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07/26/2023
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex t...
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05/20/2023
Can Public Large Language Models Help Private Cross-device Federated Learning?
We study (differentially) private federated learning (FL) of language mo...
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05/04/2023
Adaptive Selection of Anchor Items for CUR-based k-NN search with Cross-Encoders
Cross-encoder models, which jointly encode and score a query-item pair, ...
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03/27/2023
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining
Dual encoder models are ubiquitous in modern classification and retrieva...
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02/03/2023
ResMem: Learn what you can and memorize the rest
The impressive generalization performance of modern neural networks is a...
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01/27/2023
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art perf...
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12/09/2022
Multi-Task Off-Policy Learning from Bandit Feedback
Many practical applications, such as recommender systems and learning to...
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12/01/2022
Differentially Private Adaptive Optimization with Delayed Preconditioners
Privacy noise may negate the benefits of using adaptive optimizers in di...
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11/09/2022
Large Language Models with Controllable Working Memory
Large language models (LLMs) have led to a series of breakthroughs in na...
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10/31/2022
Learning to Navigate Wikipedia by Taking Random Walks
A fundamental ability of an intelligent web-based agent is seeking out a...
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10/23/2022
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Efficient k-nearest neighbor search is a fundamental task, foundational ...
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10/07/2022
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference res...
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10/06/2022
Generalization Properties of Retrieval-based Models
Many modern high-performing machine learning models such as GPT-3 primar...
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10/05/2022
A Fourier Approach to Mixture Learning
We revisit the problem of learning mixtures of spherical Gaussians. Give...
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06/21/2022
Questions Are All You Need to Train a Dense Passage Retriever
We introduce ART, a new corpus-level autoencoding approach for training ...
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05/23/2022
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Knowledge and language understanding of models evaluated through questio...
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04/07/2022
Compositional Generalization and Decomposition in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex t...
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02/22/2022
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
Question answering (QA) over real-world knowledge bases (KBs) is challen...
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02/12/2022
Private Adaptive Optimization with Side Information
Adaptive optimization methods have become the default solvers for many m...
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02/03/2022
Deep Hierarchy in Bandits
Mean rewards of actions are often correlated. The form of these correlat...
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02/02/2022
Robust Training of Neural Networks using Scale Invariant Architectures
In contrast to SGD, adaptive gradient methods like Adam allow robust tra...
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01/29/2022
A Context-Integrated Transformer-Based Neural Network for Auction Design
One of the central problems in auction design is developing an incentive...
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11/12/2021
Hierarchical Bayesian Bandits
Meta-, multi-task, and federated learning can be all viewed as solving s...
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10/19/2021
When in Doubt, Summon the Titans: Efficient Inference with Large Models
Scaling neural networks to "large" sizes, with billions of parameters, h...
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07/13/2021
No Regrets for Learning the Prior in Bandits
We propose AdaTS, a Thompson sampling algorithm that adapts sequentially...
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06/10/2021
Thompson Sampling with a Mixture Prior
We study Thompson sampling (TS) in online decision-making problems where...
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04/18/2021
Case-based 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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04/14/2021
Exact and Approximate Hierarchical Clustering Using A*
Hierarchical clustering is a critical task in numerous domains. Many app...
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02/14/2021
Model-Agnostic Graph Regularization for Few-Shot Learning
In many domains, relationships between categories are encoded in the kno...
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02/11/2021
Meta-Thompson Sampling
Efficient exploration in multi-armed bandits is a fundamental online lea...
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12/01/2020
Non-Stationary Latent Bandits
Users of recommender systems often behave in a non-stationary fashion, d...
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12/01/2020
Latent Programmer: Discrete Latent Codes for Program Synthesis
In many sequence learning tasks, such as program synthesis and document ...
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12/01/2020
Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
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11/17/2020
Federated Composite Optimization
Federated Learning (FL) is a distributed learning paradigm which scales ...
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10/22/2020
Scalable Bottom-Up Hierarchical Clustering
Bottom-up algorithms such as the classic hierarchical agglomerative clus...
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10/07/2020
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
A case-based reasoning (CBR) system solves a new problem by retrieving `...
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09/15/2020
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
High-quality dialogue-summary paired data is expensive to produce and do...
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09/08/2020
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text ...
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07/28/2020
Big Bird: Transformers for Longer Sequences
Transformers-based models, such as BERT, have been one of the most succe...
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06/25/2020
A Simple Approach to Case-Based Reasoning in Knowledge Bases
We present a surprisingly simple yet accurate approach to reasoning in k...
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06/15/2020
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm...
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06/15/2020
Piecewise-Stationary Off-Policy Optimization
Off-policy learning is a framework for evaluating and optimizing policie...
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06/09/2020
Differentiable Meta-Learning in Contextual Bandits
We study a contextual bandit setting where the learning agent has access...
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04/11/2020
Robust Large-Margin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning ...
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03/18/2020
Anchor Transform: Learning Sparse Representations of Discrete Objects
Learning continuous representations of discrete objects such as text, us...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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02/27/2020
Towards Modular Algorithm Induction
We present a modular neural network architecture Main that learns algori...
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02/25/2020
Differentiable Reasoning over a Virtual Knowledge Base
We consider the task of answering complex multi-hop questions using a co...
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02/17/2020
Differentiable Bandit Exploration
We learn bandit policies that maximize the average reward over bandit in...
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01/07/2020