it

Multistep Entitycentric Information Retrieval for MultiHop Question Answering
Multihop question answering (QA) requires an information retrieval (IR)...
read it

Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generatin...
read it

The Myths of Our Time: Fake News
While the purpose of most fake news is misinformation and political prop...
read it

Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLMT...
read it

Multistep RetrieverReader Interaction for Scalable Opendomain Question Answering
This paper introduces a new framework for opendomain question answering...
read it

On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
read it

Hallucinating Point Cloud into 3D Sculptural Object
Our team of artists and machine learning researchers designed a creative...
read it

Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance ...
read it

Towards Gradient Free and Projection Free Stochastic Optimization
This paper focuses on the problem of constrainedstochastic optimization....
read it

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Open Domain Question Answering (QA) is evolving from complex pipelined s...
read it

Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation u...
read it

Investigating the Working of Text Classifiers
Text classification is one of the most widely studied task in natural la...
read it

Compressed Video Action Recognition
Training robust deep video representations has proven to be much more ch...
read it

State Space LSTM Models with Particle MCMC Inference
Long ShortTerm Memory (LSTM) is one of the most powerful sequence model...
read it

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...
read it

A Generic Approach for Escaping Saddle points
A central challenge to using firstorder methods for optimizing nonconve...
read it

Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
Existing question answering methods infer answers either from a knowledg...
read it

Spectral Methods for Nonparametric Models
Nonparametric models are versatile, albeit computationally expensive, to...
read it

Deep Sets
In this paper, we study the problem of designing objective functions for...
read it
Manzil Zaheer
is this you? claim profile