Machine Learning (stat)

Papers filed under stat.ML on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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3,121 to 3,180 of 6,777

Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
Emile Contal, David Buffoni, Alexandre Robicquet +1
cs.LGstat.MLarXiv:1304.5350v32013
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster +2
cs.LGstat.MLarXiv:2002.06103v32020
Dynamic Word Embeddings for Evolving Semantic Discovery
Zijun Yao, Yifan Sun, Weicong Ding +2
cs.CLstat.MLarXiv:1703.00607v22017
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
David Eriksson, Martin Jankowiak
cs.LGstat.MLarXiv:2103.00349v22021
Sharp Approximation Rates for Neural Networks with Affine Latent Parameterizations
Shijun Zhang
cs.LGstat.MLarXiv:2608.31157v12026
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Andrey Bogomolov, Bruno Lepri, Michela Ferron +3
cs.CYcs.LGphysics.data-anarXiv:1410.5816v12014
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Raef Bassily, Vitaly Feldman, Cristóbal Guzmán +1
cs.LGmath.OCstat.MLarXiv:2006.06914v12020
Structured Prediction Energy Networks
David Belanger, Andrew McCallum
cs.LGstat.MLarXiv:1511.06350v32015
Data Augmentation Using GANs
Fabio Henrique Kiyoiti dos Santos Tanaka, Claus Aranha
cs.LGstat.MLarXiv:1904.09135v12019
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Michael Kearns, Seth Neel, Aaron Roth +1
cs.LGstat.MLarXiv:1808.08166v12018
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow +5
cs.CLcs.LGstat.MLarXiv:2204.05832v12022
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
Dylan Slack, Sophie Hilgard, Sameer Singh +1
cs.LGstat.MLarXiv:2008.05030v42020
Multi-Information Source Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
stat.MLarXiv:1603.00389v22016
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Cynthia Rudin
stat.MLcs.LGarXiv:1811.10154v32018
Selection-Aware Stress Testing for Interactive Agents
Yang Xu, Chenang Li, Jiefu Zhang +3
cs.LGstat.APstat.MLarXiv:2608.30916v12026
Fully Supervised Speaker Diarization
Aonan Zhang, Quan Wang, Zhenyao Zhu +2
eess.AScs.LGstat.MLarXiv:1810.04719v72018
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, Lei Feng +3
cs.LGstat.MLarXiv:2002.08053v32020
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
Sangwoo Mo, Minsu Cho, Jinwoo Shin
cs.CVcs.LGstat.MLarXiv:2002.10964v22020
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Mathieu Cliche
cs.CLstat.MLarXiv:1704.06125v12017
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Madelon Hulsebos, Kevin Hu, Michiel Bakker +5
cs.LGcs.DBcs.IRarXiv:1905.10688v12019
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister +3
cs.LGcs.ROstat.MLarXiv:2006.14911v22020
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup +2
stat.MLcs.LGarXiv:1810.05546v52018
Reinforcement Learning for Integer Programming: Learning to Cut
Yunhao Tang, Shipra Agrawal, Yuri Faenza
cs.LGmath.OCstat.MLarXiv:1906.04859v32019
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Prakhar Ganesh, Yao Chen, Xin Lou +6
cs.LGstat.MLarXiv:2002.11985v22020
Symbolic Music Generation with Diffusion Models
Gautam Mittal, Jesse Engel, Curtis Hawthorne +1
cs.SDcs.LGeess.ASarXiv:2103.16091v22021
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
Yiping Lu, Zhuohan Li, Di He +5
cs.LGcs.CLstat.MLarXiv:1906.02762v12019
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines +1
stat.MLarXiv:1706.06691v12017
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia +3
cs.LGmath.OCstat.MLarXiv:1812.08305v32018
Solving Statistical Mechanics Using Variational Autoregressive Networks
Dian Wu, Lei Wang, Pan Zhang
cond-mat.stat-mechcond-mat.dis-nncs.LGarXiv:1809.10606v22018
Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
stat.COcs.DSstat.MLarXiv:1605.06423v32016
Exact covariance thresholding into connected components for large-scale Graphical Lasso
Rahul Mazumder, Trevor Hastie
stat.MLstat.COarXiv:1108.3829v22011
When the Martingale Never Stops Firing: Anytime-Valid Gating on Real Forecast Streams
Weijia Han, Lisha Qu
cs.LGstat.MEstat.MLarXiv:2608.30502v12026
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu, Csaba Szepesvari
cs.LGmath.OCstat.MLarXiv:2012.08507v22020
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Eric Laloy, Romain Hérault, John Lee +2
stat.MLphysics.geo-pharXiv:1710.09196v12017
Frequentist Consistency of Variational Bayes
Yixin Wang, David M. Blei
stat.MLcs.LGmath.STarXiv:1705.03439v32017
Does data interpolation contradict statistical optimality?
Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov
stat.MLcs.LGmath.STarXiv:1806.09471v12018
Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
stat.MLcs.LGarXiv:1406.2673v22014
Fine-grained Sentiment Classification using BERT
Manish Munikar, Sushil Shakya, Aakash Shrestha
cs.CLcs.LGstat.MLarXiv:1910.03474v12019
Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray, Ryan Prescott Adams
stat.COstat.MLarXiv:1006.0868v22010
Neural Architecture Search: Insights from 1000 Papers
Colin White, Mahmoud Safari, Rhea Sukthanker +5
cs.LGcs.AIstat.MLarXiv:2301.08727v22023
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
cs.AIcs.LGstat.MLarXiv:1804.06459v22018
Learning PDE Time-Stepping with Neural Cellular Automata
Esha Saha, Hao Wang
cs.LGstat.MLarXiv:2608.30328v12026
Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He +4
cs.LGstat.MLarXiv:1407.5599v42014
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Wenda Zhou, Victor Veitch, Morgane Austern +2
stat.MLcs.LGarXiv:1804.05862v32018
Learning Deep Disentangled Embeddings with the F-Statistic Loss
Karl Ridgeway, Michael C. Mozer
cs.LGcs.AIstat.MLarXiv:1802.05312v22018
The Statistical Complexity of Interactive Decision Making
Dylan J. Foster, Sham M. Kakade, Jian Qian +1
cs.LGmath.OCmath.STarXiv:2112.13487v32021
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
Benyamin Ghojogh, Mark Crowley
stat.MLcs.LGarXiv:1905.12787v22019
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Abien Fred Agarap
cs.NEcs.CRcs.LGarXiv:1709.03082v82017
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
cs.LGstat.MLarXiv:1806.09206v22018
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
cs.LGstat.MLarXiv:1906.02319v12019
When Can We Work in Embedding Space? What Text Embeddings Preserve
Simon Freyaldenhoven
econ.EMcs.CLstat.MLarXiv:2608.31059v12026
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Kibok Lee, Kimin Lee, Jinwoo Shin +1
cs.CVcs.LGstat.MLarXiv:1903.12648v32019
Adversarial Continual Learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra +2
cs.LGcs.AIcs.CVarXiv:2003.09553v22020
Uniform Sampling for Matrix Approximation
Michael B. Cohen, Yin Tat Lee, Cameron Musco +3
cs.DScs.LGstat.MLarXiv:1408.5099v12014
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber +1
stat.MLcs.LGarXiv:1902.09689v12019
Solving and Learning Nonlinear PDEs with Gaussian Processes
Yifan Chen, Bamdad Hosseini, Houman Owhadi +1
math.NAstat.MLarXiv:2103.12959v22021
Robust Estimation via Robust Gradient Estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan +1
stat.MLcs.AIcs.LGarXiv:1802.06485v22018
The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller +4
cs.LGcs.AIcs.CLarXiv:2006.13760v22020
Global Optimality Guarantees For Policy Gradient Methods
Jalaj Bhandari, Daniel Russo
cs.LGstat.MLarXiv:1906.01786v32019
Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu +1
cs.LGcs.AIcs.CLarXiv:2003.09772v12020

Machine Learning (stat)

Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Dynamic Word Embeddings for Evolving Semantic Discovery

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

Sharp Approximation Rates for Neural Networks with Affine Latent Parameterizations

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

Structured Prediction Energy Networks

Data Augmentation Using GANs

An Empirical Study of Rich Subgroup Fairness for Machine Learning

What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

Multi-Information Source Optimization

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

Selection-Aware Stress Testing for Interactive Agents

Fully Supervised Speaker Diarization

Progressive Identification of True Labels for Partial-Label Learning

Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

Sherlock: A Deep Learning Approach to Semantic Data Type Detection

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Reinforcement Learning for Integer Programming: Learning to Cut

Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

Symbolic Music Generation with Diffusion Models

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Solving Statistical Mechanics Using Variational Autoregressive Networks

Coresets for Scalable Bayesian Logistic Regression

Exact covariance thresholding into connected components for large-scale Graphical Lasso

When the Martingale Never Stops Firing: Anytime-Valid Gating on Real Forecast Streams

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Frequentist Consistency of Variational Bayes

Does data interpolation contradict statistical optimality?

Mondrian Forests: Efficient Online Random Forests

Fine-grained Sentiment Classification using BERT

Slice sampling covariance hyperparameters of latent Gaussian models

Neural Architecture Search: Insights from 1000 Papers

On Learning Intrinsic Rewards for Policy Gradient Methods

Learning PDE Time-Stepping with Neural Cellular Automata

Scalable Kernel Methods via Doubly Stochastic Gradients

Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach

Learning Deep Disentangled Embeddings with the F-Statistic Loss

The Statistical Complexity of Interactive Decision Making

The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

When Can We Work in Embedding Space? What Text Embeddings Preserve

Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild

Adversarial Continual Learning

Uniform Sampling for Matrix Approximation

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Solving and Learning Nonlinear PDEs with Gaussian Processes

Robust Estimation via Robust Gradient Estimation

The NetHack Learning Environment

Global Optimality Guarantees For Policy Gradient Methods

Invariant Rationalization