ICMLSchedule2016
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Length of one talk:
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0:17:000:40:00
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beginningend
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Monday8:30AM8:40AMWelcome
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Monday8:40AM9:40AM
Invited Talk: Susan Athey, "Causal Inference for Policy Evaluation"
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Monday9:40AM10:20AMbreakbreakbreakbreakbreakbreakbreak
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Monday10:20AM12:21PMNeural Networks and Deep LearningReinforcement Learning
Optimization (Continuous)
Online Learning
Clustering
Bayesian Nonparametric Methods
Matrix Factorization / Neuroscience Applications
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Monday10:20AM10:37AM720:One-Shot Generalization in Deep Generative Models
Danilo Rezende;Shakir Mohamed;Ivo Danihelka;Karol Gregor;Daan Wierstra
Neural Networks and Deep Learning*; Approximate Inference; Latent Variable Models; Representation Learning; Unsupervised Learning
809:Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well?
Özgür Şimşek;Simón Algorta;Amit Kothiyal
Reinforcement Learning*
341:SDCA without Duality, Regularization, and Individual Convexity
Shai Shalev-Shwartz
Optimization (Continuous)*
383:Online Learning with Feedback Graphs Without the Graphs
Alon Cohen;Tamir Hazan;Tomer Koren
Online Learning*
409:Correlation Clustering and Biclustering with Locally Bounded Errors
Gregory Puleo;Olgica Milenkovic
Clustering*; Optimization (Combinatorial)
754:Mixed membership modelling with hierarchical CRMs
Gaurav Pandey;Ambedkar Dukkipati
Bayesian Nonparametric Methods*; Topic Models and Mixed Membership Models
845:The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai;Rina Barber
Feature Selection and Dimensionality Reduction*; Other Models and Methods
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Monday10:37AM10:54AM551:Learning to Generate with Memory
Chongxuan Li;Jun Zhu;Bo Zhang
Neural Networks and Deep Learning*; Latent Variable Models; Representation Learning; Unsupervised Learning
829:Opponent Modeling in Deep Reinforcement Learning
He He;Jordan Boyd-Graber;Kevin Kwok;Hal Daumé III
Reinforcement Learning*; Learning for Games; Neural Networks and Deep Learning
153:Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós;Alex Smola
Optimization (Continuous)*
995:Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis;Akshay Krishnamurthy;Robert Schapire
Online Learning*; Optimization (Combinatorial)
631:$K$-Means Clustering with Distributed Dimensions
Hu Ding;Yu Liu;Lingxiao Huang;Jian Li
Clustering*; Large Scale Learning and Big Data; Optimization (Combinatorial); Parallel and Distributed Learning; Unsupervised Learning
45:Hawkes Processes with Stochastic Excitations
Young Lee;Kar Wai Lim;Cheng Soon Ong
Bayesian Nonparametric Methods*; Economics and Finance; Other Models and Methods; Time-Series Analysis
533:A Simple and Provable Algorithm for Sparse CCA
Megasthenis Asteris;Anastasios Kyrillidis;Oluwasanmi Koyejo;Russell Poldrack
Feature Selection and Dimensionality Reduction*; Neuroscience; Sparsity and Compressed Sensing
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Monday10:54AM11:11AM1188:A Theory of Generative ConvNet
Jianwen Xie;Yang Lu;Song-Chun Zhu;Yingnian Wu
Neural Networks and Deep Learning*; Representation Learning
1242:Memory-based Control of Active Perception and Action in Minecraft
Junhyuk Oh;Valliappa Chockalingam;Satinder Singh;Honglak Lee
Reinforcement Learning*; Neural Networks and Deep Learning
315:Fast Rate Analysis of Some Stochastic Optimization Algorithms
Chao Qu;Huan Xu;Chong jin Ong
Optimization (Continuous)*
917:BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits Alexander Rakhlin;Karthik Sridharan Online Learning*; Optimization (Combinatorial)1164:Speeding up k-means by approximating Euclidean distances via block vectors
Thomas Bottesch;Thomas Bühler;Markus Kächele
Clustering*; Large Scale Learning and Big Data; Unsupervised Learning
1205:The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi;Matthew Hoffman;Matthew Johnson;Ryan Adams
Bayesian Nonparametric Methods*; Approximate Inference
292:Experimental Design on a Budget for Sparse Linear Models and Applications
Sathya Narayanan Ravi;Vamsi Ithapu;Sterling Johnson;Vikas Singh
Optimization (Combinatorial)*; Neuroscience
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Monday11:11AM11:30AMbreakbreakbreakbreakbreakbreakbreak
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Monday11:30AM11:47AM1054:Deconstructing the Ladder Network Architecture
Mohammad Pezeshki;Linxi Fan;Philémon Brakel;Aaron Courville;Yoshua Bengio
Neural Networks and Deep Learning*; Representation Learning
880:Graying the black box: Understanding DQNs
Tom Zahavy;Nir Ben-Zrihem;Shie Mannor
Reinforcement Learning*; Neural Networks and Deep Learning; Representation Learning
759:Black-box optimization with a politician
Sébastien Bubeck;Yin Tat Lee
Optimization (Continuous)*
198:Online Stochastic Linear Optimization under One-bit Feedback
Lijun Zhang;Tianbao Yang;Rong Jin;Yichi Xiao;Zhi-hua Zhou
Online Learning*; Optimization (Continuous); Supervised Learning
437:Fast k-means with accurate bounds
James Newling;François Fleuret
Clustering*
521:Markov Latent Feature Models
Aonan Zhang;John Paisley
Latent Variable Models*; Approximate Inference; Bayesian Nonparametric Methods
478:Representational Similarity Learning with Application to Brain Networks
Urvashi Oswal;Christopher Cox;Matthew Lambon-Ralph;Timothy Rogers;Robert Nowak
Sparsity and Compressed Sensing*; Metric Learning; Neuroscience; Transfer and Multi-Task Learning
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Monday11:47AM12:04PM544:Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Devansh Arpit;Yingbo Zhou;Bhargava Kota;Venu Govindaraju
Neural Networks and Deep Learning*; Representation Learning; Supervised Learning
629:Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan;Xi Chen;Rein Houthooft;John Schulman;Pieter Abbeel
Reinforcement Learning*; Neural Networks and Deep Learning; Planning and Control; Robotics
705:Starting Small - Learning with Adaptive Sample Sizes
Hadi Daneshmand;Aurelien Lucchi;Thomas Hofmann
Optimization (Continuous)*; Statistical Learning Theory
212:Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Tianbao Yang;Lijun Zhang;Rong Jin;Jinfeng Yi
Online Learning*; Optimization (Continuous)
64:k-variates++: more pluses in the k-means++
Richard Nock;Raphaël Canyasse;Roksana Boreli;Frank Nielsen
Clustering*; Computational Learning Theory; Online Learning; Parallel and Distributed Learning; Privacy, Anonymity, and Security
34:Diversity-Promoting Bayesian Learning of Latent Variable Models
Pengtao Xie;Jun Zhu;Eric Xing
Latent Variable Models*; Approximate Inference; Graphical Models
805:Dictionary Learning for Massive Matrix Factorization
Arthur Mensch;Julien Mairal;Bertrand Thirion;Gaël Varoquaux
Matrix Factorization and Related Topics*; Large Scale Learning and Big Data; Neuroscience; Recommender Systems; Sparsity and Compressed Sensing
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Monday12:04PM12:21PM516:Unitary Evolution Recurrent Neural Networks
Martin Arjovsky;Amar Shah;Yoshua Bengio
Neural Networks and Deep Learning*; Optimization (Continuous); Representation Learning
927:Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang;Tom Schaul;Matteo Hessel;Hado van Hasselt;Marc Lanctot;Nando de Freitas
Neural Networks and Deep Learning*; Reinforcement Learning
370:Primal-Dual Rates and Certificates
Celestine Dünner;Simone Forte;Martin Takac;Martin Jaggi
Optimization (Continuous)*; Sparsity and Compressed Sensing; Supervised Learning
199:Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Rodolphe Jenatton;Jim Huang;Cedric Archambeau
Online Learning*; Optimization (Continuous)
462:Compressive Spectral Clustering
Nicolas TREMBLAY;Gilles Puy;Rémi Gribonval;Pierre Vandergheynst
Clustering*; Network and Graph Analysis; Sparsity and Compressed Sensing
1264:Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein;Mingyuan Zhou;Blei David;Hanna Wallach
Topic Models and Mixed Membership Models*; Bayesian Nonparametric Methods; Computational Social Sciences; Graphs and Social Networks; Latent Variable Models; Unsupervised Learning
258:A Random Matrix Approach to Recurrent Neural Networks
Romain Couillet;Gilles Wainrib;Hafiz Tiomoko Ali;Harry Sevi
Neural Networks and Deep Learning*; Large Scale Learning and Big Data; Neuroscience; Statistical Learning Theory
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Monday2:00PM4:01PMNeural Networks and Deep Learning
Optimization / Online Learning
Machine Learning Applications
Matrix Factorization and Related Topics
Bandit Problems
Graphical Models
Transfer Learning / Learning Theory
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Monday2:00PM2:17PM76:End-to-End Speech Recognition in English and Mandarin
Dario Amodei;Rishita Anubhai;Eric Battenberg;Carl Case;Jared Casper;Bryan Catanzaro;JingDong Chen;Mike Chrzanowski;Adam Coates;Greg Diamos;Erich Elsen;Jesse Engel;Linxi Fan;Christopher Fougner;Awni Hannun;Billy Jun;Tony Han;Patrick LeGresley;Xiangang Li;Libby Lin;Sharan Narang;Andrew Ng;Sherjil Ozair;Ryan Prenger;Sheng Qian;Jonathan Raiman;Sanjeev Satheesh;David Seetapun;Shubho Sengupta;Chong Wang;Yi Wang;Zhiqian Wang;Bo Xiao;Yan Xie;Dani Yogatama;Jun Zhan;zhenyao Zhu
Speech Recognition*; Large Scale Learning and Big Data; Neural Networks and Deep Learning; Systems and Software
1318:Shifting Regret, Mirror Descent, and Matrices
András György;Csaba Szepesvari
Online Learning*
750:Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design
William Hoiles;Mihaela van der Schaar
Other Applications*; Recommender Systems
966:Complex Embeddings for Simple Link Prediction
Théo Trouillon;Johannes Welbl;Sebastian Riedel;Eric Gaussier;Guillaume Bouchard
Statistical Relational Learning*; Large Scale Learning and Big Data; Matrix Factorization and Related Topics; Ranking and Preference Learning; Recommender Systems; Spectral Methods
788:An optimal algorithm for the Thresholding Bandit Problem
Andrea LOCATELLI;Maurilio Gutzeit;Alexandra Carpentier
Active Learning*; Online Learning
174:Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams
Roy Adams;Nazir Saleheen;Edison Thomaz;Abhinav Parate;Santosh Kumar;Benjamin Marlin
Health Care*; Graphical Models; Structured Prediction
405:A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain;Amaury Habrard;François Laviolette;Emilie Morvant
Transfer and Multi-Task Learning*; Statistical Learning Theory
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Monday2:17PM2:34PM945:Persistent RNNs: Stashing Recurrent Weights On-Chip
Greg Diamos;Shubho Sengupta;Bryan Catanzaro;Mike Chrzanowski;Adam Coates;Erich Elsen;Jesse Engel;Awni Hannun;Sanjeev Satheesh
Neural Networks and Deep Learning*; Large Scale Learning and Big Data; Parallel and Distributed Learning; Speech Recognition; Systems and Software
344:Heteroscedastic Sequences: Beyond Gaussianity
Oren Anava;Shie Mannor
Online Learning*; Statistical Learning Theory; Time-Series Analysis
141:Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
Andrew Lan;Tom Goldstein;Richard Baraniuk;Christoph Studer
Other Applications*; Optimization (Continuous)
384:PAC learning of Probabilistic Automaton based on the Method of Moments
Hadrien Glaude;Olivier Pietquin
Spectral Methods*; Graphical Models; Matrix Factorization and Related Topics; Natural Language Processing; Reinforcement Learning; Statistical Learning Theory
443:Anytime Exploration for Multi-armed Bandits using Confidence Information
Kwang-Sung Jun;Robert Nowak
Active Learning*; Online Learning
1047:Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model
Xinze Guan;Raviv Raich;Weng-Keen Wong
Graphical Models*; Health Care; Latent Variable Models; Time-Series Analysis
1275:Domain Adaptation with Conditional Transferable Components
Mingming Gong;Kun Zhang;Tongliang Liu;Dacheng Tao;Clark Glymour;Bernhard Schölkopf
Transfer and Multi-Task Learning*
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Monday2:34PM2:51PM998:Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification
Kyuyeon Hwang;Wonyong Sung
Neural Networks and Deep Learning*; Online Learning; Resource Efficient Learning; Speech Recognition; Supervised Learning
140:Convergence of Stochastic Gradient Descent for PCA
Ohad Shamir
Optimization (Continuous)*; Online Learning; Statistical Learning Theory; Unsupervised Learning
1078:Estimating Cosmological Parameters from the Dark-Matter Distribution
Siamak Ravanbakhsh;Junier Oliva;Sebastian Fromenteau;Layne Price;Shirley Ho;Jeff Schneider;Barnabás Póczos
Other Applications*; Large Scale Learning and Big Data; Neural Networks and Deep Learning
717:Rich Component Analysis
Rong Ge;James Zou
Spectral Methods*; Computational Learning Theory; Latent Variable Models
746:Anytime optimal algorithms in stochastic multi-armed bandits
Rémy Degenne;Vianney Perchet
Online Learning*
1221:Topographical Features of High-Dimensional Categorical Data and Their Applications to Clustering
Chao Chen;Novi Quadrianto
Graphical Models*; Clustering; Structured Prediction
573:Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt;Ben Recht;Yoram Singer
Computational Learning Theory*; Neural Networks and Deep Learning; Optimization (Continuous); Statistical Learning Theory
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Monday2:51PM3:10PMbreakbreakbreakbreakbreakbreakbreak
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Monday3:10PM3:27PM335:Analysis of Deep Neural Networks with Extended Data Jacobian Matrix
Shengjie Wang;Abdel-rahman Mohamed;Rich Caruana;Jeff Bilmes;Matthai Plilipose;Matthew Richardson;Krzysztof Geras;Gregor Urban;Ozlem Aslan
Neural Networks and Deep Learning*
139:Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
Ohad Shamir
Optimization (Continuous)*; Computational Learning Theory; Unsupervised Learning
419:BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces
Shane Carr;Roman Garnett;Cynthia Lo
Gaussian Processes*; Optimization (Continuous); Other Applications; Sustainability, Climate, and Environment
220:Beyond CCA: Moment Matching for Multi-View Models
Anastasia Podosinnikova;Francis Bach;Simon Lacoste-Julien
Spectral Methods*; Feature Selection and Dimensionality Reduction; Latent Variable Models; Matrix Factorization and Related Topics
410:PAC Lower Bounds and Efficient Algorithms for The Max $K$-Armed Bandit Problem
Yahel David;Nahum Shimkin
Online Learning*; Reinforcement Learning
891:Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Qinliang Su;xuejun Liao;changyou Chen;Lawrence Carin
Graphical Models*; Latent Variable Models
138:Accurate Robust and Efficient Error Estimation for Decision Trees
Lixin Fan
Rule and Decision Tree Learning*; Statistical Learning Theory
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Monday3:27PM3:44PM1016:Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Wenling Shang;Kihyuk Sohn;Diogo Almeida;Honglak Lee
Neural Networks and Deep Learning*
1184:Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber;Elad Hazan;Chi Jin;Sham M. Kakade;Cameron Musco;Praneeth Netrapalli;Aaron Sidford
Optimization (Continuous)*; Computational Learning Theory; Feature Selection and Dimensionality Reduction; Large Scale Learning and Big Data; Online Learning; Spectral Methods
714:Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato;José Miguel Hernández-Lobato;Amar Shah;Ryan Adams
Gaussian Processes*; Approximate Inference; Information Theory; Optimization (Continuous); Other Applications; Resource Efficient Learning
1017:Isotonic Hawkes Processes
Yichen Wang;Bo Xie;Nan Du;Le Song
Time-Series Analysis; Recommender Systems*
582:Conservative Bandits
Yifan Wu;Roshan Shariff;Tor Lattimore;Csaba Szepesvári
Online Learning*; Reinforcement Learning
616:Collapsed Variational Inference for Sum-Product Networks
Han Zhao;Tameem Adel;Geoff Gordon;Brandon Amos
Graphical Models*; Approximate Inference; Latent Variable Models
58:The Teaching Dimension of Linear Learners
Ji Liu;Xiaojin Zhu;Hrag Ohannessian
Statistical Learning Theory*
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Monday3:44PM4:01PM806:Pixel Recurrent Neural Networks
Aäron van den Oord;Nal Kalchbrenner;Koray Kavukcuoglu
Neural Networks and Deep Learning*; Unsupervised Learning
678:Solving Ridge Regression using Sketched Preconditioned SVRG
Alon Gonen;Francesco Orabona;Shai Shalev-Shwartz
Optimization (Continuous)*; Spectral Methods
882:Pareto Frontier Learning with Expensive Correlated Objectives
Amar Shah;Zoubin Ghahramani
Optimization (Continuous)*; Active Learning; Gaussian Processes
692:Non-negative Matrix Factorization under Heavy Noise
Chiranjib Bhattacharya;Navin Goyal;Ravindran Kannan;Jagdeep Pani
Matrix Factorization and Related Topics*; Unsupervised Learning
770:No-Regret Algorithms for Heavy-Tailed Linear Bandits
Andres Munoz Medina;Scott Yang
Online Learning*; Reinforcement Learning; Statistical Learning Theory
1103:Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families which Allow Positive Dependencies
David Inouye;Pradeep Ravikumar;Inderjit S. Dhillon
Graphical Models*
330:Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini;Frank Nielsen;Richard Nock;Marcello Carioni
Semi-Supervised Learning*; Optimization (Continuous); Statistical Learning Theory; Supervised Learning
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Monday4:01PM4:15PMbreakbreakbreakbreakbreakbreakbreak
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Monday4:15PM6:16PMNeural Networks and Deep Learning I
Neural Networks and Deep Learning II (Computer Vision)
Approximate Inference
Metric and Manifold Learning / Kernel Methods
Statistical Learning Theory
Structured Prediction / Monte Carlo Methods
Online Learning
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Monday4:15PM4:32PM639:Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov;Vadim Lebedev;Andrea Vedaldi;Victor Lempitsky
Neural Networks and Deep Learning*
1335:Group Equivariant Convolutional Networks
Taco Cohen;Max Welling
Neural Networks and Deep Learning*; Computer Vision; Representation Learning
438:Boolean Matrix Factorization and Noisy Completion via Message Passing
Siamak Ravanbakhsh;Barnabás Póczos;Russell Greiner
Matrix Factorization and Related Topics*; Approximate Inference; Graphical Models; Recommender Systems; Unsupervised Learning
322:Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Ke Li;Jitendra Malik
Metric Learning*
898:Barron and Covers' Theory in Supervised Learning and Its Application to Lasso
Masanori Kawakita;Jun'ichi Takeuchi
Information Theory*; Statistical Learning Theory; Supervised Learning
881:The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram Friesen;Pedro Domingos
Structured Prediction*; Optimization (Continuous); Representation Learning
1154:Pricing a low-regret seller
Hoda Heidari;Mohammad Mahdian;Umar Syed;Sergei Vassilvitskii;Sadra Yazdanbod
Online Learning*; Learning and Game Theory
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Monday4:32PM4:49PM992:Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski;Michael Tschannen;Aleksandar Stanic;Philipp Grohs;Helmut Bölcskei
Neural Networks and Deep Learning*
554:Learning End-to-end Video Classification with Rank-Pooling
Basura Fernando;Stephen Gould
Computer Vision*; Neural Networks and Deep Learning
1342:Stochastic Discrete Clenshaw-Curtis Quadrature
Nico Piatkowski;Katharina Morik
Approximate Inference*; Graphical Models
1107:Geometric Mean Metric Learning
Pourya Zadeh;Reshad Hosseini;Suvrit Sra
Metric Learning*; Supervised Learning
299:Exact Exponent in Optimal Rates for Crowdsourcing
Chao Gao;Yu Lu;Dengyong Zhou
Statistical Learning Theory*; Spectral Methods
818:Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi;Mehrdad Mahdavi;Adrian Weller;David Sontag
Structured Prediction*; Approximate Inference; Graphical Models; Optimization (Combinatorial)
65:Multi-Player Bandits -- a Musical Chairs Approach
Jonathan Rosenski;Ohad Shamir;Liran Szlak
Online Learning*; Multi-Agent and Co-Operative Learning; Statistical Learning Theory
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Monday4:49PM5:06PM500:Deep Structured Energy Based Models for Anomaly Detection
Shuangfei Zhai;Yu Cheng;Weining Lu;Zhongfei Zhang
Neural Networks and Deep Learning*; Unsupervised Learning
209:Learning Physical Intuition of Block Towers by Example
Adam Lerer;Sam Gross;Rob Fergus
Computer Vision*; Learning for Games; Neural Networks and Deep Learning
1028:Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Tudor Achim;Ashish Sabharwal;Stefano Ermon
Approximate Inference*; Graphical Models
468:Low-rank tensor completion: a Riemannian manifold preconditioning approach
Hiroyuki Kasai;Bamdev Mishra
Optimization (Continuous)*; Large Scale Learning and Big Data; Matrix Factorization and Related Topics; Online Learning
1049:Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin;Raffaello Camoriano;Lorenzo Rosasco
Statistical Learning Theory*; Kernel Methods
1062:Evasion and Hardening of Tree Ensemble Classifiers
Alex Kantchelian;J. D. Tygar;Anthony Joseph
Ensemble Methods*; Privacy, Anonymity, and Security
581:Contextual Combinatorial Cascading Bandits
Shuai Li;Baoxiang Wang;Shengyu Zhang;Wei Chen
Online Learning*; Reinforcement Learning
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break 5:06PM5:25PMbreakbreakbreakbreakbreakbreakbreak
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Monday5:25PM5:42PM1361:Noisy Activation Functions
Caglar Gulcehre;Marcin Moczulski;Misha Denil;Yoshua Bengio
Neural Networks and Deep Learning*
257:Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu;Yandong Wen;Zhiding Yu;Meng Yang
Neural Networks and Deep Learning*; Computer Vision
146:Variable Elimination in the Fourier Domain
Yexiang Xue;Stefano Ermon;Ronan Le Bras;Carla Gomes;Bart Selman
Approximate Inference*; Optimization (Combinatorial)
128:The Variational Nystrom method for large-scale spectral problems
Max Vladymyrov;Miguel Carreira-Perpiñán
Manifold Learning*; Clustering; Large Scale Learning and Big Data; Optimization (Continuous); Spectral Methods
1032:Generalized Direct Change Estimation in Ising Model Structure
Farideh Fazayeli;Arindam Banerjee
Statistical Learning Theory*; Graphical Models
703:Importance Sampling Tree for Large-scale Empirical Expectation
Olivier Canévet;Cijo Jose;François Fleuret
Monte Carlo Methods*; Neural Networks and Deep Learning; Online Learning
579:Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Junpei Komiyama;Junya Honda;Hiroshi Nakagawa
Online Learning*; Information Theory; Ranking and Preference Learning; Statistical Learning Theory
31
Monday5:42PM5:59PM281:A Kronecker-factored approximate Fisher matrix for convolution layers
Roger Grosse;James Martens
Neural Networks and Deep Learning*; Optimization (Continuous)
279:Network Morphism
Tao Wei;Changhu Wang;Yong Rui;Chang Wen Chen
Neural Networks and Deep Learning*; Computer Vision
1167:Learning and Inference via Maximum Inner Product Search
Stephen Mussmann;Stefano Ermon
Approximate Inference*; Graphical Models
964:Fast DPP Sampling for Nystrom with Application to Kernel Methods
Chengtao Li;Stefanie Jegelka;Suvrit Sra
Kernel Methods*; Matrix Factorization and Related Topics; Monte Carlo Methods; Supervised Learning
766:Gaussian process nonparametric tensor estimator and its minimax optimality
Heishiro Kanagawa;Taiji Suzuki;Hayato Kobayashi;Nobuyuki Shimizu;Yukihiro Tagami
Statistical Learning Theory*; Bayesian Nonparametric Methods; Feature Selection and Dimensionality Reduction; Gaussian Processes; Kernel Methods
1043:Stratified Sampling Meets Machine Learning
Edo Liberty;Kevin Lang;Konstantin Shmakov
Systems and Software*; Large Scale Learning and Big Data; Other Applications; Other Models and Methods
566:DCM Bandits: Learning to Rank with Multiple Clicks
Sumeet Katariya;Branislav Kveton;Csaba Szepesvári;Zheng Wen
Ranking and Preference Learning*; Online Learning; Recommender Systems
32
Monday5:59PM6:16PM951:Recurrent Orthogonal Networks and Long-Memory Tasks
Mikael Henaff;Arthur Szlam;Yann LeCun
Neural Networks and Deep Learning*
129:MBA: Multi-Bias Non-linear Activation in Deep Neural Networks
Hongyang Li;Wanli Ouyang;Xiaogang Wang
Neural Networks and Deep Learning*; Computer Vision; Neuroscience; Representation Learning
429:Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation
David Wipf
Approximate Inference*; Feature Selection and Dimensionality Reduction; Latent Variable Models; Matrix Factorization and Related Topics; Sparsity and Compressed Sensing
1193:Computationally Efficient Nystr\{o}m Approximation using Fast Transforms
Si Si;Cho-Jui Hsieh;Inderjit S. Dhillon
Kernel Methods*; Large Scale Learning and Big Data; Spectral Methods
103:Minimum Regret Search for Single- and Multi-Task Optimization
Jan Hendrik Metzen
Optimization (Continuous)*; Gaussian Processes; Information Theory; Robotics; Transfer and Multi-Task Learning
1115:Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Yutian Chen;Zoubin Ghahramani
Monte Carlo Methods*; Approximate Inference; Large Scale Learning and Big Data
612:Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda;Balázs Szörényi;Shuai Li
Multi-Agent and Co-Operative Learning*; Clustering; Optimization (Continuous); Parallel and Distributed Learning; Recommender Systems
33
34
Tuesday8:30AM9:30AM
Invited Talk: Fei-Fei Li, "A Quest for Visual Intelligence in Computers"
35
Tuesday9:30AM9:47AM
Test of Time Award Presentation: "Dynamic topic models", David Blei and John Lafferty
36
Tuesday9:47AM10:30AMbreakbreakbreak
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Tuesday10:30AM12:31PMNeural Networks and Deep Learning
Reinforcement Learning
Optimization (Combinatorial)
Unsupervised Learning / Representation Learning
Sampling / Kernel Methods
Sparsity and Compressed Sensing
Approximate Inference
38
Tuesday10:30AM10:47AM608:Strongly-Typed Recurrent Neural Networks
David Balduzzi;Muhammad Ghifary
Neural Networks and Deep Learning*; Representation Learning
628:On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search
Piyush Khandelwal;Elad Liebman;Scott Niekum;Peter Stone
Reinforcement Learning*; Monte Carlo Methods; Planning and Control
1225:Algorithms for Optimizing the Ratio of Submodular Functions
Wenruo Bai;Rishabh Iyer;Kai Wei;Jeff Bilmes
Optimization (Combinatorial)*; Computer Vision; Feature Selection and Dimensionality Reduction; Information Retrieval
903:Nonparametric canonical correlation analysis
Tomer Michaeli;Weiran Wang;Karen Livescu
Representation Learning*; Spectral Methods
387:Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends
Christopher Tosh
Monte Carlo Methods*; Computational Learning Theory; Graphical Models
380:The Sample Complexity of Subspace Clustering with Missing Data
Daniel Pimentel-Alarcón;Robert Nowak
Sparsity and Compressed Sensing*; Clustering; Recommender Systems
168:Hierarchical Variational Models
Rajesh Ranganath;Dustin Tran;Blei David
Approximate Inference*
39
Tuesday10:47AM11:04AM971:A Convolutional Attention Network for Extreme Summarization of Source Code
Miltiadis Allamanis;Hao Peng;Charles Sutton
Neural Networks and Deep Learning*
1055:Generalization and Exploration via Randomized Value Functions
Ian Osband;Benjamin Van Roy;Zheng Wen
Reinforcement Learning*
1332:Horizontally Scalable Submodular Maximization
Mario Lucic;Olivier Bachem;Morteza Zadimoghaddam;Andreas Krause
Optimization (Combinatorial)*; Large Scale Learning and Big Data
66:The Information Sieve
Greg Ver Steeg;Aram Galstyan
Unsupervised Learning*; Information Theory; Other Models and Methods; Representation Learning
981:Pliable Rejection Sampling
Akram Erraqabi;Michal Valko;Alexandra Carpentier;Odalric Maillard
Active Learning*; Online Learning
1033:Robust Principal Component Analysis with Side Information
Kai-Yang Chiang;Cho-Jui Hsieh;Inderjit S. Dhillon
Sparsity and Compressed Sensing*; Feature Selection and Dimensionality Reduction; Matrix Factorization and Related Topics
178:A Variational Analysis of Stochastic Gradient Algorithms
Stephan Mandt;Matthew Hoffman;Blei David
Approximate Inference*
40
Tuesday11:04AM11:21AM665:Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Ankit Kumar;Ozan Irsoy;Peter Ondruska;Mohit Iyyer;James Bradbury;Ishaan Gulrajani;Victor Zhong;Romain Paulus;Richard Socher
Neural Networks and Deep Learning*; Natural Language Processing
312:Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang;Lihong Li
Reinforcement Learning*; Planning and Control
1015:Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Eric Balkanski;Baharan Mirzasoleiman;Andreas Krause;Yaron Singer
Optimization (Combinatorial)*; Information Retrieval; Optimization (Continuous)
1197:Gromov-Wasserstein Barycenters of Similarity Matrices
Gabriel Peyré;Marco Cuturi;Justin Solomon
Unsupervised Learning*; Graphs and Social Networks; Kernel Methods; Metric Learning; Optimization (Continuous)
1177:A Kernel Test of Goodness of Fit
Kacper Chwialkowski;Heiko Strathmann;Arthur Gretton
Kernel Methods*; Monte Carlo Methods; Other Models and Methods
304:Online Low-Rank Subspace Clustering by Explicit Basis Modeling
Jie Shen;Ping Li;Huan Xu
Sparsity and Compressed Sensing*
719:Black-Box Alpha Divergence Minimization
José Miguel Hernández-Lobato;Yingzhen Li;Mark Rowland;Thang Bui;Daniel Hernández-Lobato;Richard Turner
Approximate Inference*; Monte Carlo Methods
41
Tuesday11:21AM11:40AMbreakbreakbreakbreakbreakbreakbreak
42
Tuesday11:40AM11:57AM1067:Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong;Stephen Merity;Richard Socher
Neural Networks and Deep Learning*; Computer Vision
1307:Near Optimal Behavior via Approximate State Abstraction
David Abel;David Hershkowitz;Michael Littman
Reinforcement Learning*; Planning and Control
642:Fast Constrained Submodular Maximization: Personalized Data Summarization
Baharan Mirzasoleiman;Ashwinkumar Badanidiyuru;Amin Karbasi
Optimization (Combinatorial)*; Information Retrieval; Large Scale Learning and Big Data
1348:Learning Representations for Counterfactual Inference
Fredrik Johansson;Uri Shalit;David Sontag
Representation Learning*; Causal Inference; Feature Selection and Dimensionality Reduction; Transfer and Multi-Task Learning
142:A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation
Qiang liu;Jason Lee;Michael Jordan
Kernel Methods*; Neural Networks and Deep Learning
469:Provable Non-convex Phase Retrieval with Outliers: Median Truncated Wirtinger Flow
Huishuai Zhang;Yuejie Chi;Yingbin Liang
Sparsity and Compressed Sensing*; Optimization (Continuous)
1000:Variational inference for Monte Carlo objectives
Andriy Mnih;Danilo Rezende
Approximate Inference*; Latent Variable Models; Neural Networks and Deep Learning
43
Tuesday11:57AM12:14PM270: Supervised and semi-supervised text categorization using LSTM for region embeddings
Rie Johnson;Tong Zhang
Neural Networks and Deep Learning*; Natural Language Processing; Other Applications; Representation Learning
1322:Model-Free Trajectory Optimization for Reinforcement Learning of Motor Skills
Riad Akrour;Gerhard Neumann;Hany Abdulsamad;Abbas Abdolmaleki
Reinforcement Learning*; Planning and Control; Robotics
1104:A Box-Constrained Approach for Hard Permutation Problems
Cong Han Lim;Steve Wright
Optimization (Continuous)*; Optimization (Combinatorial)
62:Why Regularized Auto-Encoders learn Sparse Representation?
Devansh Arpit;Yingbo Zhou;Hung Ngo;Venu Govindaraju
Representation Learning*; Manifold Learning; Neural Networks and Deep Learning; Sparsity and Compressed Sensing; Unsupervised Learning
44:Additive Approximations in High Dimensional Regression via the SALSA
Kirthevasan Kandasamy;Yaoliang Yu
Kernel Methods*; Statistical Learning Theory; Supervised Learning
385:Estimating Structured Vector Autoregressive Models
Igor Melnyk;Arindam Banerjee
Sparsity and Compressed Sensing*; Statistical Learning Theory; Time-Series Analysis
479:Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal;Zoubin Ghahramani
Approximate Inference*; Gaussian Processes; Neural Networks and Deep Learning
44
Tuesday12:14PM12:31PM1316:PHOG: Probabilistic Model for Code
Pavol Bielik;Veselin Raychev;Martin Vechev
Other Models and Methods*; Systems and Software
1228:Model-Free Imitation Learning with Policy Optimization
Jonathan Ho;Jayesh Gupta;Stefano Ermon
Reinforcement Learning*; Planning and Control; Ranking and Preference Learning; Robotics
1031:A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery
Ian En-Hsu Yen;Xin Lin;Jiong Zhang;Pradeep Ravikumar;Inderjit S. Dhillon
Optimization (Continuous)*; Bioinformatics; Optimization (Combinatorial); Sparsity and Compressed Sensing; Unsupervised Learning
1210:Robust Random Cut Forest Based Anomaly Detection on Streams
Sudipto Guha;Nina Mishra;Gourav Roy;Okke Schrijvers
Unsupervised Learning*
336:Doubly Decomposing Nonparametric Tensor Regression
Masaaki Imaizumi;Kohei Hayashi
Matrix Factorization and Related Topics*; Bayesian Nonparametric Methods; Gaussian Processes; Kernel Methods; Statistical Learning Theory; Supervised Learning
1036:Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Huan Gui;Jiawei Han;Quanquan Gu
Matrix Factorization and Related Topics*; Sparsity and Compressed Sensing; Statistical Learning Theory
699:Auxiliary Deep Generative Models
Lars Maaløe;Casper Kaae Sønderby;Søren Kaae Sønderby;Ole Winther
Approximate Inference*; Neural Networks and Deep Learning; Semi-Supervised Learning; Unsupervised Learning
45
46
Tuesday2:00PM3:00PM
Invited Talk: Daniel Spielman, "Laplacian Matrices of Graphs: Algorithms and Applications."
47
Tuesday3:00PM3:40PMbreakbreakbreakbreakbreakbreakbreak
48
Tuesday3:40PM4:48PMNeural Networks and Deep Learning I
Neural Networks and Deep Learning II
Reinforcement Learning
Optimization (Continuous)
Matrix Factorization and Related Topics
Unsupervised Learning / Applications
Learning Theory
49
Tuesday3:40PM3:57PM599:Factored Temporal Sigmoid Belief Networks for Sequence Learning
Jiaming Song;Zhe Gan;Lawrence Carin
Neural Networks and Deep Learning*; Time-Series Analysis
23:Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang;William Cohen;Ruslan Salakhudinov
Semi-Supervised Learning*; Neural Networks and Deep Learning; Representation Learning
30:Inverse Optimal Control with Deep Networks via Policy Optimization
Chelsea Finn;Sergey Levine;Pieter Abbeel
Reinforcement Learning*
654:On the Statistical Limits of Convex Relaxations
Zhaoran Wang;Quanquan Gu;Han Liu
Optimization (Continuous)*; Sparsity and Compressed Sensing; Structured Prediction
1051:Principal Component Projection Without Principal Component Analysis
Roy Frostig;Cameron Musco;Christopher Musco;Aaron Sidford
Matrix Factorization and Related Topics*; Feature Selection and Dimensionality Reduction; Optimization (Continuous)
1023:Markov-modulated marked Poisson processes for check-in data
Jiangwei Pan;Vinayak Rao;Pankaj Agarwal;Alan Gelfand
Time-Series Analysis*; Clustering; Latent Variable Models; Topic Models and Mixed Membership Models; Unsupervised Learning
59:Truthful Univariate Estimators
Ioannis Caragiannis;Ariel Procaccia;Nisarg Shah
Learning and Mechanism Design*; Learning and Game Theory
50
Tuesday3:57PM4:14PM1122:Bidirectional Helmholtz Machines
Jörg Bornschein;Samira Shabanian;Asja Fischer;Yoshua Bengio
Neural Networks and Deep Learning*; Approximate Inference; Unsupervised Learning
1229:ADIOS: Architectures Deep In Output Space
Moustapha Cissé;Maruan Al-Shedivat;Samy Bengio
Neural Networks and Deep Learning*; Representation Learning; Supervised Learning
327:Smooth Imitation Learning
Hoang Le;Andrew Kang;Yisong Yue;Peter Carr
Reinforcement Learning*; Planning and Control; Robotics; Structured Prediction
1125:Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Jacob Abernethy;Elad Hazan
Optimization (Continuous)*; Monte Carlo Methods
1052:Recovery guarantee of weighted low-rank approximation via alternating minimization
Yuanzhi Li;Yingyu Liang;Andrej Risteski
Matrix Factorization and Related Topics*; Optimization (Continuous)
825:Hierarchical Compound Poisson Factorization
Mehmet Basbug;Barbara Engelhardt
Matrix Factorization and Related Topics*; Approximate Inference; Graphical Models; Latent Variable Models; Recommender Systems; Unsupervised Learning
1287:Fast Algorithms for Segmented Regression
Jayadev Acharya;Ilias Diakonikolas;Jerry Li;Ludwig Schmidt
Computational Learning Theory*; Statistical Learning Theory
51
Tuesday4:14PM4:31PM793:The Deep Neural Matrix Gaussian Process
Christos Louizos;Max Welling
Neural Networks and Deep Learning*; Gaussian Processes
231:Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie;Ross Girshick;Ali Farhadi
Neural Networks and Deep Learning*; Representation Learning; Unsupervised Learning
1274:Improving the Efficiency of Deep Reinforcement Learning with Normalized Advantage Functions and Synthetic Experience
Shixiang Gu;Timothy Lillicrap;Ilya Sutskever;Sergey Levine
Neural Networks and Deep Learning*; Reinforcement Learning
727:A ranking approach to global optimization
Cédric Malherbe;Emile Contal;Nicolas Vayatis
Optimization (Continuous)*; Active Learning; Ranking and Preference Learning
1269:Tensor Decomposition via Joint Matrix Schur Decomposition
Nicolò Colombo;Nikos Vlassis
Matrix Factorization and Related Topics*
487:Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
Sandhya Prabhakaran;Elham Azizi;Ambrose Carr;Dana Pe'er
Bioinformatics*; Clustering; Monte Carlo Methods; Other Applications; Unsupervised Learning
3:Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
Nihar Shah;Sivaraman Balakrishnan;Aditya Guntuboyina;Martin Wainwright
Information Theory*; Matrix Factorization and Related Topics; Ranking and Preference Learning; Recommender Systems; Statistical Learning Theory
52
Tuesday4:31PM4:48PM52:Dropout distillation
Samuel Rota Bulò;Lorenzo Porzi;Peter Kontschieder
Neural Networks and Deep Learning*; Approximate Inference; Ensemble Methods; Representation Learning; Statistical Learning Theory; Supervised Learning
943:Learning Convolutional Neural Networks for Graphs
Mathias Niepert;Mohamed Ahmed;Konstantin Kutzkov
Neural Networks and Deep Learning*; Kernel Methods; Network and Graph Analysis; Representation Learning; Supervised Learning
885:Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves;Timothy Lillicrap;Tim Harley;David Silver;Koray Kavukcuoglu
Neural Networks and Deep Learning*; Reinforcement Learning
1286:Epigraph projections for fast general convex programming
Po-Wei Wang;Matt Wytock;J. Zico Kolter
Optimization (Continuous)*
224:Fast Methods for Estimating the Numerical Rank of Large Matrices
Shashanka Ubaru;Yousef Saad
Matrix Factorization and Related Topics*; Feature Selection and Dimensionality Reduction
1349:The Automatic Statistician: A Relational Perspective
Yunseong Hwang;Anh Tong;Jaesik Choi
Statistical Relational Learning*; Gaussian Processes; Graphical Models
1279:Provable Algorithms for Inference in Topic Models
Sanjeev Arora;Rong Ge;Frederic Koehler;Tengyu Ma;Ankur Moitra
Computational Learning Theory*; Topic Models and Mixed Membership Models
53
Tuesday4:48PM5:10PMbreakbreakbreakbreakbreakbreakbreak
54
Tuesday5:10PM6:18PMNeural Networks and Deep Learning I
Neural Networks and Deep Learning II
Reinforcement Learning
Optimization (Continuous)
Large Scale Learning and Big Data
Graphical Models
Supervised Learning
55
Tuesday5:10PM5:27PM1096:Expressiveness of Rectifier Neural Network
Xingyuan Pan;Vivek Srikumar
Neural Networks and Deep Learning*; Computational Learning Theory
1345:Correcting Forecasts with Multi-force Neural Attention
Matthew Riemer;Aditya Vempaty;Flavio Calmon;Fenno Heath;Richard Hull;Elham Khabiri
Neural Networks and Deep Learning*; Representation Learning; Time-Series Analysis
470:Estimating Maximum Expected Value through Gaussian Approximation
Carlo D'Eramo;Marcello Restelli;Alessandro Nuara
Reinforcement Learning*
440:Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu;Ross Boczar;Max Simchowitz;mahdi Soltanolkotabi;Ben Recht
Optimization (Continuous)*; Matrix Factorization and Related Topics
698:Extreme F-measure Maximization using Sparse Probability Estimates
Kalina Jasinska;Krzysztof Dembczynski;Robert Busa-Fekete;Karlson Pfannschmidt;Timo Klerx;Eyke Hullermeier
Large Scale Learning and Big Data*; Structured Prediction; Supervised Learning
4:Uprooting and Rerooting Graphical Models
Adrian Weller
Graphical Models*; Approximate Inference; Optimization (Combinatorial); Structured Prediction
1040:Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier;Jérôme Gauthier;Alain Rakotomamonjy
Time-Series Analysis*; Other Models and Methods; Supervised Learning
56
Tuesday5:27PM5:44PM439:Convolutional Rectifier Networks as Generalized Tensor Decompositions
Nadav Cohen;Amnon Shashua
Neural Networks and Deep Learning*; Computational Learning Theory
839:Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro;Sergey Bartunov;Matthew Botvinick;Daan Wierstra;Timothy Lillicrap
Neural Networks and Deep Learning*; Other Applications; Supervised Learning; Transfer and Multi-Task Learning
985:Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Philip Thomas;Emma Brunskill
Reinforcement Learning*; Monte Carlo Methods
543:Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
Huikang Liu;Weijie Wu;Anthony Man-Cho So
Optimization (Continuous)*; Large Scale Learning and Big Data; Manifold Learning
823:Stochastic Optimization for Multiview Learning using Partial Least Squares
Raman Arora;Poorya Mianjy;Teodor Marinov
Large Scale Learning and Big Data*
210:Structure Learning of Partitioned Markov Networks
Song Liu;Taiji Suzuki;Masashi Sugiyama;Kenji Fukumizu
Graphical Models*; Graphs and Social Networks; Network and Graph Analysis; Statistical Learning Theory
1206:Meta--Gradient Boosted Decision Tree Model for Weight and Target Learning
Yury Ustinovskiy;Valentina Fedorova;Gleb Gusev;Pavel Serdyukov
Supervised Learning*; Information Retrieval; Optimization (Continuous); Ranking and Preference Learning; Rule and Decision Tree Learning; Transfer and Multi-Task Learning
57
Tuesday5:44PM6:01PM1278:Fixed Point Quantization of Deep Convolutional Networks
Darryl Lin;Sachin Talathi;Sreekanth Annapureddy
Neural Networks and Deep Learning*; Computer Vision; Optimization (Continuous); Representation Learning; Supervised Learning; Systems and Software
208:Learning Simple Algorithms from Examples
Wojciech Zaremba;Tomas Mikolov;Armand Joulin;Rob Fergus
Neural Networks and Deep Learning*; Reinforcement Learning
680:Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L.A.;Cheng Jie;Michael Fu;Steve Marcus;Csaba Szepesvári
Reinforcement Learning*
1224:Efficient Algorithms for Large-scale Generalized Eigenvector Computation and CCA
Rong Ge;Chi Jin;Sham M. Kakade;Praneeth Netrapalli;Aaron Sidford
Optimization (Continuous)*; Matrix Factorization and Related Topics; Spectral Methods
811:Gaussian quadrature for matrix inverse forms with applications
Chengtao Li;Suvrit Sra;Stefanie Jegelka
Large Scale Learning and Big Data*; Approximate Inference; Gaussian Processes; Graphs and Social Networks; Optimization (Continuous); Other Models and Methods
733:Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Christopher De Sa;Chris Re;Kunle Olukotun
Graphical Models*; Large Scale Learning and Big Data; Monte Carlo Methods; Parallel and Distributed Learning
866:Class Probability Estimation via Differential Geometric Regularization
Qinxun Bai;Steven Rosenberg;Zheng Wu;Stan Sclaroff
Supervised Learning*; Manifold Learning
58
Tuesday6:01PM6:18PM113:CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Ran Gilad-Bachrach;Nathan Dowlin;Kim Laine;Kristin Lauter;Michael Naehrig;John Wernsing
Neural Networks and Deep Learning*; Privacy, Anonymity, and Security
920:Associative Long Short-Term Memory
Ivo Danihelka;Greg Wayne;Benigno Uria;Nal Kalchbrenner;Alex Graves
Neural Networks and Deep Learning*; Natural Language Processing
854:Softened Approximate Policy Iteration for Markov Games
Julien Pérolat;Bilal Piot;Matthieu Geist;Bruno Scherrer;Olivier Pietquin
Reinforcement Learning*; Learning and Game Theory; Learning for Games; Multi-Agent and Co-Operative Learning; Optimization (Continuous)
783:Matrix Eigendecomposition via Doubly Stochastic Riemannian Optimization
Zhiqiang Xu;Peilin Zhao;Jianneng Cao;Xiaoli Li
Optimization (Continuous)*; Large Scale Learning and Big Data; Manifold Learning; Spectral Methods
563:A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling
Mostafa Rahmani;Geroge Atia
Large Scale Learning and Big Data*; Information Retrieval; Matrix Factorization and Related Topics; Sparsity and Compressed Sensing; Unsupervised Learning
1155:Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan;Roy Frostig;John Duchi;Percy Liang
Structured Prediction*; Graphical Models; Latent Variable Models
152:Linking losses for density ratio and class-probability estimation
Aditya Menon;Cheng Soon Ong
Supervised Learning*; Semi-Supervised Learning; Transfer and Multi-Task Learning
59
60
Wednesday8:30AM9:55AMNeural Networks and Deep Learning IOptimization (Continuous)
Multi-label, multi-task, and neural networks
Gaussian Processes
Feature Selection and Dimensionality Reduction
Graph Analysis/ Spectral Methods
Ranking and Preference Learning
61
Wednesday8:30AM8:47AM804:Neural Variational Inference for Text Processing
Yishu Miao;Lei Yu;Phil Blunsom
Natural Language Processing*; Approximate Inference; Latent Variable Models; Neural Networks and Deep Learning; Representation Learning; Unsupervised Learning
832:SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu;Peter Richtárik;Martin Takac;Olivier Fercoq
Optimization (Continuous)*; Large Scale Learning and Big Data; Supervised Learning
135:Asymmetric Multi-task Learning based on Task Relatedness and Confidence
Giwoong Lee;Eunho Yang;Sung ju Hwang
Transfer and Multi-Task Learning*; Computer Vision; Supervised Learning
273:Stability of Controllers for Gaussian Process Forward Models
Julia Vinogradska;Bastian Bischoff;Duy Nguyen-Tuong;Anne Romer;Henner Schmidt;Jan Peters
Gaussian Processes*; Planning and Control; Reinforcement Learning
93:On the Consistency of Feature Selection With Lasso for Non-linear Targets
Yue Zhang;Weihong Guo;Soumya Ray
Feature Selection and Dimensionality Reduction*; Supervised Learning
56:Metadata-conscious anonymous messaging
Giulia Fanti;Peter Kairouz;Sewoong Oh;Kannan Ramchandran;Pramod Viswanath
Privacy, Anonymity, and Security*; Network and Graph Analysis
1327:Controlling the distance to a Kemeny consensus without computing it
Anna Korba;Yunlong Jiao;Eric Sibony
Ranking and Preference Learning*
62
Wednesday8:47AM9:04AM22:A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham;Xiuyuan Cheng;Omer Dror;Ariel Jaffe;Boaz Nadler;Joseph Chang;Yuval Kluger
Ensemble Methods*; Approximate Inference; Neural Networks and Deep Learning; Unsupervised Learning
865:Stochastic Block BFGS: Squeezing More Curvature out of Data
Robert Gower;Donald Goldfarb;Peter Richtárik
Optimization (Continuous)*; Large Scale Learning and Big Data; Matrix Factorization and Related Topics; Online Learning
997:Training Deep Neural Networks via Direct Loss Minimization
Yang Song;Alexander Schwing;Richard S. Zemel;Raquel Urtasun
Neural Networks and Deep Learning*; Computer Vision
188:A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models
Trong Nghia Hoang;Quang Minh Hoang;Bryan Kian Hsiang Low
Gaussian Processes*; Bayesian Nonparametric Methods; Large Scale Learning and Big Data; Parallel and Distributed Learning
831:No penalty no tears: Least squares in high-dimensional linear models
Xiangyu Wang;David Dunson;Chenlei Leng
Feature Selection and Dimensionality Reduction*; Statistical Learning Theory
887:A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Nate Veldt;David Gleich;Michael Jordan
Network and Graph Analysis*; Semi-Supervised Learning; Spectral Methods
50:Data-driven Rank Breaking for Efficient Rank Aggregation
Ashish Khetan;Sewoong Oh
Ranking and Preference Learning*; Optimization (Continuous)
63
Wednesday9:04AM9:21AM755:From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
André Martins;Ramon Astudillo
Neural Networks and Deep Learning*; Natural Language Processing
1375:A Primal and Dual Sparse Approach to Extreme Classification
Ian En-Hsu Yen;Xiangru Huang;Pradeep Ravikumar;Kai Zhong;Inderjit S. Dhillon
Optimization (Continuous)*; Large Scale Learning and Big Data
445:Structured Prediction Energy Networks
David Belanger;Andrew McCallum
Structured Prediction*; Graphical Models; Neural Networks and Deep Learning
706:Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang Bui;José Miguel Hernández-Lobato;Daniel Hernández-Lobato;Yingzhen Li;Richard Turner
Gaussian Processes*; Approximate Inference
744:Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki;Masayuki Karasuyama;Kohei Hatano;Ichiro Takeuchi
Feature Selection and Dimensionality Reduction*; Large Scale Learning and Big Data; Optimization (Continuous); Sparsity and Compressed Sensing; Supervised Learning
328:Community Recovery in Graphs with Locality
Yuxin Chen;Govinda Kamath;Changho Suh;David Tse
Clustering*; Bioinformatics; Sparsity and Compressed Sensing; Spectral Methods; Statistical Learning Theory; Structured Prediction
253:Parameter Estimation for Generalized Thurstone Choice Models
Milan Vojnovic;Seyoung Yun
Ranking and Preference Learning*; Clustering; Information Theory; Optimization (Continuous); Recommender Systems
64
Wednesday9:21AM9:38AM351:A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng;Bangsheng Tang;Wenkui Ding;Hanning Zhou
Neural Networks and Deep Learning*; Recommender Systems
731:Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang;Veeranjaneyulu Sadhanala;Wei Dai;Willie Neiswanger;Suvrit Sra;Eric Xing
Optimization (Continuous)*; Parallel and Distributed Learning
1114:Conditional Bernoulli Mixtures for Multi-label Classification
Cheng Li;Bingyu Wang;Virgil Pavlu;Javed Aslam
Structured Prediction*; Latent Variable Models; Supervised Learning
1129:Preconditioning Kernel Matrices
Kurt Cutajar;Michael Osborne;John Cunningham;Maurizio Filippone
Gaussian Processes*; Approximate Inference; Kernel Methods; Optimization (Continuous)
1190:Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution
Quanming Yao;James Kwok
Feature Selection and Dimensionality Reduction*; Large Scale Learning and Big Data; Matrix Factorization and Related Topics; Optimization (Combinatorial); Structured Prediction
969:Interactive Bayesian Hierarchical Clustering
Sharad Vikram;Sanjoy Dasgupta
Clustering*; Active Learning; Approximate Inference; Bayesian Nonparametric Methods; Monte Carlo Methods; Unsupervised Learning
1306:Learning Mixtures of Plackett-Luce Models
Zhibing Zhao;Peter Piech;Lirong Xia
Ranking and Preference Learning*; Computational Learning Theory; Computational Social Sciences; Economics and Finance; Statistical Learning Theory
65
Wednesday9:38AM9:55AM1319:Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina;Tapani Raiko;Mathias Berglund;Klaus Greff
Neural Networks and Deep Learning*; Optimization (Continuous); Supervised Learning
296:Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVM
Anton Osokin;Jean-Baptiste Alayrac;Isabella Lukasewitz;Puneet Dokania;Simon Lacoste-Julien
Optimization (Continuous)*; Structured Prediction
1215:Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor;Ryan Burmeister;Zheng Xu;Bharat Singh;Ankit Patel;Tom Goldstein
Neural Networks and Deep Learning*; Optimization (Continuous); Parallel and Distributed Learning; Supervised Learning
775:Extended and Unscented Kitchen Sinks
Edwin Bonilla;Daniel Steinberg;Alistair Reid
Gaussian Processes*; Approximate Inference
974:How to Fake Multiply by a Gaussian Matrix
Michael Kapralov;Vamsi Potluru;David Woodruff
Feature Selection and Dimensionality Reduction*; Matrix Factorization and Related Topics
1019:Cross-graph Learning of Multi-relational Associations
Hanxiao Liu;Yiming Yang
Statistical Relational Learning*; Manifold Learning; Semi-Supervised Learning
784:Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel;Adith Swaminathan;Ashudeep Singh;Navin Chandak;Thorsten Joachims
Recommender Systems*; Matrix Factorization and Related Topics; Ranking and Preference Learning
66
Wednesday
9:55AM10:20AMbreakbreakbreakbreakbreakbreakbreak
67
Wednesday
10:20AM12:21PMNeural Networks and Deep Learning
Optimization (Continuous)
Applications and Time-Series Analysis
Dimensionality Reduction / Private Learning
Monte Carlo Methods
Crowdsourcing and Interactive Learning
Learning Theory
68
Wednesday10:20AM10:37AM485:Generative Adversarial Text to Image Synthesis
Scott Reed;Zeynep Akata;Xinchen Yan;Lajanugen Logeswaran;Bernt Schiele;Honglak Lee
Neural Networks and Deep Learning*; Computer Vision; Natural Language Processing; Representation Learning; Semi-Supervised Learning
421:On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Yossi Arjevani;Ohad Shamir
Optimization (Continuous)*; Large Scale Learning and Big Data
1003:Hierarchical Decision Making In Electricity Grid Management
Gal Dalal;Elad Gilboa;Shie Mannor
Sustainability, Climate, and Environment*; Other Applications; Planning and Control; Reinforcement Learning
1130:Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler;Aditya Bhaskara;Gang Fu;Vahab Mirrokni;Afshin Rostamizadeh;Morteza Zadimoghaddam
Feature Selection and Dimensionality Reduction*; Optimization (Combinatorial); Parallel and Distributed Learning
1180:Interacting Particle Markov Chain Monte Carlo
Tom Rainforth;Christian Naesseth;Fredrik Lindsten;Brooks Paige;Jan-Willem Vandemeent;Arnaud Doucet;Frank Wood
Monte Carlo Methods*
1:No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing
Nihar Shah;Dengyong Zhou
Learning and Mechanism Design*; Economics and Finance
496:Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu;Yang Yuan
Computational Learning Theory*; Supervised Learning
69
Wednesday10:37AM10:54AM732:Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen;Søren Kaae Sønderby;Hugo Larochelle;Ole Winther
Neural Networks and Deep Learning*; Computer Vision; Metric Learning; Representation Learning; Unsupervised Learning
586:Variance-Reduced and Projection-Free Stochastic Optimization
Elad Hazan;Haipeng Luo
Optimization (Continuous)*; Large Scale Learning and Big Data
785:ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission
Jinsung Yoon;Ahmed Alaa;Scott Hu;Mihaela van der Schaar
Health Care*; Other Applications; Structured Prediction
238:Efficient Private Empirical Risk Minimization for High-dimensional Learning
Shiva Prasad Kasiviswanathan;Hongxia Jin
Privacy, Anonymity, and Security*; Feature Selection and Dimensionality Reduction; Optimization (Combinatorial); Sparsity and Compressed Sensing
1359:Slice Sampling on Hamiltonian Trajectories
Benjamin Bloem-Reddy;John Cunningham
Monte Carlo Methods*; Approximate Inference; Bayesian Nonparametric Methods; Gaussian Processes
1246:The Label Complexity of Mixed-Initiative Classifier Training
Jina Suh;Xiaojin Zhu;Saleema Amershi
Active Learning*; Computational Learning Theory; Statistical Learning Theory
329:Variance Reduction for Faster Non-Convex Optimization
Zeyuan Allen-Zhu;Elad Hazan
Computational Learning Theory*; Neural Networks and Deep Learning; Optimization (Continuous); Supervised Learning
70
Wednesday10:54AM11:11AM871:Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman;Jeffrey De Fauw;Koray Kavukcuoglu
Neural Networks and Deep Learning*; Computer Vision; Representation Learning
834:On Graduated Optimization for Stochastic Non-Convex Problems
Elad Hazan;Kfir Yehuda Levy;Shai Shalev-Shwartz
Optimization (Continuous)*; Large Scale Learning and Big Data
1308:Power of Ordered Hypothesis Testing
Lihua Lei;William Fithian
Bioinformatics*
175:Binary embeddings with structured hashed projections
Anna Choromanska;Krzysztof Choromanski;Mariusz Bojarski;Tony Jebara;Sanjiv Kumar;Yann LeCun
Feature Selection and Dimensionality Reduction*; Neural Networks and Deep Learning
1202:Robust Monte Carlo Sampling using Riemannian Nos\'{e}-Poincar\'{e} Hamiltonian Dynamics
Anirban Roychowdhury;Brian Kulis;Srinivasan Parthasarathy
Monte Carlo Methods*; Approximate Inference; Bayesian Nonparametric Methods
529:The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang;Chu Wang;Warren Powell
Active Learning*; Gaussian Processes; Online Learning; Recommender Systems; Resource Efficient Learning; Statistical Learning Theory
507:Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Zeyuan Allen-Zhu;Zheng Qu;Peter Richtárik;Yang Yuan
Optimization (Continuous)*; Computational Learning Theory; Supervised Learning
71
Wednesday
11:11AM11:30AMbreakbreakbreakbreakbreakbreak
72
Wednesday11:30AM11:47AM413:A Comparative Analysis and Study of Multiview Convolutional Neural Network Models for Joint Object Categorization and Pose Estimation
Mohamed Elhoseiny;Tarek El-Gaaly;Amr Bakry;Ahmed Elgammal
Neural Networks and Deep Learning*; Computer Vision
305:A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization
Frank Curtis
Optimization (Continuous)*; Large Scale Learning and Big Data
558:Learning to Filter with Predictive State Inference Machines
Wen Sun;Arun Venkatraman;Byron Boots;J.Andrew Bagnell
Time-Series Analysis*; Graphical Models; Latent Variable Models
984:Differentially Private Policy Evaluation
Borja Balle;Maziar Gomrokchi;Doina Precup
Privacy, Anonymity, and Security*; Reinforcement Learning
1356:Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige;Frank Wood
Monte Carlo Methods*; Approximate Inference
691:Estimating Accuracy from Unlabeled Data: A Bayesian Approach
Emmanouil Antonios Platanios;Avinava Dubey;Tom Mitchell
Unsupervised Learning*; Bayesian Nonparametric Methods; Ensemble Methods; Other Applications; Transfer and Multi-Task Learning
600:False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
QianQian Xu;Jiechao Xiong;Xiaochun Cao;Yuan Yao
Ranking and Preference Learning*; Sparsity and Compressed Sensing
73
Wednesday11:47AM12:04PM1149:Dynamic Capacity Networks
Amjad Almahairi;Nicolas Ballas;Tim Cooijmans;Yin Zheng;Hugo Larochelle;Aaron Courville
Neural Networks and Deep Learning*; Computer Vision; Representation Learning; Resource Efficient Learning
1170:A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums
Anton Rodomanov;Dmitry Kropotov
Optimization (Continuous)*; Large Scale Learning and Big Data
1093:Learning population-level diffusions with generative RNNs
Tatsunori Hashimoto;David Gifford;Tommi Jaakkola
Bioinformatics*; Neural Networks and Deep Learning; Time-Series Analysis
182:Learning from Multiway Data: Simple and Efficient Tensor Regression
Rose Yu;Yan Liu
Transfer and Multi-Task Learning*; Feature Selection and Dimensionality Reduction; Sparsity and Compressed Sensing; Sustainability, Climate, and Environment; Time-Series Analysis
1299:Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson;Patrick Stinson;Ari Pakman;Liam Paninski
Monte Carlo Methods*
203:Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Adish Singla;Sebastian Tschiatschek;Andreas Krause
Active Learning*; Metric Learning; Ranking and Preference Learning
1030:On the Power of Distance-Based Learning
Periklis Papakonstantinou;Jia Xu;Guang Yang
Computational Learning Theory*; Metric Learning; Natural Language Processing; Structured Prediction; Supervised Learning
74
Wednesday12:04PM12:21PM303:Augmenting Neural Networks with Reconstructive Decoding Pathways for Large-scale Image Classification
Yuting Zhang;Kibok Lee;Honglak Lee
Neural Networks and Deep Learning*; Computer Vision; Representation Learning; Semi-Supervised Learning
423:Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning
Xingguo Li;Tuo Zhao;Raman Arora;Han Liu;Jarvis Haupt
Optimization (Continuous)*; Large Scale Learning and Big Data
789:Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching
Mu Niu;Simon Rogers;Maurizio Filippone;Dirk Husmeier
Other Models and Methods*; Approximate Inference; Bioinformatics; Kernel Methods; Optimization (Continuous); Other Applications
148:Low-Rank Matrix Approximation with Stability
Dongsheng Li;Chao Chen;Qin Lv;Junchi Yan;Li Shang;Stephen Chu
Recommender Systems*; Matrix Factorization and Related Topics
306:Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli;Roland Badeau;Taylan Cemgil;Gaël Richard
Monte Carlo Methods*; Large Scale Learning and Big Data; Matrix Factorization and Related Topics; Parallel and Distributed Learning
272:Optimality of Belief Propagation for Crowdsourced Classification
Jungseul Ok;Sewoong Oh;Jinwoo Shin;Yung Yi
Graphical Models*; Information Theory; Latent Variable Models; Resource Efficient Learning
371:Minimizing the Maximal Loss: How and Why
Shai Shalev-Shwartz;Yonatan Wexler
Supervised Learning*; Online Learning; Optimization (Continuous); Statistical Learning Theory
75
76
Wednesday
2:00PM3:00PM
Invited Talk: Christos Faloutsos, "Mining Large Graphs: Patterns, Anomalies, and Fraud Detection"
77
Wednesday
3:00PM3:40PMbreakbreakbreakbreakbreakbreakbreak
78
Wednesday
3:40PM4:31PMOptimization (Continuous)
Supervised LearningKernel Methods
Matrix Factorization and Related Topics
Privacy, Anonymity, and Security
Causal Inference
Optimization
79
Wednesday3:40PM3:57PM1293:Energetic Natural Gradient Descent
Philip Thomas;Bruno Castro da Silva;Christoph Dann;Emma Brunskill
Reinforcement Learning*; Optimization (Continuous)
1113:Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
Zhuoran Yang;Zhaoran Wang;Han Liu;Yonina Eldar;Tong Zhang
Sparsity and Compressed Sensing*; Optimization (Continuous); Supervised Learning
707:DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic;Dino Sejdinovic;Yee-Whye Teh
Approximate Inference*; Kernel Methods
1120:Recycling Randomness with Structure for Sublinear time Kernel Expansions
Krzysztof Choromanski;Vikas Sindhwani
Kernel Methods*; Large Scale Learning and Big Data
278:Learning privately from multiparty data
Jihun Hamm;Yingjun Cao;Mikhail Belkin
Privacy, Anonymity, and Security*; Ensemble Methods
959:The Arrow of Time in Multivariate Time Series
Stefan Bauer;Bernhard Schölkopf;Jonas Peters
Causal Inference*; Time-Series Analysis
675:Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Igor Colin;Aurélien Bellet;Joseph Salmon;Stéphan Clémençon
Parallel and Distributed Learning*; Metric Learning; Optimization (Continuous)
80
Wednesday3:57PM4:14PM363:On the Quality of the Initial Basin in Overspecified Neural Networks
Itay Safran;Ohad Shamir
Optimization (Continuous)*; Neural Networks and Deep Learning
400:Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel;Masakazu Ishihata;Akinori Fujino;Naonori Ueda
Matrix Factorization and Related Topics*; Kernel Methods; Neural Networks and Deep Learning; Optimization (Continuous); Recommender Systems; Supervised Learning
942:Persistence weighted Gaussian kernel for topological data analysis
Genki Kusano;Yasuaki Hiraoka;Kenji Fukumizu
Kernel Methods*
725:Optimal Classification with Multivariate Losses
Nagarajan Natarajan;Oluwasanmi Koyejo;Pradeep Ravikumar;Inderjit S. Dhillon
Information Retrieval*; Optimization (Combinatorial); Statistical Learning Theory; Structured Prediction; Supervised Learning
980:Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Ryan Rogers;Salil Vadhan;Hyun Lim;Marco Gaboardi
Privacy, Anonymity, and Security*; Other Applications
1237:Causal Strength via Shannon Capacity: Axioms, Estimators and Applications
Weihao Gao;Sreeram Kannan;Sewoong Oh;Pramod Viswanath
Causal Inference*; Bayesian Nonparametric Methods; Bioinformatics; Information Theory
181:Adaptive Sampling for SGD by Exploiting Side Information
Siddharth Gopal
Resource Efficient Learning*; Optimization (Continuous); Supervised Learning
81
Wednesday4:14PM4:31PM451:L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang;Jason D. Lee;Michael Jordan
Kernel Methods*; Neural Networks and Deep Learning; Statistical Learning Theory
337:Hyperparameter optimization with approximate gradient
Fabian Pedregosa
Optimization (Continuous)*; Resource Efficient Learning; Supervised Learning
1208:Discriminative Embeddings of Latent Variable Models for Structured Data
Hanjun Dai;Bo Dai;Le Song
Kernel Methods*; Structured Prediction
499:Sparse Parameter Recovery from Aggregated Data
Avradeep Bhowmik;Joydeep Ghosh;Oluwasanmi Koyejo
Semi-Supervised Learning*; Health Care; Other Models and Methods; Privacy, Anonymity, and Security; Sparsity and Compressed Sensing; Supervised Learning
1101:Discrete Distribution Estimation under Local Privacy
Peter Kairouz;Keith Bonawitz;Daniel Ramage
Privacy, Anonymity, and Security*; Learning and Mechanism Design; Statistical Learning Theory
801:Learning Granger Causality for Hawkes Processes
Hongteng Xu;Mehrdad Farajtabar;Hongyuan Zha
Time-Series Analysis*; Causal Inference
960:Mixture Proportion Estimation via Kernel Embeddings of Distributions
Harish Ramaswamy;Clayton Scott;Ambuj Tewari
Statistical Learning Theory*; Computational Learning Theory; Kernel Methods; Semi-Supervised Learning; Supervised Learning; Unsupervised Learning
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84
85
86
87
88
89
90
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