Proceedings of the 30th International Conference on Machine Learning, Cycle 1
ICML c1, 2013.

@proceedings{ICML-c1-2013,
	address       = "Atlanta, Georgia, USA",
	ee            = "http://jmlr.org/proceedings/papers/v28/",
	publisher     = "{JMLR.org}",
	series        = "{JMLR Proceedings}",
	title         = "{Proceedings of the 30th International Conference on Machine Learning, Cycle 1}",
	volume        = 28,
	year          = 2013,
}

Contents (74 items)

21 ×#learning
11 ×#optimisation
9 ×#performance
7 ×#modelling
7 ×#multi
6 ×#classification
6 ×#probability
5 ×#adaptation
5 ×#algorithm
5 ×#approach

ICML-c1-2013-SznitmanLFJF #locality #policy

An Optimal Policy for Target Localization with Application to Electron Microscopy (RS, AL, PIF, BJ, PF), pp. 1–9.

ICML-c1-2013-MuandetBS #invariant #representation

Domain Generalization via Invariant Feature Representation (KM, DB, BS), pp. 10–18.

ICML-c1-2013-BootsG #approach #learning

A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.

ICML-c1-2013-KumarLVV #bound

Near-Optimal Bounds for Cross-Validation via Loss Stability (RK, DL, SV, AV), pp. 27–35.

ICML-c1-2013-MehtaG #bound #predict

Sparsity-Based Generalization Bounds for Predictive Sparse Coding (NAM, AGG), pp. 36–44.

ICML-c1-2013-ZhangC #analysis #linear

Sparse Uncorrelated Linear Discriminant Analysis (XZ, DC), pp. 45–52.

ICML-c1-2013-Lacoste-JulienJSP #coordination #optimisation

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (SLJ, MJ, MWS, PP), pp. 53–61.

ICML-c1-2013-Hennig #optimisation #performance #probability

Fast Probabilistic Optimization from Noisy Gradients (PH), pp. 62–70.

ICML-c1-2013-Shamir0 #convergence #optimisation #probability

Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes (OS, TZ), pp. 71–79.

ICML-c1-2013-OuyangHTG #multi #probability

Stochastic Alternating Direction Method of Multipliers (HO, NH, LT, AGG), pp. 80–88.

ICML-c1-2013-WangX #clustering

Noisy Sparse Subspace Clustering (YXW, HX), pp. 89–97.

ICML-c1-2013-WilliamsonDX #markov #modelling #monte carlo #parallel #parametricity

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models (SW, AD, EPX), pp. 98–106.

ICML-c1-2013-GiguereLMS #algorithm #approach #bound #learning #predict

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction (SG, FL, MM, KS), pp. 107–114.

ICML-c1-2013-BergstraYC #architecture #optimisation

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures (JB, DY, DDC), pp. 115–123.

ICML-c1-2013-ZhuCPZ #algorithm #modelling #performance #topic

Gibbs Max-Margin Topic Models with Fast Sampling Algorithms (JZ, NC, HP, BZ), pp. 124–132.

ICML-c1-2013-XuKWC #classification

Cost-Sensitive Tree of Classifiers (ZEX, MJK, KQW, MC), pp. 133–141.

ICML-c1-2013-LiLSHD #generative #learning #using

Learning Hash Functions Using Column Generation (XL, GL, CS, AvdH, ARD), pp. 142–150.

ICML-c1-2013-ChenWY #combinator #framework #multi

Combinatorial Multi-Armed Bandit: General Framework and Applications (WC, YW, YY), pp. 151–159.

ICML-c1-2013-ChenK #adaptation #learning #optimisation

Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.

ICML-c1-2013-DoK

Convex formulations of radius-margin based Support Vector Machines (HD, AK), pp. 169–177.

ICML-c1-2013-HamiltonFP #modelling #predict

Modelling Sparse Dynamical Systems with Compressed Predictive State Representations (WLH, MMF, JP), pp. 178–186.

ICML-c1-2013-MenonTGLK #framework #machine learning #programming

A Machine Learning Framework for Programming by Example (AKM, OT, SG, BWL, AK), pp. 187–195.

ICML-c1-2013-GirshickSD

Discriminatively Activated Sparselets (RBG, HOS, TD), pp. 196–204.

ICML-c1-2013-PeleTGW #classification #performance

The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification (OP, BT, AG, MW), pp. 205–213.

ICML-c1-2013-LiWWT #fixpoint

Fixed-Point Model For Structured Labeling (QL, JW, DPW, ZT), pp. 214–221.

ICML-c1-2013-GongGS #adaptation #invariant #learning

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation (BG, KG, FS), pp. 222–230.

ICML-c1-2013-KumarSK #algorithm #matrix #performance

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization (AK, VS, PK), pp. 231–239.

ICML-c1-2013-HanL13a #analysis #component

Principal Component Analysis on non-Gaussian Dependent Data (FH, HL), pp. 240–248.

ICML-c1-2013-AnandkumarHJK #learning #linear #network

Learning Linear Bayesian Networks with Latent Variables (AA, DH, AJ, SK), pp. 249–257.

ICML-c1-2013-BubeckWV #identification #multi

Multiple Identifications in Multi-Armed Bandits (SB, TW, NV), pp. 258–265.

ICML-c1-2013-CotterSS #learning

Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.

ICML-c1-2013-HeaukulaniG #modelling #network #probability #social

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks (CH, ZG), pp. 275–283.

ICML-c1-2013-XiangTY #feature model #optimisation #performance

Efficient Sparse Group Feature Selection via Nonconvex Optimization (SX, XT, JY), pp. 284–292.

ICML-c1-2013-XiaoG #adaptation #probability #sequence

Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model (MX, YG), pp. 293–301.

ICML-c1-2013-ChenWC

Maximum Variance Correction with Application to A* Search (WC, KQW, YC), pp. 302–310.

ICML-c1-2013-WongAF #adaptation #modelling #visual notation

Adaptive Sparsity in Gaussian Graphical Models (EW, SPA, PTF), pp. 311–319.

ICML-c1-2013-GrinbergP #optimisation

Average Reward Optimization Objective In Partially Observable Domains (YG, DP), pp. 320–328.

ICML-c1-2013-KolarL #classification #feature model

Feature Selection in High-Dimensional Classification (MK, HL), pp. 329–337.

ICML-c1-2013-PareekR

Human Boosting (HHP, PDR), pp. 338–346.

ICML-c1-2013-AvronBTZ #analysis #canonical #correlation #performance #reduction

Efficient Dimensionality Reduction for Canonical Correlation Analysis (HA, CB, ST, AZ), pp. 347–355.

ICML-c1-2013-WulsinFL #correlation #markov #parsing #process #using

Parsing epileptic events using a Markov switching process model for correlated time series (DW, EBF, BL), pp. 356–364.

ICML-c1-2013-RamdasS #optimisation #probability

Optimal rates for stochastic convex optimization under Tsybakov noise condition (AR, AS), pp. 365–373.

ICML-c1-2013-AfkanpourGSB #algorithm #kernel #learning #multi #random #scalability

A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning (AA, AG, CS, MB), pp. 374–382.

ICML-c1-2013-ChenC13a

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery (YC, CC), pp. 383–391.

ICML-c1-2013-Suzuki #multi #online

Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method (TS), pp. 392–400.

ICML-c1-2013-Shin #design #future of #kernel

A New Frontier of Kernel Design for Structured Data (KS), pp. 401–409.

ICML-c1-2013-MaatenCTW #learning

Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.

ICML-c1-2013-KrauseFGI #approximate

Approximation properties of DBNs with binary hidden units and real-valued visible units (OK, AF, TG, CI), pp. 419–426.

ICML-c1-2013-Jaggi #optimisation

Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization (MJ), pp. 427–435.

ICML-c1-2013-ChenLYY #functional #matrix #using

General Functional Matrix Factorization Using Gradient Boosting (TC, HL, QY, YY), pp. 436–444.

ICML-c1-2013-KarbasiSS #learning

Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.

ICML-c1-2013-GilboaSCG #approximate #multi #process #scalability #using

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations (EG, YS, JPC, EG), pp. 454–461.

ICML-c1-2013-ZuluagaSKP #learning #multi #optimisation

Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.

ICML-c1-2013-KadriGP #approach #kernel #learning

A Generalized Kernel Approach to Structured Output Learning (HK, MG, PP), pp. 471–479.

ICML-c1-2013-GonenSS #approach #learning #performance

Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.

ICML-c1-2013-OstingBO #ranking #statistics

Enhanced statistical rankings via targeted data collection (BO, CB, SO), pp. 489–497.

ICML-c1-2013-0005LSL #feature model #learning #modelling #online

Online Feature Selection for Model-based Reinforcement Learning (TTN, ZL, TS, TYL), pp. 498–506.

ICML-c1-2013-RuvoloE #algorithm #learning #named #performance

ELLA: An Efficient Lifelong Learning Algorithm (PR, EE), pp. 507–515.

ICML-c1-2013-NarasimhanA #approach #optimisation

A Structural SVM Based Approach for Optimizing Partial AUC (HN, SA), pp. 516–524.

ICML-c1-2013-KumarB #bound #graph #learning

Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs (KSSK, FRB), pp. 525–533.

ICML-c1-2013-HoJV #adaptation #classification #crowdsourcing

Adaptive Task Assignment for Crowdsourced Classification (CJH, SJ, JWV), pp. 534–542.

ICML-c1-2013-MaillardNOR #bound #learning #representation

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning (OAM, PN, RO, DR), pp. 543–551.

ICML-c1-2013-BengioMDR

Better Mixing via Deep Representations (YB, GM, YD, SR), pp. 552–560.

ICML-c1-2013-ZhaiB #infinity #online

Online Latent Dirichlet Allocation with Infinite Vocabulary (KZ, JLBG), pp. 561–569.

ICML-c1-2013-YuCSS #theorem

Characterizing the Representer Theorem (YY, HC, DS, CS), pp. 570–578.

ICML-c1-2013-HallW #modelling #online #programming

Dynamical Models and tracking regret in online convex programming (ECH, RW), pp. 579–587.

ICML-c1-2013-AbernethyAKD #learning #problem #scalability

Large-Scale Bandit Problems and KWIK Learning (JA, KA, MK, MD), pp. 588–596.

ICML-c1-2013-LivniLSNSG #analysis #component

Vanishing Component Analysis (RL, DL, SS, HN, SSS, AG), pp. 597–605.

ICML-c1-2013-GolubCY #learning

Learning an Internal Dynamics Model from Control Demonstration (MG, SC, BY), pp. 606–614.

ICML-c1-2013-LimLM #learning #metric #robust

Robust Structural Metric Learning (DL, GRGL, BM), pp. 615–623.

ICML-c1-2013-BuhlerRSH #clustering #community #detection #set #source code

Constrained fractional set programs and their application in local clustering and community detection (TB, SSR, SS, MH), pp. 624–632.

ICML-c1-2013-BalcanBEL #learning #performance

Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.

ICML-c1-2013-TorkamaniL #classification

Convex Adversarial Collective Classification (MT, DL), pp. 642–650.

ICML-c1-2013-ChevaleyreKZ #classification #linear

Rounding Methods for Discrete Linear Classification (YC, FK, JDZ), pp. 651–659.