Proceedings of the 33rd International Conference on Machine Learning
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Maria-Florina Balcan, Kilian Q. Weinberger
Proceedings of the 33rd International Conference on Machine Learning
ICML, 2016.

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@proceedings{ICML-2016,
	editor        = "Maria-Florina Balcan and Kilian Q. Weinberger",
	ee            = "http://proceedings.mlr.press/v48/",
	publisher     = "{JMLR.org}",
	series        = "{JMLR Workshop and Conference Proceedings}",
	title         = "{Proceedings of the 33rd International Conference on Machine Learning}",
	volume        = 48,
	year          = 2016,
}

Contents (322 items)

ICML-2016-ShahZ #crowdsourcing #self
No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing (NBS, DZ), pp. 1–10.
ICML-2016-ShahBGW #modelling #statistics #transitive
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues (NBS, SB, AG, MJW), pp. 11–20.
ICML-2016-Weller #modelling #visual notation
Uprooting and Rerooting Graphical Models (AW), pp. 21–29.
ICML-2016-ShahamCDJNCK #approach #learning
A Deep Learning Approach to Unsupervised Ensemble Learning (US, XC, OD, AJ, BN, JTC, YK), pp. 30–39.
ICML-2016-YangCS #graph #learning
Revisiting Semi-Supervised Learning with Graph Embeddings (ZY, WWC, RS), pp. 40–48.
ICML-2016-FinnLA #learning #optimisation #policy
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (CF, SL, PA), pp. 49–58.
ICML-2016-XieZX #learning #modelling
Diversity-Promoting Bayesian Learning of Latent Variable Models (PX, JZ0, EPX), pp. 59–68.
ICML-2016-KandasamyY #approximate #parametricity
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA (KK, YY), pp. 69–78.
ICML-2016-LeeLO #probability #process
Hawkes Processes with Stochastic Excitations (YL, KWL, CSO), pp. 79–88.
ICML-2016-KhetanO #data-driven #performance #rank
Data-driven Rank Breaking for Efficient Rank Aggregation (AK, SO), pp. 89–98.
ICML-2016-BuloPK
Dropout distillation (SRB, LP, PK), pp. 99–107.
ICML-2016-FantiKORV
Metadata-conscious anonymous messaging (GCF, PK, SO, KR, PV), pp. 108–116.
ICML-2016-LiuZO #education #linear
The Teaching Dimension of Linear Learners (JL, XZ0, HO), pp. 117–126.
ICML-2016-CaragiannisPS
Truthful Univariate Estimators (IC, ADP, NS0), pp. 127–135.
ICML-2016-ArpitZNG #question #representation #why
Why Regularized Auto-Encoders learn Sparse Representation? (DA, YZ, HQN0, VG), pp. 136–144.
ICML-2016-NockCBN
k-variates++: more pluses in the k-means++ (RN, RC, RB, FN), pp. 145–154.
ICML-2016-RosenskiSS #approach #multi
Multi-Player Bandits - a Musical Chairs Approach (JR, OS, LS), pp. 155–163.
ICML-2016-SteegG
The Information Sieve (GVS, AG), pp. 164–172.
ICML-2016-AmodeiABCCCCCCD #recognition #speech
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (DA, SA, RA, JB, EB, CC, JC, BC, JC, MC, AC, GD, EE, JHE, LF, CF, AYH, BJ, TH, PL, XL, LL, SN, AYN, SO, RP, SQ, JR, SS, DS, SS, CW0, YW, ZW, BX, YX, DY, JZ, ZZ), pp. 173–182.
ICML-2016-ZhangGR #consistency #feature model #on the
On the Consistency of Feature Selection With Lasso for Non-linear Targets (YZ, WG, SR), pp. 183–191.
ICML-2016-Metzen #multi #optimisation
Minimum Regret Search for Single- and Multi-Task Optimization (JHM), pp. 192–200.
ICML-2016-Gilad-BachrachD #named #network #throughput
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy (RGB, ND, KL, KEL, MN, JW), pp. 201–210.
ICML-2016-VladymyrovC #problem #scalability
The Variational Nystrom method for large-scale spectral problems (MV, MÁCP), pp. 211–220.
ICML-2016-LiOW #multi #network
Multi-Bias Non-linear Activation in Deep Neural Networks (HL, WO, XW0), pp. 221–229.
ICML-2016-LeeYH #learning #multi #symmetry
Asymmetric Multi-task Learning based on Task Relatedness and Confidence (GL, EY, SJH), pp. 230–238.
ICML-2016-Fan #estimation #fault #performance #robust
Accurate Robust and Efficient Error Estimation for Decision Trees (LF), pp. 239–247.
ICML-2016-Shamir #algorithm #convergence #performance #probability
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity (OS), pp. 248–256.
ICML-2016-Shamir16a #convergence #probability
Convergence of Stochastic Gradient Descent for PCA (OS), pp. 257–265.
ICML-2016-LanGBS #education #named
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data (ASL, TG, RGB, CS), pp. 266–275.
ICML-2016-LiuLJ #kernel #testing
A Kernelized Stein Discrepancy for Goodness-of-fit Tests (QL, JDL, MIJ), pp. 276–284.
ICML-2016-XueEBGS #fourier
Variable Elimination in the Fourier Domain (YX, SE, RLB, CPG, BS), pp. 285–294.
ICML-2016-LiCLYSC #approximate #matrix #rank
Low-Rank Matrix Approximation with Stability (DL, CC0, QL, JY, LS, SMC), pp. 295–303.
ICML-2016-MenonO #estimation
Linking losses for density ratio and class-probability estimation (AKM, CSO), pp. 304–313.
ICML-2016-ReddiHSPS #optimisation #probability #reduction
Stochastic Variance Reduction for Nonconvex Optimization (SJR, AH, SS, BP, AJS), pp. 314–323.
ICML-2016-RanganathTB #modelling
Hierarchical Variational Models (RR, DT, DMB), pp. 324–333.
ICML-2016-AdamsSTPKM #data type #random #smarttech
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams (RJA, NS, ET, AP, SK0, BMM), pp. 334–343.
ICML-2016-ChoromanskaCBJK
Binary embeddings with structured hashed projections (AC, KC, MB, TJ, SK, YL), pp. 344–353.
ICML-2016-MandtHB #algorithm #analysis #probability
A Variational Analysis of Stochastic Gradient Algorithms (SM, MDH, DMB), pp. 354–363.
ICML-2016-Gopal #adaptation
Adaptive Sampling for SGD by Exploiting Side Information (SG), pp. 364–372.
ICML-2016-YuL #learning #multi #performance
Learning from Multiway Data: Simple and Efficient Tensor Regression (RY, YL0), pp. 373–381.
ICML-2016-HoangHL #distributed #framework #modelling #parallel #process
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models (TNH, QMH, BKHL), pp. 382–391.
ICML-2016-ZhangYJXZ #feedback #linear #online #optimisation #probability
Online Stochastic Linear Optimization under One-bit Feedback (LZ0, TY, RJ, YX, ZHZ), pp. 392–401.
ICML-2016-JenattonHA #adaptation #algorithm #constraints #online #optimisation
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints (RJ, JCH, CA), pp. 402–411.
ICML-2016-SinglaTK #elicitation #learning
Actively Learning Hemimetrics with Applications to Eliciting User Preferences (AS, ST, AK0), pp. 412–420.
ICML-2016-ZarembaMJF #algorithm #learning
Learning Simple Algorithms from Examples (WZ, TM, AJ, RF), pp. 421–429.
ICML-2016-LererGF #learning #physics
Learning Physical Intuition of Block Towers by Example (AL, SG, RF), pp. 430–438.
ICML-2016-LiuSSF #learning #markov #network
Structure Learning of Partitioned Markov Networks (SL0, TS, MS, KF), pp. 439–448.
ICML-2016-YangZJY #learning #online
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient (TY, LZ0, RJ, JY), pp. 449–457.
ICML-2016-PodosinnikovaBL #modelling #multi
Beyond CCA: Moment Matching for Multi-View Models (AP, FRB, SLJ), pp. 458–467.
ICML-2016-UbaruS #matrix #performance #rank #scalability
Fast methods for estimating the Numerical rank of large matrices (SU, YS), pp. 468–477.
ICML-2016-XieGF #analysis #clustering
Unsupervised Deep Embedding for Clustering Analysis (JX, RBG, AF), pp. 478–487.
ICML-2016-Kasiviswanathan #empirical #learning #performance
Efficient Private Empirical Risk Minimization for High-dimensional Learning (SPK, HJ), pp. 488–497.
ICML-2016-VojnovicY #estimation #modelling #parametricity
Parameter Estimation for Generalized Thurstone Choice Models (MV, SYY), pp. 498–506.
ICML-2016-LiuWYY #network
Large-Margin Softmax Loss for Convolutional Neural Networks (WL, YW, ZY, MY0), pp. 507–516.
ICML-2016-CouilletWAS #approach #matrix #network #random
A Random Matrix Approach to Echo-State Neural Networks (RC, GW, HTA, HS), pp. 517–525.
ICML-2016-JohnsonZ #categorisation #using
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings (RJ, TZ0), pp. 526–534.
ICML-2016-OkOSY #classification #crowdsourcing
Optimality of Belief Propagation for Crowdsourced Classification (JO, SO, JS, YY), pp. 535–544.
ICML-2016-VinogradskaBNRS #modelling #process
Stability of Controllers for Gaussian Process Forward Models (JV, BB, DNT, AR, HS, JP0), pp. 545–554.
ICML-2016-HammCB #learning #multi
Learning privately from multiparty data (JH, YC, MB), pp. 555–563.
ICML-2016-WeiWRC #morphism #network
Network Morphism (TW, CW, YR, CWC), pp. 564–572.
ICML-2016-GrosseM #approximate #matrix
A Kronecker-factored approximate Fisher matrix for convolution layers (RBG, JM), pp. 573–582.
ICML-2016-RaviIJS #design #linear #modelling
Experimental Design on a Budget for Sparse Linear Models and Applications (SNR, VKI, SCJ, VS), pp. 583–592.
ICML-2016-OsokinALDL #optimisation
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (AO, JBA, IL, PKD, SLJ), pp. 593–602.
ICML-2016-GaoLZ #crowdsourcing
Exact Exponent in Optimal Rates for Crowdsourcing (CG, YL, DZ), pp. 603–611.
ICML-2016-ZhangLL #classification #image #network #scalability
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification (YZ, KL, HL), pp. 612–621.
ICML-2016-ShenLX #clustering #online #rank #taxonomy
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit (JS0, PL0, HX), pp. 622–631.
ICML-2016-Curtis #algorithm #optimisation #probability #self
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization (FC), pp. 632–641.
ICML-2016-SimsekliBCR #monte carlo #probability
Stochastic Quasi-Newton Langevin Monte Carlo (US, RB, ATC, GR), pp. 642–651.
ICML-2016-JiangL #evaluation #learning #robust
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (NJ, LL0), pp. 652–661.
ICML-2016-QuXO #algorithm #analysis #optimisation #performance #probability
Fast Rate Analysis of Some Stochastic Optimization Algorithms (CQ, HX, CJO), pp. 662–670.
ICML-2016-LiM #nearest neighbour #performance
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing (KL, JM), pp. 671–679.
ICML-2016-LeKYC #learning #online #predict #sequence
Smooth Imitation Learning for Online Sequence Prediction (HML0, AK, YY, PC0), pp. 680–688.
ICML-2016-ChenKST #community #graph #locality
Community Recovery in Graphs with Locality (YC0, GMK, CS, DT), pp. 689–698.
ICML-2016-ZhuH #optimisation #performance #reduction
Variance Reduction for Faster Non-Convex Optimization (ZAZ, EH), pp. 699–707.
ICML-2016-PatriniNNC #learning #robust
Loss factorization, weakly supervised learning and label noise robustness (GP, FN, RN, MC), pp. 708–717.
ICML-2016-WangMCBPRGUA #analysis #matrix #network
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix (SW, ArM, RC, JAB, MP, MR, KG, GU, ÖA), pp. 718–726.
ICML-2016-ImaizumiH #parametricity
Doubly Decomposing Nonparametric Tensor Regression (MI, KH), pp. 727–736.
ICML-2016-Pedregosa #approximate #optimisation
Hyperparameter optimization with approximate gradient (FP), pp. 737–746.
ICML-2016-Shalev-Shwartz
SDCA without Duality, Regularization, and Individual Convexity (SSS), pp. 747–754.
ICML-2016-AnavaM #sequence
Heteroscedastic Sequences: Beyond Gaussianity (OA, SM), pp. 755–763.
ICML-2016-ZhengTDZ #approach #collaboration
A Neural Autoregressive Approach to Collaborative Filtering (YZ, BT, WD, HZ), pp. 764–773.
ICML-2016-SafranS #network #on the #quality
On the Quality of the Initial Basin in Overspecified Neural Networks (IS, OS), pp. 774–782.
ICML-2016-DunnerFTJ
Primal-Dual Rates and Certificates (CD, SF, MT, MJ), pp. 783–792.
ICML-2016-Shalev-ShwartzW #how #why
Minimizing the Maximal Loss: How and Why (SSS, YW), pp. 793–801.
ICML-2016-Pimentel-Alarcon #clustering #requirements
The Information-Theoretic Requirements of Subspace Clustering with Missing Data (DLPA, RDN), pp. 802–810.
ICML-2016-CohenHK #feedback #graph #learning #online
Online Learning with Feedback Graphs Without the Graphs (AC, TH, TK), pp. 811–819.
ICML-2016-GlaudeP #automaton #learning #probability
PAC learning of Probabilistic Automaton based on the Method of Moments (HG, OP), pp. 820–829.
ICML-2016-MelnykB #modelling
Estimating Structured Vector Autoregressive Models (IM, AB), pp. 830–839.
ICML-2016-Tosh #strict
Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends (CT), pp. 840–849.
ICML-2016-BlondelIFU #algorithm #network #performance #polynomial
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms (MB, MI, AF, NU), pp. 850–858.
ICML-2016-GermainHLM #adaptation
A New PAC-Bayesian Perspective on Domain Adaptation (PG, AH, FL, EM), pp. 859–868.
ICML-2016-PuleoM #bound #clustering #correlation #fault
Correlation Clustering and Biclustering with Locally Bounded Errors (GJP, OM), pp. 869–877.
ICML-2016-DavidS #algorithm #bound #performance #problem
PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem (YD, NS), pp. 878–887.
ICML-2016-ElhoseinyEBE #analysis #categorisation #comparative #estimation #modelling #multi
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation (ME, TEG, AB, AME), pp. 888–897.
ICML-2016-CarrGL #energy #named #optimisation
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces (SC, RG, CL), pp. 898–907.
ICML-2016-ArjevaniS #algorithm #complexity #first-order #on the #optimisation
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms (YA, OS), pp. 908–916.
ICML-2016-LiZALH #learning #optimisation #probability
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning (XL, TZ, RA, HL0, JDH), pp. 917–925.
ICML-2016-Wipf #analysis #estimation #rank
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation (DPW), pp. 926–935.
ICML-2016-NewlingF #bound #performance
Fast k-means with accurate bounds (JN, FF), pp. 936–944.
ICML-2016-RavanbakhshPG #matrix #message passing
Boolean Matrix Factorization and Noisy Completion via Message Passing (SR, BP, RG), pp. 945–954.
ICML-2016-CohenS #network
Convolutional Rectifier Networks as Generalized Tensor Decompositions (NC, AS), pp. 955–963.
ICML-2016-TuBSSR #equation #linear #matrix #rank
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow (ST, RB, MS, MS, BR), pp. 964–973.
ICML-2016-JunN #multi #using
Anytime Exploration for Multi-armed Bandits using Confidence Information (KSJ, RDN), pp. 974–982.
ICML-2016-BelangerM #energy #network #predict
Structured Prediction Energy Networks (DB, AM), pp. 983–992.
ICML-2016-ZhangLJ #network #polynomial
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time (YZ0, JDL, MIJ), pp. 993–1001.
ICML-2016-TremblayPGV #clustering
Compressive Spectral Clustering (NT, GP, RG, PV), pp. 1002–1011.
ICML-2016-KasaiM #approach #rank
Low-rank tensor completion: a Riemannian manifold preconditioning approach (HK, BM), pp. 1012–1021.
ICML-2016-ZhangCL #retrieval
Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow (HZ, YC, YL), pp. 1022–1031.
ICML-2016-DEramoRN #approximate
Estimating Maximum Expected Value through Gaussian Approximation (CD, MR, AN), pp. 1032–1040.
ICML-2016-OswalCRRN #learning #network #similarity
Representational Similarity Learning with Application to Brain Networks (UO, CRC, MALR, TTR, RDN), pp. 1041–1049.
ICML-2016-GalG #approximate #learning #nondeterminism #representation
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (YG, ZG), pp. 1050–1059.
ICML-2016-ReedAYLSL #generative #image #synthesis
Generative Adversarial Text to Image Synthesis (SER, ZA, XY, LL, BS, HL), pp. 1060–1069.
ICML-2016-PrabhakaranACP #process
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data (SP, EA, AC, DP), pp. 1070–1079.
ICML-2016-ZhuY
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives (ZAZ, YY), pp. 1080–1089.
ICML-2016-BhowmikGK #parametricity
Sparse Parameter Recovery from Aggregated Data (AB, JG, OK), pp. 1090–1099.
ICML-2016-ZhaiCLZ #detection #energy #modelling
Deep Structured Energy Based Models for Anomaly Detection (SZ, YC, WL, ZZ), pp. 1100–1109.
ICML-2016-ZhuQRY #coordination #performance #using
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling (ZAZ, ZQ, PR, YY), pp. 1110–1119.
ICML-2016-ArjovskySB #evolution #network
Unitary Evolution Recurrent Neural Networks (MA, AS, YB), pp. 1120–1128.
ICML-2016-ZhangP #feature model #markov #modelling
Markov Latent Feature Models (AZ, JWP), pp. 1129–1137.
ICML-2016-WangWP #probability
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks (YW, CW, WBP), pp. 1138–1147.
ICML-2016-AsterisKKP #algorithm
A Simple and Provable Algorithm for Sparse Diagonal CCA (MA, AK, OK, RAP), pp. 1148–1157.
ICML-2016-LiuWS #constraints #convergence #linear #optimisation #orthogonal #polynomial
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods (HL, WW, AMCS), pp. 1158–1167.
ICML-2016-ArpitZKG #network #normalisation #parametricity
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks (DA, YZ, BUK, VG), pp. 1168–1176.
ICML-2016-LiZZ #learning #memory management
Learning to Generate with Memory (CL, JZ0, BZ0), pp. 1177–1186.
ICML-2016-FernandoG #classification #learning #video
Learning End-to-end Video Classification with Rank-Pooling (BF, SG), pp. 1187–1196.
ICML-2016-SunVBB #learning #predict
Learning to Filter with Predictive State Inference Machines (WS0, AV, BB, JAB), pp. 1197–1205.
ICML-2016-RahmaniA #approach #composition #learning #matrix #performance
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling (MR, GKA), pp. 1206–1214.
ICML-2016-KatariyaKSW #learning #multi #rank
DCM Bandits: Learning to Rank with Multiple Clicks (SK, BK, CS, ZW), pp. 1215–1224.
ICML-2016-HardtRS #performance #probability
Train faster, generalize better: Stability of stochastic gradient descent (MH, BR, YS), pp. 1225–1234.
ICML-2016-KomiyamaHN #algorithm #bound #performance #problem
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm (JK, JH, HN), pp. 1235–1244.
ICML-2016-LiWZC #combinator
Contextual Combinatorial Cascading Bandits (SL0, BW, SZ, WC), pp. 1245–1253.
ICML-2016-WuSLS
Conservative Bandits (YW, RS, TL, CS), pp. 1254–1262.
ICML-2016-HazanL #optimisation #probability
Variance-Reduced and Projection-Free Stochastic Optimization (EH, HL), pp. 1263–1271.
ICML-2016-SongGC #learning #network #sequence
Factored Temporal Sigmoid Belief Networks for Sequence Learning (JS, ZG, LC), pp. 1272–1281.
ICML-2016-XuXCY #assessment #crowdsourcing #quality #ranking #statistics
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking (QX, JX, XC, YY0), pp. 1282–1291.
ICML-2016-BalduzziG #network
Strongly-Typed Recurrent Neural Networks (DB, MG), pp. 1292–1300.
ICML-2016-KordaSL #clustering #distributed #linear #network
Distributed Clustering of Linear Bandits in Peer to Peer Networks (NK, BS, SL), pp. 1301–1309.
ICML-2016-ZhaoAGA #network
Collapsed Variational Inference for Sum-Product Networks (HZ0, TA, GJG, BA), pp. 1310–1318.
ICML-2016-KhandelwalLNS #analysis #monte carlo #on the
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search (PK, EL, SN, PS), pp. 1319–1328.
ICML-2016-DuanCHSA #benchmark #learning #metric
Benchmarking Deep Reinforcement Learning for Continuous Control (YD, XC0, RH, JS, PA), pp. 1329–1338.
ICML-2016-DingLHL #clustering #distributed
K-Means Clustering with Distributed Dimensions (HD, YL, LH, JL0), pp. 1339–1348.
ICML-2016-UlyanovLVL #image #network #synthesis
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images (DU, VL, AV, VSL), pp. 1349–1357.
ICML-2016-MirzasoleimanBK #performance #personalisation #summary
Fast Constrained Submodular Maximization: Personalized Data Summarization (BM, AB, AK), pp. 1358–1367.
ICML-2016-WangGL #on the #statistics
On the Statistical Limits of Convex Relaxations (ZW, QG, HL0), pp. 1368–1377.
ICML-2016-KumarIOIBGZPS #memory management #natural language #network
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (AK, OI, PO, MI, JB0, IG, VZ, RP, RS), pp. 1378–1387.
ICML-2016-ColinBSC #distributed #optimisation
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (IC, AB, JS, SC), pp. 1388–1396.
ICML-2016-GonenOS #sketching #using
Solving Ridge Regression using Sketched Preconditioned SVRG (AG, FO, SSS), pp. 1397–1405.
ICML-2016-AJFMS #cumulative #learning #predict
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control (PLA, CJ, MCF0, SIM, CS), pp. 1406–1415.
ICML-2016-PlataniosDM #approach
Estimating Accuracy from Unlabeled Data: A Bayesian Approach (EAP, AD, TMM), pp. 1416–1425.
ICML-2016-BhattacharyaGKP #matrix
Non-negative Matrix Factorization under Heavy Noise (CB, NG, RK, JP), pp. 1426–1434.
ICML-2016-JasinskaDBPKH #probability #using
Extreme F-measure Maximization using Sparse Probability Estimates (KJ, KD, RBF, KP, TK, EH), pp. 1435–1444.
ICML-2016-MaaloeSSW #generative #modelling
Auxiliary Deep Generative Models (LM, CKS, SKS, OW), pp. 1445–1453.
ICML-2016-CanevetJF #empirical #scalability
Importance Sampling Tree for Large-scale Empirical Expectation (OC, CJ, FF), pp. 1454–1462.
ICML-2016-DaneshmandLH #adaptation #learning
Starting Small - Learning with Adaptive Sample Sizes (HD, AL, TH), pp. 1463–1471.
ICML-2016-BuiHHLT #approximate #process #using
Deep Gaussian Processes for Regression using Approximate Expectation Propagation (TDB, DHL, JMHL, YL, RET), pp. 1472–1481.
ICML-2016-MitrovicST #approximate #kernel #named
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression (JM, DS, YWT), pp. 1482–1491.
ICML-2016-Hernandez-Lobato #multi #optimisation #predict
Predictive Entropy Search for Multi-objective Bayesian Optimization (DHL, JMHL, AS, RPA), pp. 1492–1501.
ICML-2016-GeZ #analysis #component
Rich Component Analysis (RG0, JZ), pp. 1502–1510.
ICML-2016-Hernandez-Lobato16a #black box
Black-Box Alpha Divergence Minimization (JMHL, YL, MR, TDB, DHL, RET), pp. 1511–1520.
ICML-2016-RezendeMDGW #generative #modelling
One-Shot Generalization in Deep Generative Models (DJR, SM, ID, KG, DW), pp. 1521–1529.
ICML-2016-NatarajanKRD #classification #multi
Optimal Classification with Multivariate Losses (NN, OK, PR, ISD), pp. 1530–1538.
ICML-2016-MalherbeCV #approach #optimisation #ranking
A ranking approach to global optimization (CM, EC, NV), pp. 1539–1547.
ICML-2016-WangSDNSX #algorithm #coordination #distributed #parallel
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms (YXW, VS, WD0, WN, SS, EPX), pp. 1548–1557.
ICML-2016-LarsenSLW #encoding #metric #similarity #using
Autoencoding beyond pixels using a learned similarity metric (ABLL, SKS, HL, OW), pp. 1558–1566.
ICML-2016-SaRO #agile #bias
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling (CDS, CR, KO), pp. 1567–1576.
ICML-2016-ShibagakiKHT #modelling
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling (AS, MK, KH, IT), pp. 1577–1586.
ICML-2016-DegenneP #algorithm #multi #probability
Anytime optimal algorithms in stochastic multi-armed bandits (RD, VP), pp. 1587–1595.
ICML-2016-HoilesS #bound #design #education #evaluation #recommendation
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design (WH, MvdS), pp. 1596–1604.
ICML-2016-PandeyD #metric #on the #random #representation
On collapsed representation of hierarchical Completely Random Measures (GP0, AD), pp. 1605–1613.
ICML-2016-MartinsA #classification #multi
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification (AFTM, RFA), pp. 1614–1623.
ICML-2016-BubeckL #black box #optimisation
Black-box Optimization with a Politician (SB, YTL), pp. 1624–1631.
ICML-2016-KanagawaSKST #parametricity #process
Gaussian process nonparametric tensor estimator and its minimax optimality (HK, TS, HK, NS, YT), pp. 1632–1641.
ICML-2016-MedinaY #algorithm #linear
No-Regret Algorithms for Heavy-Tailed Linear Bandits (AMM, SY), pp. 1642–1650.
ICML-2016-BonillaSR
Extended and Unscented Kitchen Sinks (EVB, DMS, AR0), pp. 1651–1659.
ICML-2016-XuZCL #matrix #optimisation #probability
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization (ZX, PZ, JC, XL0), pp. 1660–1669.
ICML-2016-SchnabelSSCJ #evaluation #learning #recommendation
Recommendations as Treatments: Debiasing Learning and Evaluation (TS, AS, AS, NC, TJ), pp. 1670–1679.
ICML-2016-YoonAHS #named #predict
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission (JY, AMA, SH, MvdS), pp. 1680–1689.
ICML-2016-LocatelliGC #algorithm #problem
An optimal algorithm for the Thresholding Bandit Problem (AL, MG, AC), pp. 1690–1698.
ICML-2016-NiuRFH #parametricity #performance #using
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching (MN, SR, MF, DH), pp. 1699–1707.
ICML-2016-LouizosW #learning #matrix #performance
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (CL, MW), pp. 1708–1716.
ICML-2016-XuFZ #learning #process
Learning Granger Causality for Hawkes Processes (HX, MF, HZ), pp. 1717–1726.
ICML-2016-MiaoYB
Neural Variational Inference for Text Processing (YM, LY, PB), pp. 1727–1736.
ICML-2016-MenschMTV #learning #matrix #taxonomy
Dictionary Learning for Massive Matrix Factorization (AM, JM, BT, GV), pp. 1737–1746.
ICML-2016-OordKK #network
Pixel Recurrent Neural Networks (AvdO, NK, KK), pp. 1747–1756.
ICML-2016-SimsekAK #problem #why
Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As Well (ÖS, SA, AK), pp. 1757–1765.
ICML-2016-LiSJ #matrix
Gaussian quadrature for matrix inverse forms with applications (CL, SS, SJ), pp. 1766–1775.
ICML-2016-MeshiMWS #predict
Train and Test Tightness of LP Relaxations in Structured Prediction (OM, MM, AW, DAS), pp. 1776–1785.
ICML-2016-AroraMM #learning #multi #optimisation #probability #representation #using
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares (RA, PM, TVM), pp. 1786–1794.
ICML-2016-BasbugE
Hierarchical Compound Poisson Factorization (MEB, BEE), pp. 1795–1803.
ICML-2016-HeB #learning #modelling
Opponent Modeling in Deep Reinforcement Learning (HH0, JLBG), pp. 1804–1813.
ICML-2016-WangDL #linear #modelling
No penalty no tears: Least squares in high-dimensional linear models (XW0, DBD, CL), pp. 1814–1822.
ICML-2016-QuRTF #empirical #named #probability
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization (ZQ, PR, MT, OF), pp. 1823–1832.
ICML-2016-HazanLS #on the #optimisation #probability #problem
On Graduated Optimization for Stochastic Non-Convex Problems (EH, KYL, SSS), pp. 1833–1841.
ICML-2016-SantoroBBWL #network
Meta-Learning with Memory-Augmented Neural Networks (AS, SB, MB, DW, TPL), pp. 1842–1850.
ICML-2016-DaiB #multi
The knockoff filter for FDR control in group-sparse and multitask regression (RD, RB), pp. 1851–1859.
ICML-2016-PerolatPGSP #approximate #game studies #markov #policy
Softened Approximate Policy Iteration for Markov Games (JP, BP, MG, BS, OP), pp. 1860–1868.
ICML-2016-GowerGR #probability
Stochastic Block BFGS: Squeezing More Curvature out of Data (RMG, DG, PR), pp. 1869–1878.
ICML-2016-BaiRWS #classification #difference #geometry #learning
Differential Geometric Regularization for Supervised Learning of Classifiers (QB, SR, ZW, SS), pp. 1879–1888.
ICML-2016-DielemanFK #network #symmetry
Exploiting Cyclic Symmetry in Convolutional Neural Networks (SD, JDF, KK), pp. 1889–1898.
ICML-2016-ZahavyBM #black box #comprehension
Graying the black box: Understanding DQNs (TZ, NBZ, SM), pp. 1899–1908.
ICML-2016-FriesenD #learning #modelling #theorem
The Sum-Product Theorem: A Foundation for Learning Tractable Models (ALF, PMD), pp. 1909–1918.
ICML-2016-ShahG #correlation #learning
Pareto Frontier Learning with Expensive Correlated Objectives (AS, ZG), pp. 1919–1927.
ICML-2016-MnihBMGLHSK #learning
Asynchronous Methods for Deep Reinforcement Learning (VM, APB, MM, AG, TPL, TH, DS, KK), pp. 1928–1937.
ICML-2016-VeldtGM
A Simple and Strongly-Local Flow-Based Method for Cut Improvement (NV, DFG, MWM), pp. 1938–1947.
ICML-2016-SuLCC #learning #modelling #statistics #visual notation
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (QS, XL, CC, LC), pp. 1948–1957.
ICML-2016-KawakitaT #learning
Barron and Cover's Theory in Supervised Learning and its Application to Lasso (MK, JT), pp. 1958–1966.
ICML-2016-MichaeliWL #analysis #canonical #correlation #parametricity
Nonparametric Canonical Correlation Analysis (TM, WW, KL), pp. 1967–1976.
ICML-2016-RakhlinS #named #performance
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits (AR, KS), pp. 1977–1985.
ICML-2016-DanihelkaWUKG #memory management
Associative Long Short-Term Memory (ID, GW, BU, NK, AG), pp. 1986–1994.
ICML-2016-WangSHHLF #architecture #learning #network
Dueling Network Architectures for Deep Reinforcement Learning (ZW0, TS, MH, HvH, ML, NdF), pp. 1995–2003.
ICML-2016-KusanoHF #data analysis #kernel #persistent
Persistence weighted Gaussian kernel for topological data analysis (GK, YH, KF), pp. 2004–2013.
ICML-2016-NiepertAK #graph #learning #network
Learning Convolutional Neural Networks for Graphs (MN, MA, KK), pp. 2014–2023.
ICML-2016-DiamosSCCCEEHS #persistent
Persistent RNNs: Stashing Recurrent Weights On-Chip (GD, SS, BC, MC, AC, EE, JHE, AYH, SS), pp. 2024–2033.
ICML-2016-HenaffSL #network #orthogonal
Recurrent Orthogonal Networks and Long-Memory Tasks (MH, AS, YL), pp. 2034–2042.
ICML-2016-BauerSP #multi
The Arrow of Time in Multivariate Time Series (SB, BS, JP), pp. 2043–2051.
ICML-2016-RamaswamyST #estimation #kernel
Mixture Proportion Estimation via Kernel Embeddings of Distributions (HGR, CS, AT), pp. 2052–2060.
ICML-2016-LiJS #kernel #performance
Fast DPP Sampling for Nystrom with Application to Kernel Methods (CL, SJ, SS), pp. 2061–2070.
ICML-2016-TrouillonWRGB #predict
Complex Embeddings for Simple Link Prediction (TT, JW, SR0, ÉG, GB), pp. 2071–2080.
ICML-2016-VikramD #clustering #interactive
Interactive Bayesian Hierarchical Clustering (SV, SD), pp. 2081–2090.
ICML-2016-AllamanisPS #network #source code #summary
A Convolutional Attention Network for Extreme Summarization of Source Code (MA, HP, CAS), pp. 2091–2100.
ICML-2016-KapralovPW #how #matrix #multi
How to Fake Multiply by a Gaussian Matrix (MK, VKP, DPW), pp. 2101–2110.
ICML-2016-RogersVLG #independence #testing
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing (MG, HWL, RMR, SPV), pp. 2111–2120.
ICML-2016-ErraqabiVCM
Pliable Rejection Sampling (AE, MV, AC, OAM), pp. 2121–2129.
ICML-2016-BalleGP #evaluation #policy
Differentially Private Policy Evaluation (BB, MG, DP), pp. 2130–2138.
ICML-2016-ThomasB #evaluation #learning #policy
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (PST, EB), pp. 2139–2148.
ICML-2016-WiatowskiTSGB #architecture #feature model
Discrete Deep Feature Extraction: A Theory and New Architectures (TW, MT, AS, PG, HB), pp. 2149–2158.
ICML-2016-SyrgkanisKS #algorithm #learning #performance
Efficient Algorithms for Adversarial Contextual Learning (VS, AK, RES), pp. 2159–2168.
ICML-2016-SongSZU #network
Training Deep Neural Networks via Direct Loss Minimization (YS, AGS, RSZ, RU), pp. 2169–2177.
ICML-2016-HwangS #sequence
Sequence to Sequence Training of CTC-RNNs with Partial Windowing (KH, WS), pp. 2178–2187.
ICML-2016-MnihR #monte carlo
Variational Inference for Monte Carlo Objectives (AM, DJR), pp. 2188–2196.
ICML-2016-DalalGM #grid
Hierarchical Decision Making In Electricity Grid Management (GD, EG, SM), pp. 2197–2206.
ICML-2016-BalkanskiMKS #combinator #learning
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization (EB, BM, AK0, YS), pp. 2207–2216.
ICML-2016-ShangSAL #comprehension #linear #network
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (WS, KS, DA, HL), pp. 2217–2225.
ICML-2016-WangXDS #process
Isotonic Hawkes Processes (YW0, BX0, ND, LS), pp. 2226–2234.
ICML-2016-LiuY #learning #multi
Cross-Graph Learning of Multi-Relational Associations (HL, YY), pp. 2235–2243.
ICML-2016-PanRAG #process
Markov-modulated Marked Poisson Processes for Check-in Data (JP, VAR, PKA, AEG), pp. 2244–2253.
ICML-2016-AchimSE #analysis #constraints #fourier
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference (TA, AS, SE), pp. 2254–2262.
ICML-2016-Papakonstantinou #learning #on the
On the Power and Limits of Distance-Based Learning (PAP, JX0, GY), pp. 2263–2271.
ICML-2016-YenLZRD #approach #multi #sequence
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (IEHY, XL, JZ, PR, ISD), pp. 2272–2280.
ICML-2016-FazayeliB #estimation
Generalized Direct Change Estimation in Ising Model Structure (FF, AB), pp. 2281–2290.
ICML-2016-ChiangHD #analysis #component #robust
Robust Principal Component Analysis with Side Information (KYC, CJH, ISD), pp. 2291–2299.
ICML-2016-GuiHG #estimation #matrix #performance #rank #towards
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation (HG, JH0, QG), pp. 2300–2309.
ICML-2016-SangnierGR #detection #proximity #reliability #representation #using
Early and Reliable Event Detection Using Proximity Space Representation (MS, JG, AR), pp. 2310–2319.
ICML-2016-LibertyLS #machine learning
Stratified Sampling Meets Machine Learning (EL, KJL, KS), pp. 2320–2329.
ICML-2016-GuanRW #learning #markov #multi #performance #process #recognition #using
Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model (XG, RR, WKW), pp. 2330–2339.
ICML-2016-LinCR #multi
Generalization Properties and Implicit Regularization for Multiple Passes SGM (JL, RC, LR), pp. 2340–2348.
ICML-2016-FrostigMMS #analysis #component
Principal Component Projection Without Principal Component Analysis (RF, CM, CM, AS), pp. 2349–2357.
ICML-2016-LiLR #approximate #rank
Recovery guarantee of weighted low-rank approximation via alternating minimization (YL, YL, AR), pp. 2358–2367.
ICML-2016-PezeshkiFBCB #architecture #network
Deconstructing the Ladder Network Architecture (MP, LF, PB, ACC, YB), pp. 2368–2376.
ICML-2016-OsbandRW #random
Generalization and Exploration via Randomized Value Functions (IO, BVR, ZW), pp. 2377–2386.
ICML-2016-KantchelianTJ #classification
Evasion and Hardening of Tree Ensemble Classifiers (AK, JDT, ADJ), pp. 2387–2396.
ICML-2016-XiongMS #memory management #network #visual notation
Dynamic Memory Networks for Visual and Textual Question Answering (CX, SM, RS), pp. 2397–2406.
ICML-2016-RavanbakhshOFPH #matter #parametricity
Estimating Cosmological Parameters from the Dark Matter Distribution (SR, JBO, SF, LP, SH, JGS, BP), pp. 2407–2416.
ICML-2016-HashimotoGJ #generative #learning
Learning Population-Level Diffusions with Generative RNNs (TBH, DKG, TSJ), pp. 2417–2426.
ICML-2016-PanS #network
Expressiveness of Rectifier Networks (XP, VS), pp. 2427–2435.
ICML-2016-KairouzBR #estimation #privacy
Discrete Distribution Estimation under Local Privacy (PK, KB, DR), pp. 2436–2444.
ICML-2016-InouyeRD #dependence #exponential #modelling #multi #product line #visual notation
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies (DII, PR, ISD), pp. 2445–2453.
ICML-2016-LimW #approach #permutation #problem
A Box-Constrained Approach for Hard Permutation Problems (CHL, SW), pp. 2454–2463.
ICML-2016-ZadehHS #geometry #learning #metric
Geometric Mean Metric Learning (PZ, RH, SS), pp. 2464–2471.
ICML-2016-YangWLEZ #estimation #parametricity
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity (ZY, ZW, HL0, YCE, TZ0), pp. 2472–2481.
ICML-2016-LiWPA #classification #multi
Conditional Bernoulli Mixtures for Multi-label Classification (CL, BW, VP, JAA), pp. 2482–2491.
ICML-2016-ChenG #multi #problem #scalability
Scalable Discrete Sampling as a Multi-Armed Bandit Problem (YC, ZG), pp. 2492–2501.
ICML-2016-ChoromanskiS #kernel #sublinear
Recycling Randomness with Structure for Sublinear time Kernel Expansions (KC, VS), pp. 2502–2510.
ICML-2016-BornscheinSFB #bidirectional
Bidirectional Helmholtz Machines (JB, SS, AF, YB), pp. 2511–2519.
ICML-2016-AbernethyH #optimisation #performance
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier (JDA, EH), pp. 2520–2528.
ICML-2016-CutajarOCF #kernel #matrix
Preconditioning Kernel Matrices (KC, MAO, JPC, MF), pp. 2529–2538.
ICML-2016-AltschulerBFMRZ #algorithm #bound #distributed #set
Greedy Column Subset Selection: New Bounds and Distributed Algorithms (JA, AB, GF, VSM, AR, MZ), pp. 2539–2548.
ICML-2016-AlmahairiBCZLC #capacity #network
Dynamic Capacity Networks (AA, NB, TC, YZ, HL, ACC), pp. 2549–2558.
ICML-2016-HeidariMSVY
Pricing a Low-regret Seller (HH, MM, US, SV, SY), pp. 2559–2567.
ICML-2016-RaghunathanFDL #estimation #linear
Estimation from Indirect Supervision with Linear Moments (AR, RF, JCD, PL), pp. 2568–2577.
ICML-2016-BotteschBK #approximate
Speeding up k-means by approximating Euclidean distances via block vectors (TB, TB, MK), pp. 2578–2586.
ICML-2016-MussmannE #learning
Learning and Inference via Maximum Inner Product Search (SM, SE), pp. 2587–2596.
ICML-2016-RodomanovK #finite #optimisation
A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums (AR, DK), pp. 2597–2605.
ICML-2016-ChwialkowskiSG #kernel
A Kernel Test of Goodness of Fit (KC, HS, AG), pp. 2606–2615.
ICML-2016-RainforthNLPMDW #markov #monte carlo
Interacting Particle Markov Chain Monte Carlo (TR, CAN, FL, BP, JWvdM, AD, FDW), pp. 2616–2625.
ICML-2016-GarberHJKMNS #performance
Faster Eigenvector Computation via Shift-and-Invert Preconditioning (DG, EH, CJ, SMK, CM, PN, AS), pp. 2626–2634.
ICML-2016-XieLZW #formal method #generative
A Theory of Generative ConvNet (JX, YL0, SCZ, YNW), pp. 2635–2644.
ICML-2016-YaoK #learning #performance #product line
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity (QY, JTK), pp. 2645–2654.
ICML-2016-SiHD #approximate #performance #using
Computationally Efficient Nyström Approximation using Fast Transforms (SS, CJH, ISD), pp. 2655–2663.
ICML-2016-PeyreCS #distance #kernel #matrix
Gromov-Wasserstein Averaging of Kernel and Distance Matrices (GP, MC, JS), pp. 2664–2672.
ICML-2016-RoychowdhuryKP #monte carlo #robust #using
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics (AR, BK, SP0), pp. 2673–2681.
ICML-2016-SaeediHJA #infinity #performance
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM (AS, MDH, MJJ0, RPA), pp. 2682–2691.
ICML-2016-UstinovskiyFGS #learning
Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (YU, VF, GG, PS), pp. 2692–2701.
ICML-2016-DaiDS #modelling
Discriminative Embeddings of Latent Variable Models for Structured Data (HD, BD, LS), pp. 2702–2711.
ICML-2016-GuhaMRS #detection #random #robust
Robust Random Cut Forest Based Anomaly Detection on Streams (SG, NM, GR, OS), pp. 2712–2721.
ICML-2016-TaylorBXSPG #approach #network #scalability
Training Neural Networks Without Gradients: A Scalable ADMM Approach (GT, RB, ZX0, BS, ABP, TG), pp. 2722–2731.
ICML-2016-ChenQ #category theory #clustering
Clustering High Dimensional Categorical Data via Topographical Features (CC0, NQ), pp. 2732–2740.
ICML-2016-GeJKNS #algorithm #analysis #canonical #correlation #performance #scalability
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis (RG0, CJ, SMK, PN, AS), pp. 2741–2750.
ICML-2016-BaiIWB #algorithm #optimisation
Algorithms for Optimizing the Ratio of Submodular Functions (WB, RKI, KW, JAB), pp. 2751–2759.
ICML-2016-HoGE #learning #optimisation #policy
Model-Free Imitation Learning with Policy Optimization (JH, JKG, SE), pp. 2760–2769.
ICML-2016-CisseAB #architecture #named
ADIOS: Architectures Deep In Output Space (MC, MAS, SB), pp. 2770–2779.
ICML-2016-GaoKOV #axiom #capacity #dependence
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications (WG, SK, SO, PV), pp. 2780–2789.
ICML-2016-OhCSL #memory management
Control of Memory, Active Perception, and Action in Minecraft (JO, VC, SPS, HL), pp. 2790–2799.
ICML-2016-SuhZA #classification #complexity
The Label Complexity of Mixed-Initiative Classifier Training (JS, XZ0, SA), pp. 2800–2809.
ICML-2016-ScheinZBW #composition #learning
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations (AS, MZ, DMB, HMW), pp. 2810–2819.
ICML-2016-ColomboV #composition #matrix
Tensor Decomposition via Joint Matrix Schur Decomposition (NC, NV), pp. 2820–2828.
ICML-2016-GuLSL #modelling
Continuous Deep Q-Learning with Model-based Acceleration (SG, TPL, IS, SL), pp. 2829–2838.
ICML-2016-GongZLTGS #adaptation #component
Domain Adaptation with Conditional Transferable Components (MG, KZ0, TL, DT, CG, BS), pp. 2839–2848.
ICML-2016-LinTA #fixpoint #network
Fixed Point Quantization of Deep Convolutional Networks (DDL, SST, VSA), pp. 2849–2858.
ICML-2016-AroraGKMM #algorithm #modelling #topic
Provable Algorithms for Inference in Topic Models (SA, RG0, FK, TM, AM), pp. 2859–2867.
ICML-2016-WangWK #performance #programming
Epigraph projections for fast general convex programming (PWW, MW, JZK), pp. 2868–2877.
ICML-2016-AcharyaDLS #algorithm #performance
Fast Algorithms for Segmented Regression (JA, ID, JL0, LS), pp. 2878–2886.
ICML-2016-ThomasSDB
Energetic Natural Gradient Descent (PST, BCdS, CD, EB), pp. 2887–2895.
ICML-2016-CarlsonSPP
Partition Functions from Rao-Blackwellized Tempered Sampling (DEC, PS, AP, LP), pp. 2896–2905.
ICML-2016-ZhaoPX #learning #modelling
Learning Mixtures of Plackett-Luce Models (ZZ, PP, LX), pp. 2906–2914.
ICML-2016-AbelHL #abstraction #approximate #behaviour
Near Optimal Behavior via Approximate State Abstraction (DA, DEH, MLL), pp. 2915–2923.
ICML-2016-LeiF #order #power of #testing
Power of Ordered Hypothesis Testing (LL, WF), pp. 2924–2932.
ICML-2016-BielikRV #named #probability
PHOG: Probabilistic Model for Code (PB, VR, MTV), pp. 2933–2942.
ICML-2016-GyorgyS #matrix
Shifting Regret, Mirror Descent, and Matrices (AG, CS), pp. 2943–2951.
ICML-2016-LuketinaRBG #scalability
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters (JL, TR, MB, KG), pp. 2952–2960.
ICML-2016-AkrourNAA #learning #optimisation
Model-Free Trajectory Optimization for Reinforcement Learning (RA, GN, HA, AA), pp. 2961–2970.
ICML-2016-JiaoKS #distance
Controlling the distance to a Kemeny consensus without computing it (YJ, AK, ES), pp. 2971–2980.
ICML-2016-LucicBZK #scalability
Horizontally Scalable Submodular Maximization (ML, OB, MZ, AK0), pp. 2981–2989.
ICML-2016-CohenW #network
Group Equivariant Convolutional Networks (TC, MW), pp. 2990–2999.
ICML-2016-PiatkowskiM #probability
Stochastic Discrete Clenshaw-Curtis Quadrature (NP, KM), pp. 3000–3009.
ICML-2016-RiemerVCHHK #multi
Correcting Forecasts with Multifactor Neural Attention (MR, AV, FdPC, FFTHI, RH0, EK), pp. 3010–3019.
ICML-2016-JohanssonSS #learning
Learning Representations for Counterfactual Inference (FDJ, US, DAS), pp. 3020–3029.
ICML-2016-HwangTC #automation #modelling #multi #parametricity #relational
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series (YH, AT, JC), pp. 3030–3039.
ICML-2016-PaigeW #modelling #monte carlo #network #visual notation
Inference Networks for Sequential Monte Carlo in Graphical Models (BP, FDW), pp. 3040–3049.
ICML-2016-Bloem-ReddyC #slicing
Slice Sampling on Hamiltonian Trajectories (BBR, JC), pp. 3050–3058.
ICML-2016-GulcehreMDB
Noisy Activation Functions (ÇG, MM, MD, YB), pp. 3059–3068.
ICML-2016-YenHRZD #approach #classification #multi
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification (IEHY, XH, PR, KZ, ISD), pp. 3069–3077.

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