Search Results for author:
Found 37 papers, 18 papers with code
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
Real-world deployment of machine learning models is challenging because data evolves over time.
Faster Deep Reinforcement Learning with Slower Online Network
In this paper we endow two popular deep reinforcement learning algorithms, namely DQN and Rainbow, with updates that incentivize the online network to remain in the proximity of the target network.
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well.
Ranked #2 on
Binary Classification
on kickstarter
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
Dive into Deep Learning
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code.
Flexible Model Aggregation for Quantile Regression
Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being overly reductive.
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration.
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity.
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators.
TraDE: Transformers for Density Estimation
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.
Transformer on a Diet
Transformer has been widely used thanks to its ability to capture sequence information in an efficient way.
Meta-Q-Learning
This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL).
P3O: Policy-on Policy-off Policy Optimization
Extensive experiments on the Atari-2600 and MuJoCo benchmark suites show that this simple technique is effective in reducing the sample complexity of state-of-the-art algorithms.
Language Models with Transformers
In this paper, we explore effective Transformer architectures for language model, including adding additional LSTM layers to better capture the sequential context while still keeping the computation efficient.
Ranked #2 on
Language Modelling
on Penn Treebank (Word Level)
(using extra training data)
Compressed Video Action Recognition
), we propose to train a deep network directly on the compressed video.
Ranked #46 on
Action Classification
on Charades
(using extra training data)
State Space LSTM Models with Particle MCMC Inference
Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.
A Generic Approach for Escaping Saddle points
A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points.
Latent LSTM Allocation: Joint clustering and non-linear dynamic modeling of sequence data
Recurrent neural networks, such as long-short term memory (LSTM) networks, are powerful tools for modeling sequential data like user browsing history (Tan et al., 2016; Korpusik et al., 2016) or natural language text (Mikolov et al., 2010).
Sampling Matters in Deep Embedding Learning
In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.
Ranked #5 on
Image Retrieval
on CARS196
Spectral Methods for Nonparametric Models
Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models.
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices.
Recurrent Recommender Networks
Recommender systems traditionally assume that user profiles and movie attributes are static.
Variance Reduction in Stochastic Gradient Langevin Dynamics
In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient.
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization
We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex.
Attributing Hacks
That is, given properties of sites and the temporal occurrence of attacks, we are able to attribute individual attacks to joint causes and vulnerabilities, as well as estimating the evolution of these vulnerabilities over time.
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
With this novel technique we propose a new Bayesian model for joint collaborative filtering of ratings and text reviews through a sum of simple co-clusterings.
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
We study distributed stochastic convex optimization under the delayed gradient model where the server nodes perform parameter updates, while the worker nodes compute stochastic gradients.
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
Distributed computing excels at processing large scale data, but the communication cost for synchronizing the shared parameters may slow down the overall performance.
Fast Differentially Private Matrix Factorization
Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed.
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data.
A la Carte - Learning Fast Kernels
Kernel methods have great promise for learning rich statistical representations of large modern datasets.
Communication Efficient Distributed Machine Learning with the Parameter Server
This paper describes a third-generation parameter server framework for distributed machine learning.
Spectral Methods for Indian Buffet Process Inference
The Indian Buffet Process is a versatile statistical tool for modeling distributions over binary matrices.
Variance Reduction for Stochastic Gradient Optimization
We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization.
