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04/10/2023
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
Diffusion models that are based on iterative denoising have been recentl...
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04/10/2023
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
This work proposes POMP, a prompt pre-training method for vision-languag...
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02/06/2023
RLSbench: Domain Adaptation Under Relaxed Label Shift
Despite the emergence of principled methods for domain adaptation under ...
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02/02/2023
Multimodal Chain-of-Thought Reasoning in Language Models
Large language models (LLMs) have shown impressive performance on comple...
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01/04/2023
Parameter-Efficient Fine-Tuning Design Spaces
Parameter-efficient fine-tuning aims to achieve performance comparable t...
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10/07/2022
Automatic Chain of Thought Prompting in Large Language Models
Large language models (LLMs) can perform complex reasoning by generating...
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07/04/2022
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
Existing out-of-distribution (OOD) detection methods are typically bench...
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11/01/2021
Mixture Proportion Estimation and PU Learning: A Modern Approach
Given only positive examples and unlabeled examples (from both positive ...
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04/07/2021
Graph Reordering for Cache-Efficient Near Neighbor Search
Graph search is one of the most successful algorithmic trends in near ne...
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05/16/2020
Tiering as a Stochastic Submodular Optimization Problem
Tiering is an essential technique for building large-scale information r...
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09/11/2019
Recognizing Variables from their Data via Deep Embeddings of Distributions
A key obstacle in automated analytics and meta-learning is the inability...
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05/28/2019
Deep Factors for Forecasting
Producing probabilistic forecasts for large collections of similar and/o...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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11/30/2018
Deep Factors with Gaussian Processes for Forecasting
A large collection of time series poses significant challenges for class...
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06/04/2018
Deep Graphs
We propose an algorithm for deep learning on networks and graphs. It rel...
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02/12/2018
Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to ...
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11/15/2017
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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02/14/2017
Efficient Multi-task Feature and Relationship Learning
In this paper we propose a multi-convex framework for multi-task learnin...
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11/14/2016
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
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08/24/2016
AIDE: Fast and Communication Efficient Distributed Optimization
In this paper, we present two new communication-efficient methods for di...
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07/27/2016
Stochastic Frank-Wolfe Methods for Nonconvex Optimization
We study Frank-Wolfe methods for nonconvex stochastic and finite-sum opt...
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07/18/2016
Neural Machine Translation with Recurrent Attention Modeling
Knowing which words have been attended to in previous time steps while g...
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05/23/2016
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
We analyze stochastic algorithms for optimizing nonconvex, nonsmooth fin...
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03/19/2016
Stochastic Variance Reduction for Nonconvex Optimization
We study nonconvex finite-sum problems and analyze stochastic variance r...
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03/19/2016
Fast Incremental Method for Nonconvex Optimization
We analyze a fast incremental aggregated gradient method for optimizing ...
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12/15/2015
Data Driven Resource Allocation for Distributed Learning
In distributed machine learning, data is dispatched to multiple machines...
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11/07/2015
Stacked Attention Networks for Image Question Answering
This paper presents stacked attention networks (SANs) that learn to answ...
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06/23/2015
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
We study optimization algorithms based on variance reduction for stochas...
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02/26/2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
We consider the problem of Bayesian learning on sensitive datasets and p...
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12/22/2014
Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
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10/28/2014
Trend Filtering on Graphs
We introduce a family of adaptive estimators on graphs, based on penaliz...
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05/03/2014
The Falling Factorial Basis and Its Statistical Applications
We study a novel spline-like basis, which we name the "falling factorial...
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02/01/2014
Randomized Nonlinear Component Analysis
Classical methods such as Principal Component Analysis (PCA) and Canonic...
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07/11/2012
Exponential Families for Conditional Random Fields
In this paper we de ne conditional random elds in reproducing kernel Hil...
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06/27/2012
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
This paper analyzes the problem of Gaussian process (GP) bandits with de...
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03/15/2012
Super-Samples from Kernel Herding
We extend the herding algorithm to continuous spaces by using the kernel...
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03/09/2012
