Proceedings of the 32nd International Conference on Machine Learning
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Francis R. Bach, David M. Blei
Proceedings of the 32nd International Conference on Machine Learning
ICML, 2015.

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@proceedings{ICML-2015,
	address       = "Lille, France",
	editor        = "Francis R. Bach and David M. Blei",
	ee            = "http://jmlr.org/proceedings/papers/v37/",
	publisher     = "{JMLR.org}",
	series        = "{JMLR Proceedings}",
	title         = "{Proceedings of the 32nd International Conference on Machine Learning}",
	volume        = 37,
	year          = 2015,
}

Contents (270 items)

ICML-2015-ZhaoZ #optimisation #probability
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (PZ, TZ), pp. 1–9.
ICML-2015-ShahZP #crowdsourcing
Approval Voting and Incentives in Crowdsourcing (NBS, DZ, YP), pp. 10–19.
ICML-2015-BounliphoneGTB #consistency #dependence
A low variance consistent test of relative dependency (WB, AG, AT, MBB), pp. 20–29.
ICML-2015-Bai0ZH #graph #kernel
An Aligned Subtree Kernel for Weighted Graphs (LB, LR, ZZ, ERH), pp. 30–39.
ICML-2015-BoutsidisKG #clustering
Spectral Clustering via the Power Method — Provably (CB, PK, AG), pp. 40–48.
ICML-2015-SunWKM #geometry #network
Information Geometry and Minimum Description Length Networks (KS, JW, AK, SMM), pp. 49–58.
ICML-2015-TristanTS #estimation #gpu #performance
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation (JBT, JT, GLSJ), pp. 59–68.
ICML-2015-ZhaoYZL #adaptation #multi #probability
Adaptive Stochastic Alternating Direction Method of Multipliers (PZ, JY, TZ, PL), pp. 69–77.
ICML-2015-AgarwalB #bound #finite #optimisation
A Lower Bound for the Optimization of Finite Sums (AA, LB), pp. 78–86.
ICML-2015-YogatamaFDS #learning #word
Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICML-2015-LongC0J #adaptation #learning #network
Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICML-2015-Osogami #markov #process #robust
Robust partially observable Markov decision process (TO), pp. 106–115.
ICML-2015-ZhaoMP #network #on the
On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.
ICML-2015-MenonROW #estimation #learning
Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICML-2015-Yang0JZ #bound #fault #set
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection (TY, LZ, RJ, SZ), pp. 135–143.
ICML-2015-Shamir #algorithm #convergence #exponential #probability
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate (OS), pp. 144–152.
ICML-2015-KuklianskyS #linear #performance
Attribute Efficient Linear Regression with Distribution-Dependent Sampling (DK, OS), pp. 153–161.
ICML-2015-FetayaU #invariant #learning
Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICML-2015-MaLF #analysis #canonical #correlation #dataset #linear #scalability
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis (ZM, YL, DPF), pp. 169–178.
ICML-2015-JiangKS #abstraction #learning #modelling
Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICML-2015-KarN0 #precise
Surrogate Functions for Maximizing Precision at the Top (PK, HN, PJ), pp. 189–198.
ICML-2015-NarasimhanK0 #metric #optimisation #performance
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes (HN, PK, PJ), pp. 199–208.
ICML-2015-BachemLK #estimation #parametricity
Coresets for Nonparametric Estimation — the Case of DP-Means (OB, ML, AK), pp. 209–217.
ICML-2015-GajaneUC #algorithm #exponential
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits (PG, TU, FC), pp. 218–227.
ICML-2015-BahadoriKFL #clustering #functional
Functional Subspace Clustering with Application to Time Series (MTB, DCK, YF, YL), pp. 228–237.
ICML-2015-YuCL #learning #multi #online #rank
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
ICML-2015-JewellSB #process
Atomic Spatial Processes (SJ, NS, ABC), pp. 248–256.
ICML-2015-HazanLM #classification #rank
Classification with Low Rank and Missing Data (EH, RL, YM), pp. 257–266.
ICML-2015-RichmanM #classification #constraints
Dynamic Sensing: Better Classification under Acquisition Constraints (OR, SM), pp. 267–275.
ICML-2015-GongY #analysis #convergence #memory management
A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis (PG, JY), pp. 276–284.
ICML-2015-ShajarisalesJSB #linear
Telling cause from effect in deterministic linear dynamical systems (NS, DJ, BS, MB), pp. 285–294.
ICML-2015-KandasamySP #modelling #optimisation
High Dimensional Bayesian Optimisation and Bandits via Additive Models (KK, JGS, BP), pp. 295–304.
ICML-2015-Yang0JZ15a #random #reduction
Theory of Dual-sparse Regularized Randomized Reduction (TY, LZ, RJ, SZ), pp. 305–314.
ICML-2015-TewariC #bound #documentation #fault #learning #matter #question #rank
Generalization error bounds for learning to rank: Does the length of document lists matter? (AT, SC), pp. 315–323.
ICML-2015-HockingRB #detection #learning #named #segmentation
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data (TH, GR, GB), pp. 324–332.
ICML-2015-FercoqGS
Mind the duality gap: safer rules for the Lasso (OF, AG, JS), pp. 333–342.
ICML-2015-NishiharaLRPJ #analysis #convergence
A General Analysis of the Convergence of ADMM (RN, LL, BR, AP, MIJ), pp. 343–352.
ICML-2015-ZhangL #coordination #empirical #probability
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization (YZ, XL), pp. 353–361.
ICML-2015-ZhangL15a #distributed #empirical #named #optimisation #self
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss (YZ, XL), pp. 362–370.
ICML-2015-ChenS #rank
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons (YC, CS), pp. 371–380.
ICML-2015-BachHBG #learning #performance
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICML-2015-CortesKMS #modelling
Structural Maxent Models (CC, VK, MM, US), pp. 391–399.
ICML-2015-GhoshdastidarD #clustering
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning (DG, AD), pp. 400–409.
ICML-2015-LondonHG #approximate #learning
The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICML-2015-XinW #adaptation #probability #rank
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA (BX, DPW), pp. 419–427.
ICML-2015-MaeharaYK #game studies #multi #perspective #problem
Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View (TM, AY, KiK), pp. 428–437.
ICML-2015-BlechschmidtGL #approximate #multi #optimisation #parametricity #problem
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains (KB, JG, SL), pp. 438–447.
ICML-2015-IoffeS #network #normalisation
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (SI, CS), pp. 448–456.
ICML-2015-ZhangWJ #algorithm #bound #distributed #estimation #matrix #performance #rank
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds (YZ, MJW, MIJ), pp. 457–465.
ICML-2015-LiangP #process
Landmarking Manifolds with Gaussian Processes (DL, JP), pp. 466–474.
ICML-2015-ZhangP #markov #modelling
Markov Mixed Membership Models (AZ, JP), pp. 475–483.
ICML-2015-YangX #algorithm #framework
A Unified Framework for Outlier-Robust PCA-like Algorithms (WY, HX), pp. 484–493.
ICML-2015-YangX15a #analysis #component #streaming
Streaming Sparse Principal Component Analysis (WY, HX), pp. 494–503.
ICML-2015-YangX15b #clustering #distributed #divide and conquer #framework #graph
A Divide and Conquer Framework for Distributed Graph Clustering (WY, HX), pp. 504–513.
ICML-2015-AnBB #how #linear #network #question
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (SA, FB, MB), pp. 514–523.
ICML-2015-LakshmananOR #bound #learning
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICML-2015-Betancourt #monte carlo #scalability
The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling (MB), pp. 533–540.
ICML-2015-GarberH #performance #set
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets (DG, EH), pp. 541–549.
ICML-2015-DasBB #modelling #order #parametricity
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models (MKD, TB, CB), pp. 550–559.
ICML-2015-GarberHM #learning #online
Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICML-2015-HoangHL #big data #framework #modelling #probability #process
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data (TNH, QMH, BKHL), pp. 569–578.
ICML-2015-DingZSMM #consistency
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup (YD, YZ, XS, MM, TM), pp. 579–587.
ICML-2015-VirtanenG #modelling
Ordinal Mixed Membership Models (SV, MG), pp. 588–596.
ICML-2015-HongYKH #learning #network #online
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICML-2015-FlaxmanWNNS #performance #process
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods (SF, AGW, DN, HN, AJS), pp. 607–616.
ICML-2015-RaskuttiM #algorithm #random #sketching #statistics
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares (GR, MM), pp. 617–625.
ICML-2015-KordaA #approximate #bound #convergence #exponential #on the
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence (NK, PLA), pp. 626–634.
ICML-2015-WeissN #alias #learning
Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICML-2015-GalCG #category theory #estimation #multi #process
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data (YG, YC, ZG), pp. 645–654.
ICML-2015-GalT #approximate #nondeterminism #process #representation
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs (YG, RT), pp. 655–664.
ICML-2015-RajkumarGL0 #probability #ranking #set
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top (AR, SG, LHL, SA), pp. 665–673.
ICML-2015-CsibaQR #adaptation #coordination #probability
Stochastic Dual Coordinate Ascent with Adaptive Probabilities (DC, ZQ, PR), pp. 674–683.
ICML-2015-TanseyPSR #exponential #markov #product line #random
Vector-Space Markov Random Fields via Exponential Families (WT, OHMP, ASS, PR), pp. 684–692.
ICML-2015-HugginsNSM #markov #named #process
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes (JHH, KN, AS, VKM), pp. 693–701.
ICML-2015-UbaruMS #approximate #fault #matrix #rank #using
Low Rank Approximation using Error Correcting Coding Matrices (SU, AM, YS), pp. 702–710.
ICML-2015-HallakSMM #learning #modelling
Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICML-2015-HuangWSLC #classification #image #learning #metric #set #symmetry
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (ZH, RW, SS, XL, XC), pp. 720–729.
ICML-2015-Kandemir #learning #process #symmetry
Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICML-2015-ZhuG #complexity #robust #towards
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing (RZ, QG), pp. 739–747.
ICML-2015-GouwsBC #distributed #named #performance #word
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments (SG, YB, GC), pp. 748–756.
ICML-2015-SunLXB #clustering #multi
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization (JS, JL, TX, JB), pp. 757–766.
ICML-2015-KvetonSWA #learning #rank
Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICML-2015-FouldsKG #framework #modelling #network #probability #programming #topic
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models (JRF, SHK, LG), pp. 777–786.
ICML-2015-EneN #coordination #random
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions (AE, HLN), pp. 787–795.
ICML-2015-NarayanPA #metaprogramming
α-β Divergences Discover Micro and Macro Structures in Data (KSN, AP, PA), pp. 796–804.
ICML-2015-HeinrichLS #game studies #self
Fictitious Self-Play in Extensive-Form Games (JH, ML, DS), pp. 805–813.
ICML-2015-SwaminathanJ #feedback #learning
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICML-2015-KricheneBTB #algorithm
The Hedge Algorithm on a Continuum (WK, MB, CJT, AMB), pp. 824–832.
ICML-2015-BelangerK #linear
A Linear Dynamical System Model for Text (DB, SMK), pp. 833–842.
ICML-2015-SrivastavaMS #learning #using #video
Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICML-2015-SunSK #message passing #modelling #visual notation
Message Passing for Collective Graphical Models (TS, DS, AK), pp. 853–861.
ICML-2015-WangZ #clustering #named #parametricity
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics (YW, JZ), pp. 862–870.
ICML-2015-HeRFGL #modelling #named #network #topic
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades (XH, TR, JRF, LG, YL), pp. 871–880.
ICML-2015-GermainGML #estimation #named
MADE: Masked Autoencoder for Distribution Estimation (MG, KG, IM, HL), pp. 881–889.
ICML-2015-WuS #algorithm #learning #modelling #online
An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICML-2015-Papachristoudis #adaptation
Adaptive Belief Propagation (GP, JWF), pp. 899–907.
ICML-2015-HanMS #probability #scalability
Large-scale log-determinant computation through stochastic Chebyshev expansions (IH, DM, JS), pp. 908–917.
ICML-2015-KusnerGGW #optimisation
Differentially Private Bayesian Optimization (MJK, JRG, RG, KQW), pp. 918–927.
ICML-2015-HegdeIS #framework
A Nearly-Linear Time Framework for Graph-Structured Sparsity (CH, PI, LS), pp. 928–937.
ICML-2015-LuoXZL #matrix
Support Matrix Machines (LL, YX, ZZ, WJL), pp. 938–947.
ICML-2015-NockPF
Rademacher Observations, Private Data, and Boosting (RN, GP, AF), pp. 948–956.
ICML-2015-KusnerSKW #documentation #word
From Word Embeddings To Document Distances (MJK, YS, NIK, KQW), pp. 957–966.
ICML-2015-MatthewCYW #empirical
Bayesian and Empirical Bayesian Forests (TM, CSC, JY, MW), pp. 967–976.
ICML-2015-Pouget-AbadieH #framework #graph
Inferring Graphs from Cascades: A Sparse Recovery Framework (JPA, TH), pp. 977–986.
ICML-2015-LeeR #distributed #linear #optimisation #polynomial
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM (CPL, DR), pp. 987–996.
ICML-2015-SuiGBK #optimisation #process
Safe Exploration for Optimization with Gaussian Processes (YS, AG, JWB, AK), pp. 997–1005.
ICML-2015-BlumH #contest #machine learning #reliability
The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
ICML-2015-FilipponeE #linear #probability #process #scalability
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) (MF, RE), pp. 1015–1024.
ICML-2015-GarnettHS #process
Finding Galaxies in the Shadows of Quasars with Gaussian Processes (RG, SH, JS), pp. 1025–1033.
ICML-2015-CohenH #learning #online
Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICML-2015-SteinhardtL #modelling
Reified Context Models (JS, PL), pp. 1043–1052.
ICML-2015-Abbasi-YadkoriB #crowdsourcing #markov #problem #scalability
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing (YAY, PLB, XC, AM), pp. 1053–1062.
ICML-2015-SteinhardtL15a #learning #modelling #predict
Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICML-2015-Hernandez-Lobato #feature model #multi #probability
A Probabilistic Model for Dirty Multi-task Feature Selection (DHL, JMHL, ZG), pp. 1073–1082.
ICML-2015-WangALB #learning #multi #on the #representation
On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICML-2015-PiechHNPSG #feedback #learning #student
Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICML-2015-ZhouZ #problem
Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems (QZ, QZ), pp. 1103–1112.
ICML-2015-WenKA #combinator #learning #performance #scalability
Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICML-2015-ManoelKTZ #approximate #estimation #message passing
Swept Approximate Message Passing for Sparse Estimation (AM, FK, EWT, LZ), pp. 1123–1132.
ICML-2015-CarpentierV #infinity
Simple regret for infinitely many armed bandits (AC, MV), pp. 1133–1141.
ICML-2015-ChaoSMS #exponential #integration #monte carlo
Exponential Integration for Hamiltonian Monte Carlo (WLC, JS, DM, FS), pp. 1142–1151.
ICML-2015-KomiyamaHN #analysis #multi #probability #problem
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays (JK, JH, HN), pp. 1152–1161.
ICML-2015-IzbickiS #performance
Faster cover trees (MI, CRS), pp. 1162–1170.
ICML-2015-JohnsonG #named #optimisation #scalability
Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization (TJ, CG), pp. 1171–1179.
ICML-2015-GaninL #adaptation
Unsupervised Domain Adaptation by Backpropagation (YG, VSL), pp. 1180–1189.
ICML-2015-LiuHW #collaboration
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer (YFL, CYH, SHW), pp. 1190–1198.
ICML-2015-KimXVS #process
Manifold-valued Dirichlet Processes (HJK, JX, BCV, VS), pp. 1199–1208.
ICML-2015-WangWLCW #learning #multi #segmentation
Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICML-2015-SalimansKW #markov #monte carlo
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (TS, DPK, MW), pp. 1218–1226.
ICML-2015-LiuFFM #modelling #relational #scalability
Scalable Model Selection for Large-Scale Factorial Relational Models (CL, LF, RF, YM), pp. 1227–1235.
ICML-2015-BarbosaENW #dataset #distributed #power of
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets (RdPB, AE, HLN, JW), pp. 1236–1244.
ICML-2015-EggelingKG #big data
Dealing with small data: On the generalization of context trees (RE, MK, IG), pp. 1245–1253.
ICML-2015-YuanHTLC #modelling
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood (XY, RH, ET, RL, LC), pp. 1254–1263.
ICML-2015-BenavoliCMZ #algorithm #parametricity
A Bayesian nonparametric procedure for comparing algorithms (AB, GC, FM, MZ), pp. 1264–1272.
ICML-2015-Suzuki #convergence
Convergence rate of Bayesian tensor estimator and its minimax optimality (TS), pp. 1273–1282.
ICML-2015-WuGS #combinator #feedback #finite #identification #on the
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments (YW, AG, CS), pp. 1283–1291.
ICML-2015-NaessethLS #monte carlo
Nested Sequential Monte Carlo Methods (CAN, FL, TBS), pp. 1292–1301.
ICML-2015-ShethWK #modelling
Sparse Variational Inference for Generalized GP Models (RS, YW, RK), pp. 1302–1311.
ICML-2015-SchaulHGS #approximate
Universal Value Function Approximators (TS, DH, KG, DS), pp. 1312–1320.
ICML-2015-PerolatSPP #approximate #game studies #markov #programming
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games (JP, BS, BP, OP), pp. 1321–1329.
ICML-2015-SharmaKD #on the
On Greedy Maximization of Entropy (DS, AK, AD), pp. 1330–1338.
ICML-2015-WangLWC #metadata #process
Metadata Dependent Mondrian Processes (YW, BL, YW, FC), pp. 1339–1347.
ICML-2015-ChangYXY #detection #semantics #using
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM (XC, YY, EPX, YY), pp. 1348–1357.
ICML-2015-HayashiMF
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood (KH, SiM, RF), pp. 1358–1366.
ICML-2015-LimKPJ #performance #scalability #set
Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets (WL, MK, HP, KJ), pp. 1367–1375.
ICML-2015-KairouzOV #composition #difference #privacy #theorem
The Composition Theorem for Differential Privacy (PK, SO, PV), pp. 1376–1385.
ICML-2015-PlessisNS #learning
Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICML-2015-MiyauchiIFK
Threshold Influence Model for Allocating Advertising Budgets (AM, YI, TF, NK), pp. 1395–1404.
ICML-2015-DanielyGS #adaptation #learning #online
Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICML-2015-XuJZ #algorithm #matrix
CUR Algorithm for Partially Observed Matrices (MX, RJ, ZHZ), pp. 1412–1421.
ICML-2015-WangWS #analysis #clustering
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data (YW, YXW, AS), pp. 1422–1431.
ICML-2015-SibonyCJ #learning #ranking #statistics
MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICML-2015-HugginsT
Risk and Regret of Hierarchical Bayesian Learners (JH, JT), pp. 1442–1451.
ICML-2015-Lopez-PazMST #learning #towards
Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICML-2015-GregorDGRW #generative #image #named #network
DRAW: A Recurrent Neural Network For Image Generation (KG, ID, AG, DJR, DW), pp. 1462–1471.
ICML-2015-AmidU #learning #multi
Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICML-2015-DeisenrothN #distributed #process
Distributed Gaussian Processes (MPD, JWN), pp. 1481–1490.
ICML-2015-TangS #approach #composition
Guaranteed Tensor Decomposition: A Moment Approach (GT, PS), pp. 1491–1500.
ICML-2015-ZhouZS #analysis #bound #convergence #fault #first-order
ℓ₁,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods (ZZ, QZ, AMCS), pp. 1501–1510.
ICML-2015-HanXA #consistency #estimation #modelling #multi
Consistent estimation of dynamic and multi-layer block models (QH, KSX, EA), pp. 1511–1520.
ICML-2015-TagortiS #bound #convergence #fault #on the
On the Rate of Convergence and Error Bounds for LSTD(λ) (MT, BS), pp. 1521–1529.
ICML-2015-RezendeM #normalisation
Variational Inference with Normalizing Flows (DJR, SM), pp. 1530–1538.
ICML-2015-MacdonaldHH #modelling #process
Controversy in mechanistic modelling with Gaussian processes (BM, CFH, DH), pp. 1539–1547.
ICML-2015-CilibertoMPR #learning #multi
Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICML-2015-OsadchyHK #classification
K-hyperplane Hinge-Minimax Classifier (MO, TH, DK), pp. 1558–1566.
ICML-2015-LesnerS #approximate #policy
Non-Stationary Approximate Modified Policy Iteration (BL, BS), pp. 1567–1575.
ICML-2015-SerrurierP #evaluation #learning
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees (MS, HP), pp. 1576–1584.
ICML-2015-YouV #geometry
Geometric Conditions for Subspace-Sparse Recovery (CY, RV), pp. 1585–1593.
ICML-2015-ShahKG #algorithm #empirical #probability #process
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process (AS, DAK, ZG), pp. 1594–1603.
ICML-2015-ZhuSG #memory management #recursion
Long Short-Term Memory Over Recursive Structures (XDZ, PS, HG), pp. 1604–1612.
ICML-2015-BlundellCKW #network #nondeterminism
Weight Uncertainty in Neural Network (CB, JC, KK, DW), pp. 1613–1622.
ICML-2015-YuB #learning
Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICML-2015-NutiniSLFK #coordination #performance #random
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection (JN, MWS, IHL, MPF, HAK), pp. 1632–1641.
ICML-2015-LengWCZL #distributed
Hashing for Distributed Data (CL, JW, JC, XZ, HL), pp. 1642–1650.
ICML-2015-HuHDX #distributed #parametricity #scalability
Large-scale Distributed Dependent Nonparametric Trees (ZH, QH, AD, EPX), pp. 1651–1659.
ICML-2015-SzorenyiBWH #approach #multi
Qualitative Multi-Armed Bandits: A Quantile-Based Approach (BS, RBF, PW, EH), pp. 1660–1668.
ICML-2015-XuRYLJ
Deep Edge-Aware Filters (LX, JR, QY, RL, JJ), pp. 1669–1678.
ICML-2015-LimCX #clustering #framework #optimisation
A Convex Optimization Framework for Bi-Clustering (SHL, YC, HX), pp. 1679–1688.
ICML-2015-XiaoBBFER #feature model #question
Is Feature Selection Secure against Training Data Poisoning? (HX, BB, GB, GF, CE, FR), pp. 1689–1698.
ICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.
ICML-2015-PerrotH #analysis #learning #metric
A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICML-2015-LiSZ #generative #network
Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
ICML-2015-AsterisKDYC #graph
Stay on path: PCA along graph paths (MA, ATK, AGD, HGY, BC), pp. 1728–1736.
ICML-2015-GuptaAGN #learning #precise
Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICML-2015-WangY #learning #matrix #multi
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICML-2015-CohenW #exponential #product line
Harmonic Exponential Families on Manifolds (TC, MW), pp. 1757–1765.
ICML-2015-ClarkS #game studies #network
Training Deep Convolutional Neural Networks to Play Go (CC, AJS), pp. 1766–1774.
ICML-2015-WilsonN #kernel #process #scalability
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) (AGW, HN), pp. 1775–1784.
ICML-2015-ChenSYU #learning #modelling
Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICML-2015-AvronH #community #detection #personalisation #rank #using
Community Detection Using Time-Dependent Personalized PageRank (HA, LH), pp. 1795–1803.
ICML-2015-DjolongaK #modelling #scalability
Scalable Variational Inference in Log-supermodular Models (JD, AK), pp. 1804–1813.
ICML-2015-LloydGOR #process
Variational Inference for Gaussian Process Modulated Poisson Processes (CML, TG, MAO, SJR), pp. 1814–1822.
ICML-2015-GanCHCC #analysis #modelling #scalability #topic
Scalable Deep Poisson Factor Analysis for Topic Modeling (ZG, CC, RH, DEC, LC), pp. 1823–1832.
ICML-2015-GornitzBK #detection #markov
Hidden Markov Anomaly Detection (NG, MLB, MK), pp. 1833–1842.
ICML-2015-QiuXHLC #estimation #matrix #process #robust
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes (HQ, SX, FH, HL, BC), pp. 1843–1851.
ICML-2015-RamaswamyT0 #classification
Convex Calibrated Surrogates for Hierarchical Classification (HGR, AT, SA), pp. 1852–1860.
ICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICML-2015-BerlindU #nearest neighbour
Active Nearest Neighbors in Changing Environments (CB, RU), pp. 1870–1879.
ICML-2015-LiuY #graph #learning #predict
Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICML-2015-SchulmanLAJM #optimisation #policy #trust
Trust Region Policy Optimization (JS, SL, PA, MIJ, PM), pp. 1889–1897.
ICML-2015-GongZSTG
Discovering Temporal Causal Relations from Subsampled Data (MG, KZ, BS, DT, PG), pp. 1898–1906.
ICML-2015-ParkNZSD #collaboration #ranking #scalability
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons (DP, JN, JZ, SS, ISD), pp. 1907–1916.
ICML-2015-GeigerZSGJ #component #identification #process
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (PG, KZ, BS, MG, DJ), pp. 1917–1925.
ICML-2015-NeyshaburS #on the #symmetry
On Symmetric and Asymmetric LSHs for Inner Product Search (BN, NS), pp. 1926–1934.
ICML-2015-JiaoV #kernel #permutation
The Kendall and Mallows Kernels for Permutations (YJ, JPV), pp. 1935–1944.
ICML-2015-RajanHSFJ #locality #multi
Bayesian Multiple Target Localization (PR, WH, RS, PIF, BJ), pp. 1945–1953.
ICML-2015-WeiIB #learning #set
Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICML-2015-BachmanP #collaboration #generative #network #probability
Variational Generative Stochastic Networks with Collaborative Shaping (PB, DP), pp. 1964–1972.
ICML-2015-MaSJJRT #distributed #optimisation
Adding vs. Averaging in Distributed Primal-Dual Optimization (CM, VS, MJ, MIJ, PR, MT), pp. 1973–1982.
ICML-2015-NanWS #random
Feature-Budgeted Random Forest (FN, JW, VS), pp. 1983–1991.
ICML-2015-LibbrechtHBN #graph
Entropic Graph-based Posterior Regularization (ML, MMH, JAB, WSN), pp. 1992–2001.
ICML-2015-LeC #learning #metric #using
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICML-2015-ZukW #matrix #metric #rank
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements (OZ, AW), pp. 2012–2020.
ICML-2015-GiguereRLM #algorithm #kernel #predict #problem #string
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction (SG, AR, FL, MM), pp. 2021–2029.
ICML-2015-LianHRLC #multi #predict #process
A Multitask Point Process Predictive Model (WL, RH, VR, JEL, LC), pp. 2030–2038.
ICML-2015-ZhuE #approach #hybrid #probability #random #using
A Hybrid Approach for Probabilistic Inference using Random Projections (MZ, SE), pp. 2039–2047.
ICML-2015-XuBKCCSZB #generative #image #visual notation
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (KX, JB, RK, KC, ACC, RS, RSZ, YB), pp. 2048–2057.
ICML-2015-ChangKADL #education #learning
Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICML-2015-ChungGCB #feedback #network
Gated Feedback Recurrent Neural Networks (JC, ÇG, KC, YB), pp. 2067–2075.
ICML-2015-Soltanmohammadi #data fusion
Context-based Unsupervised Data Fusion for Decision Making (ES, MNP, MvdS), pp. 2076–2084.
ICML-2015-LebretPC #image
Phrase-based Image Captioning (RL, POP, RC), pp. 2085–2094.
ICML-2015-RegierMMAHLSP #generative #image #named
Celeste: Variational inference for a generative model of astronomical images (JR, AM, JM, RPA, MDH, DL, DS, P), pp. 2095–2103.
ICML-2015-PrasadPR #analysis #axiom #rank
Distributional Rank Aggregation, and an Axiomatic Analysis (AP, HHP, PDR), pp. 2104–2112.
ICML-2015-MaclaurinDA #learning #optimisation
Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICML-2015-AllamanisTGW #modelling #natural language #source code
Bimodal Modelling of Source Code and Natural Language (MA, DT, ADG, YW), pp. 2123–2132.
ICML-2015-HanawalSVM
Cheap Bandits (MKH, VS, MV, RM), pp. 2133–2142.
ICML-2015-ChazalFLMRW #persistent
Subsampling Methods for Persistent Homology (FC, BF, FL, BM, AR, LAW), pp. 2143–2151.
ICML-2015-Romera-ParedesT #approach #learning
An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICML-2015-YiCP #algorithm #performance
Binary Embedding: Fundamental Limits and Fast Algorithm (XY, CC, EP), pp. 2162–2170.
ICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using
Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
ICML-2015-GlobersonRSY #how #predict #question
How Hard is Inference for Structured Prediction? (AG, TR, DS, CY), pp. 2181–2190.
ICML-2015-AnavaHZ #online #predict
Online Time Series Prediction with Missing Data (OA, EH, AZ), pp. 2191–2199.
ICML-2015-PachecoS #approach #pseudo
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach (JP, EBS), pp. 2200–2208.
ICML-2015-JerniteRS #approach #learning #markov #modelling #performance #random
A Fast Variational Approach for Learning Markov Random Field Language Models (YJ, AMR, DS), pp. 2209–2217.
ICML-2015-ScholkopfHWFJSP #fault
Removing systematic errors for exoplanet search via latent causes (BS, DWH, DW, DFM, DJ, CJSG, JP), pp. 2218–2226.
ICML-2015-SamoR #parametricity #process #scalability
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes (YLKS, SR), pp. 2227–2236.
ICML-2015-AhnCGMW #clustering #correlation #data type
Correlation Clustering in Data Streams (KJA, GC, SG, AM, AW), pp. 2237–2246.
ICML-2015-TangSX #learning #network
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICML-2015-Sohl-DicksteinW #learning #using
Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICML-2015-TraskGR #modelling #order #scalability #word
Modeling Order in Neural Word Embeddings at Scale (AT, DG, MR), pp. 2266–2275.
ICML-2015-GeCWG #distributed #modelling #process
Distributed Inference for Dirichlet Process Mixture Models (HG, YC, MW, ZG), pp. 2276–2284.
ICML-2015-ChenWTWC #network
Compressing Neural Networks with the Hashing Trick (WC, JTW, ST, KQW, YC), pp. 2285–2294.
ICML-2015-GeZ #matrix
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees (RG, JZ), pp. 2295–2303.
ICML-2015-GrosseS #matrix #scalability
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix (RBG, RS), pp. 2304–2313.
ICML-2015-VanseijenS #learning
A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICML-2015-BeygelzimerKL #adaptation #algorithm #online
Optimal and Adaptive Algorithms for Online Boosting (AB, SK, HL), pp. 2323–2331.
ICML-2015-SaRO #convergence #matrix #probability #problem
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems (CDS, CR, KO), pp. 2332–2341.
ICML-2015-JozefowiczZS #architecture #empirical #network
An Empirical Exploration of Recurrent Network Architectures (RJ, WZ, IS), pp. 2342–2350.
ICML-2015-SunQW #optimisation #taxonomy #using
Complete Dictionary Recovery Using Nonconvex Optimization (JS, QQ, JW), pp. 2351–2360.
ICML-2015-Bou-AmmarTE #learning #policy #sublinear
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICML-2015-HsiehYD #named #parallel #probability
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (CJH, HFY, ISD), pp. 2370–2379.
ICML-2015-ThomasTG #policy
High Confidence Policy Improvement (PST, GT, MG), pp. 2380–2388.
ICML-2015-MarietS #algorithm #fixpoint #learning #process
Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICML-2015-NarasimhanRS0 #algorithm #consistency #metric #multi #performance
Consistent Multiclass Algorithms for Complex Performance Measures (HN, HGR, AS, SA), pp. 2398–2407.
ICML-2015-MartensG #approximate #network #optimisation
Optimizing Neural Networks with Kronecker-factored Approximate Curvature (JM, RBG), pp. 2408–2417.
ICML-2015-YenLZRD #approach #modelling #process
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (IEHY, XL, KZ, PKR, ISD), pp. 2418–2426.
ICML-2015-PhamRFA #learning #multi #novel
Multi-instance multi-label learning in the presence of novel class instances (ATP, RR, XZF, JPA), pp. 2427–2435.
ICML-2015-RalaivolaA
Entropy-Based Concentration Inequalities for Dependent Variables (LR, MRA), pp. 2436–2444.
ICML-2015-HsiehND #learning #matrix
PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICML-2015-AybatWI #distributed #optimisation
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization (NSA, ZW, GI), pp. 2454–2462.
ICML-2015-YangRV #clustering
Sparse Subspace Clustering with Missing Entries (CY, DR, RV), pp. 2463–2472.
ICML-2015-GuanSBMBB #linear
Moderated and Drifting Linear Dynamical Systems (JG, KS, EB, CM, EB, KB), pp. 2473–2482.
ICML-2015-LeeY #category theory #predict #strict
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.
ICML-2015-WangFS #for free #monte carlo #privacy #probability
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (YXW, SEF, AJS), pp. 2493–2502.
ICML-2015-TheisH #probability #streaming
A trust-region method for stochastic variational inference with applications to streaming data (LT, MDH), pp. 2503–2511.
ICML-2015-WinnerBS
Inference in a Partially Observed Queuing Model with Applications in Ecology (KW, GB, DS), pp. 2512–2520.
ICML-2015-HuangGS #analysis #component #independence
Deterministic Independent Component Analysis (RH, AG, CS), pp. 2521–2530.
ICML-2015-GasseAE #classification #composition #multi #on the #set
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property (MG, AA, HE), pp. 2531–2539.
ICML-2015-FrostigGKS #algorithm #approximate #empirical #named #performance #probability
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization (RF, RG, SK, AS), pp. 2540–2548.
ICML-2015-GuL #algorithm #fault
A New Generalized Error Path Algorithm for Model Selection (BG, CXL), pp. 2549–2558.

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