index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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6,200 | Variational Information Maximization for Feature Selection Shuyang Gao Greg Ver Steeg Aram Galstyan University of Southern California, Information Sciences Institute gaos@usc.edu, gregv@isi.edu, galstyan@isi.edu Abstract Feature selection is one of the most fundamental problems in machine learning. An... | 2016 | 286 |
6,201 | Learning Bound for Parameter Transfer Learning Wataru Kumagai Faculty of Engineering Kanagawa University kumagai@kanagawa-u.ac.jp Abstract We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied... | 2016 | 287 |
6,202 | Matrix Completion has No Spurious Local Minimum Rong Ge Duke University 308 Research Drive, NC 27708 rongge@cs.duke.edu. Jason D. Lee University of Southern California 3670 Trousdale Pkwy, CA 90089 jasonlee@marshall.usc.edu. Tengyu Ma Princeton University 35 Olden Street, NJ 08540 tengyu@cs.prin... | 2016 | 288 |
6,203 | Deep Submodular Functions: Definitions & Learning Brian Dolhansky‡ <bdol@cs.washington.edu> Jeff Bilmes†‡ <bilmes@uw.edu> Dept. of Computer Science and Engineering‡ University of Washington Seattle, WA 98105 Dept. of Electrical Engineering† University of Washington Seattle, WA 98105 Abstract We propo... | 2016 | 289 |
6,204 | Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction Jacob Steinhardt Stanford University Gregory Valiant Stanford University Moses Charikar Stanford University Abstract We consider a crowdsourcing model in which n workers are asked to rate the quality of n items previou... | 2016 | 29 |
6,205 | Adaptive optimal training of animal behavior Ji Hyun Bak1,4 Jung Yoon Choi2,3 Athena Akrami3,5 Ilana Witten2,3 Jonathan W. Pillow2,3 1Department of Physics, 2Department of Psychology, Princeton University 3Princeton Neuroscience Institute, Princeton University 4School of Computational Sciences, Korea Institute ... | 2016 | 290 |
6,206 | Structured Matrix Recovery via the Generalized Dantzig Selector Sheng Chen Arindam Banerjee Dept. of Computer Science & Engineering University of Minnesota, Twin Cities {shengc,banerjee}@cs.umn.edu Abstract In recent years, structured matrix recovery problems have gained considerable attention for its... | 2016 | 291 |
6,207 | Robust k-means: a Theoretical Revisit Alexandros Georgogiannis School of Electrical and Computer Engineering Technical University of Crete, Greece alexandrosgeorgogiannis at gmail.com Abstract Over the last years, many variations of the quadratic k-means clustering procedure have been proposed, all aiming... | 2016 | 292 |
6,208 | Tree-Structured Reinforcement Learning for Sequential Object Localization Zequn Jie1, Xiaodan Liang2, Jiashi Feng1, Xiaojie Jin1, Wen Feng Lu1, Shuicheng Yan1 1 National University of Singapore, Singapore 2 Carnegie Mellon University, USA Abstract Existing object proposal algorithms usually search for possi... | 2016 | 293 |
6,209 | One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities Michalis K. Titsias Department of Informatics Athens University of Economics and Business mtitsias@aueb.gr Abstract The softmax representation of probabilities for categorical variables plays a prominent role in modern machine l... | 2016 | 294 |
6,210 | Poisson–Gamma Dynamical Systems Aaron Schein College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 aschein@cs.umass.edu Mingyuan Zhou McCombs School of Business The University of Texas at Austin Austin, TX 78712 mingyuan.zhou@mccombs.utexas.edu Hanna Wa... | 2016 | 295 |
6,211 | Convergence guarantees for kernel-based quadrature rules in misspecified settings Motonobu Kanagawa∗, Bharath K Sriperumbudur†, Kenji Fukumizu∗ ∗The Institute of Statistical Mathematics, Tokyo 190-8562, Japan †Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA kanagawa@... | 2016 | 296 |
6,212 | Maximization of Approximately Submodular Functions Thibaut Horel Harvard University thorel@seas.harvard.edu Yaron Singer Harvard University yaron@seas.harvard.edu Abstract We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submo... | 2016 | 297 |
6,213 | Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou Department of EECS UC Berekely yxzhou@berkeley.edu Costas J. Spanos Department of EECS UC Berkeley spanos@berkeley.edu Abstract We study causal subset selection with Directed Information as the measure of prediction c... | 2016 | 298 |
6,214 | Linear Feature Encoding for Reinforcement Learning Zhao Song, Ronald Parr†, Xuejun Liao, Lawrence Carin Department of Electrical and Computer Engineering † Department of Computer Science Duke University, Durham, NC 27708, USA Abstract Feature construction is of vital importance in reinforcement learning, as... | 2016 | 299 |
6,215 | Tagger: Deep Unsupervised Perceptual Grouping Klaus Greff*, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber*, Harri Valpola The Curious AI Company {antti,mathias,hotloo,harri}@cai.fi *IDSIA {klaus,juergen}@idsia.ch Abstract We present a framework for efficient perceptual inference that ex... | 2016 | 3 |
6,216 | Threshold Bandit, With and Without Censored Feedback Jacob Abernethy Department of Computer Science University of Michigan Ann Arbor, MI 48109 jabernet@umich.edu Kareem Amin Department of Computer Science University of Michigan Ann Arbor, MI 48109 amkareem@umich.edu Ruihao Zhu AeroAstro&CSAIL ... | 2016 | 30 |
6,217 | Mixed Linear Regression with Multiple Components Kai Zhong 1 Prateek Jain 2 Inderjit S. Dhillon 3 1,3 University of Texas at Austin 2 Microsoft Research India 1 zhongkai@ices.utexas.edu, 2 prajain@microsoft.com 3 inderjit@cs.utexas.edu Abstract In this paper, we study the mixed linear regression (ML... | 2016 | 300 |
6,218 | Unsupervised Learning of Spoken Language with Visual Context David Harwath, Antonio Torralba, and James R. Glass Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02115 {dharwath, torralba, jrg}@csail.mit.edu Abstract Humans learn to speak before ... | 2016 | 301 |
6,219 | Crowdsourced Clustering: Querying Edges vs Triangles Ramya Korlakai Vinayak Department of Electrical Engineering Caltech, Pasadena ramya@caltech.edu Babak Hassibi Department of Electrical Engineering Caltech, Pasadena hassibi@systems.caltech.edu Abstract We consider the task of clustering items us... | 2016 | 302 |
6,220 | Learning feed-forward one-shot learners Luca Bertinetto∗ University of Oxford luca@robots.ox.ac.uk João F. Henriques∗ University of Oxford joao@robots.ox.ac.uk Jack Valmadre∗ University of Oxford jvlmdr@robots.ox.ac.uk Philip H. S. Torr University of Oxford philip.torr@eng.ox.ac.uk Andrea Veda... | 2016 | 303 |
6,221 | Reshaped Wirtinger Flow for Solving Quadratic System of Equations Huishuai Zhang Department of EECS Syracuse University Syracuse, NY 13244 hzhan23@syr.edu Yingbin Liang Department of EECS Syracuse University Syracuse, NY 13244 yliang06@syr.edu Abstract We study the problem of recovering a vect... | 2016 | 304 |
6,222 | Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li ∗ Vanderbilt University bo.li.2@vanderbilt.edu Yining Wang ∗ Carnegie Mellon University ynwang.yining@gmail.com Aarti Singh Carnegie Mellon University aarti@cs.cmu.edu Yevgeniy Vorobeychik Vanderbilt University yevgeni... | 2016 | 305 |
6,223 | PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain† Francis Bach† Alexandre Lacoste‡ Simon Lacoste-Julien† † INRIA Paris - École Normale Supérieure, firstname.lastname@inria.fr ‡ Google, allac@google.com Abstract We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the Ba... | 2016 | 306 |
6,224 | Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li MIT ctli@mit.edu Stefanie Jegelka MIT stefje@csail.mit.edu Suvrit Sra MIT suvrit@mit.edu Abstract We study probability measures induced by set functions with constraints. Such measures arise in... | 2016 | 307 |
6,225 | Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction Hsiang-Fu Yu University of Texas at Austin rofuyu@cs.utexas.edu Nikhil Rao Technicolor Research nikhilrao86@gmail.com Inderjit S. Dhillon University of Texas at Austin inderjit@cs.utexas.edu Abstract Time serie... | 2016 | 308 |
6,226 | FPNN: Field Probing Neural Networks for 3D Data Yangyan Li1,2 Sören Pirk1 Hao Su1 Charles R. Qi1 Leonidas J. Guibas1 1Stanford University, USA 2Shandong University, China Abstract Building discriminative representations for 3D data has been an important task in computer graphics and computer vision ... | 2016 | 309 |
6,227 | Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon Department of Computer Science Stanford University {adityag,ermon}@cs.stanford.edu Abstract Variational approaches are often used to approximate intractable posteriors or normalization constants in hierarchical latent variable models. Wh... | 2016 | 31 |
6,228 | Object based Scene Representations using Fisher Scores of Local Subspace Projections Mandar Dixit and Nuno Vasconcelos Department of Electrical and Computer Engineering University of California, San Diego {mdixit, nvasconcelos}@ucsd.edu Abstract Several works have shown that deep CNNs can be easily transf... | 2016 | 310 |
6,229 | Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang1,∗, Yuhuai Wu2,∗, Tong Che4, Zhouhan Lin1, Roland Memisevic1,5, Ruslan Salakhutdinov3,5 and Yoshua Bengio1,5 1MILA, Université de Montréal, 2University of Toronto, 3Carnegie Mellon University, 4Institut des Hautes Études Scientifique... | 2016 | 311 |
6,230 | Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray1, Sidhant Misra2, Andrey Y. Lokhov1,3, and Michael Chertkov1,3,4 1Theoretical Division T-4, Los Alamos National Laboratory, Los Alamos, NM 87545, USA 2Theoretical Division T-5, Los Alamos National Laboratory, Los Alamos, NM... | 2016 | 312 |
6,231 | Community Detection on Evolving Graphs Aris Anagnostopoulos Sapienza University of Rome aris@dis.uniroma1.it Jakub Ł ˛acki Sapienza University of Rome j.lacki@mimuw.edu.pl Silvio Lattanzi Google silviol@google.com Stefano Leonardi Sapienza University of Rome leonardi@dis.uniroma1.it Mohammad M... | 2016 | 313 |
6,232 | Preference Completion from Partial Rankings Suriya Gunasekar University of Texas, Austin, TX, USA suriya@utexas.edu Oluwasanmi Koyejo University of Illinois, Urbana-Champaign, IL, USA sanmi@illinois.edu Joydeep Ghosh University of Texas,Austin, TX, USA ghosh@ece.utexas.edu Abstract We propose a no... | 2016 | 314 |
6,233 | “Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation Wen-Hao Zhang1,2 ∗, He Wang1, K. Y. Michael Wong1, Si Wu2 wenhaoz@ust.hk, hwangaa@connect.ust.hk, phkywong@ust.hk, wusi@bnu.edu.cn 1Department of Physics, Hong Kong University of Science and Technology, Hong Kong. 2State ... | 2016 | 315 |
6,234 | A Consistent Regularization Approach for Structured Prediction Carlo Ciliberto ∗,1 cciliber@mit.edu Alessandro Rudi ∗,1,2 ale_rudi@mit.edu Lorenzo Rosasco 1,2 lrosasco@mit.edu 1 Laboratory for Computational and Statistical Learning - Istituto Italiano di Tecnologia, Genova, Italy & Massachusetts Insti... | 2016 | 316 |
6,235 | Fast recovery from a union of subspaces Chinmay Hegde Iowa State University Piotr Indyk MIT Ludwig Schmidt MIT Abstract We address the problem of recovering a high-dimensional but structured vector from linear observations in a general setting where the vector can come from an arbitrary union of sub... | 2016 | 317 |
6,236 | Improved Techniques for Training GANs Tim Salimans tim@openai.com Ian Goodfellow ian@openai.com Wojciech Zaremba woj@openai.com Vicki Cheung vicki@openai.com Alec Radford alec@openai.com Xi Chen peter@openai.com Abstract We present a variety of new architectural features and training procedu... | 2016 | 318 |
6,237 | Coordinate-wise Power Method Qi Lei 1 Kai Zhong 1 Inderjit S. Dhillon 1,2 1 Institute for Computational Engineering & Sciences 2 Department of Computer Science University of Texas at Austin {leiqi, zhongkai}@ices.utexas.edu, inderjit@cs.utexas.edu Abstract In this paper, we propose a coordinate-wise v... | 2016 | 319 |
6,238 | Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models Juho Lee POSTECH, Korea stonecold@postech.ac.kr Lancelot F. James HKUST, Hong Kong lancelot@ust.hk Seungjin Choi POSTECH, Korea seungjin@postech.ac.kr Abstract Bayesian nonparametric methods based on the Diric... | 2016 | 32 |
6,239 | On Mixtures of Markov Chains Rishi Gupta∗ Stanford University Stanford, CA 94305 rishig@cs.stanford.edu Ravi Kumar Google Research Mountain View, CA 94043 ravi.k53@gmail.com Sergei Vassilvitskii Google Research New York, NY 10011 sergeiv@google.com Abstract We study the problem of reconstruc... | 2016 | 320 |
6,240 | Near-Optimal Smoothing of Structured Conditional Probability Matrices Moein Falahatgar University of California, San Diego San Diego, CA, USA moein@ucsd.edu Mesrob I. Ohannessian Toyota Technological Institute at Chicago Chicago, IL, USA mesrob@ttic.edu Alon Orlitsky University of California, San ... | 2016 | 321 |
6,241 | Dynamic Filter Networks Bert De Brabandere1∗ ESAT-PSI, KU Leuven, iMinds Xu Jia1∗ ESAT-PSI, KU Leuven, iMinds Tinne Tuytelaars1 ESAT-PSI, KU Leuven, iMinds Luc Van Gool1,2 ESAT-PSI, KU Leuven, iMinds D-ITET, ETH Zurich 1firstname.lastname@esat.kuleuven.be 2vangool@vision.ee.ethz.ch Abstract In... | 2016 | 322 |
6,242 | Estimating the Size of a Large Network and its Communities from a Random Sample Lin Chen1,2, Amin Karbasi1,2, Forrest W. Crawford2,3 1Department of Electrical Engineering, 2Yale Institute for Network Science, 3Department of Biostatistics, Yale University {lin.chen, amin.karbasi, forrest.crawford}@yale.edu A... | 2016 | 323 |
6,243 | Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back∗ Vitaly Feldman IBM Research – Almaden Abstract In stochastic convex optimization the goal is to minimize a convex function F(x) .= Ef∼D[f(x)] over a convex set K ⊂Rd where D is some unknown distribution and each f(·) in... | 2016 | 324 |
6,244 | Nested Mini-Batch K-Means James Newling Idiap Research Institue & EPFL james.newling@idiap.ch Franc¸ois Fleuret Idiap Research Institue & EPFL francois.fleuret@idiap.ch Abstract A new algorithm is proposed which accelerates the mini-batch k-means algorithm of Sculley (2010) by using the distance bound... | 2016 | 325 |
6,245 | Infinite Hidden Semi-Markov Modulated Interaction Point Process Peng Lin§†, Bang Zhang§, Ting Guo§, Yang Wang§, Fang Chen§ §Data61 CSIRO, Australian Technology Park, 13 Garden Street, Eveleigh NSW 2015, Australia †School of Computer Science and Engineering, The University of New South Wales, Australia {peng.li... | 2016 | 326 |
6,246 | A Credit Assignment Compiler for Joint Prediction Kai-Wei Chang University of Virginia kw@kwchang.net He He University of Maryland hhe@cs.umd.edu Hal Daumé III University of Maryland me@hal3.name John Langford Microsoft Research jcl@microsoft.com Stephane Ross Google stephaneross@google.co... | 2016 | 327 |
6,247 | Deep Exploration via Bootstrapped DQN Ian Osband1,2, Charles Blundell2, Alexander Pritzel2, Benjamin Van Roy1 1Stanford University, 2Google DeepMind {iosband, cblundell, apritzel}@google.com, bvr@stanford.edu Abstract Efficient exploration remains a major challenge for reinforcement learning (RL). Common dith... | 2016 | 328 |
6,248 | Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods Cristina Savin IST Austria Klosterneuburg, AT 3400 csavin@ist.ac.at Gasper Tkaˇcik IST Austria Klosterneuburg, AT 3400 tkacik@ist.ac.at Abstract Jointly characterizing neural responses in terms of several external... | 2016 | 329 |
6,249 | Maximal Sparsity with Deep Networks? Bo Xin1,2 Yizhou Wang1 Wen Gao1 Baoyuan Wang3 David Wipf2 1Peking University 2Microsoft Research, Beijing 3Microsoft Research, Redmond {boxin, baoyuanw, davidwip}@microsoft.com {yizhou.wang, wgao}@pku.edu.cn Abstract The iterations of many sparse estimation a... | 2016 | 33 |
6,250 | Structured Sparse Regression via Greedy Hard-thresholding Prateek Jain Microsoft Research India Nikhil Rao Technicolor Inderjit Dhillon UT Austin Abstract Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overl... | 2016 | 330 |
6,251 | A Multi-Batch L-BFGS Method for Machine Learning Albert S. Berahas Northwestern University Evanston, IL albertberahas@u.northwestern.edu Jorge Nocedal Northwestern University Evanston, IL j-nocedal@northwestern.edu Martin Takáˇc Lehigh University Bethlehem, PA takac.mt@gmail.com Abstract T... | 2016 | 331 |
6,252 | Cooperative Graphical Models Josip Djolonga Dept. of Computer Science, ETH Z¨urich josipd@inf.ethz.ch Stefanie Jegelka CSAIL, MIT stefje@mit.edu Sebastian Tschiatschek Dept. of Computer Science, ETH Z¨urich stschia@inf.ethz.ch Andreas Krause Dept. of Computer Science, ETH Z¨urich krausea@inf.eth... | 2016 | 332 |
6,253 | What Makes Objects Similar: A Unified Multi-Metric Learning Approach Han-Jia Ye De-Chuan Zhan Xue-Min Si Yuan Jiang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China {yehj,zhandc,sixm,jiangy,zhouzh}@lamda.nju.edu.cn Abstract Linkages are ... | 2016 | 333 |
6,254 | Matching Networks for One Shot Learning Oriol Vinyals Google DeepMind vinyals@google.com Charles Blundell Google DeepMind cblundell@google.com Timothy Lillicrap Google DeepMind countzero@google.com Koray Kavukcuoglu Google DeepMind korayk@google.com Daan Wierstra Google DeepMind wierstra@g... | 2016 | 334 |
6,255 | Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares Rong Zhu Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. rongzhu@amss.ac.cn Abstract In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computat... | 2016 | 335 |
6,256 | Accelerating Stochastic Composition Optimization Mengdi Wang⇤, Ji Liu⇤, and Ethan X. Fang Princeton University, University of Rochester, Pennsylvania State University mengdiw@princeton.edu, ji.liu.uwisc@gmail.com, xxf13@psu.edu Abstract Consider the stochastic composition optimization problem where the object... | 2016 | 336 |
6,257 | Variational Inference in Mixed Probabilistic Submodular Models Josip Djolonga Sebastian Tschiatschek Andreas Krause Department of Computer Science, ETH Z¨urich {josipd,tschiats,krausea}@inf.ethz.ch Abstract We consider the problem of variational inference in probabilistic models with both log-submodul... | 2016 | 337 |
6,258 | The Limits of Learning with Missing Data Brian Bullins Elad Hazan Princeton University Princeton, NJ {bbullins,ehazan}@cs.princeton.edu Tomer Koren Google Brain Mountain View, CA tkoren@google.com Abstract We study linear regression and classification in a setting where the learning algorithm is al... | 2016 | 338 |
6,259 | Clustering with Same-Cluster Queries Hassan Ashtiani , Shrinu Kushagra and Shai Ben-David David R. Cheriton School of Computer Science University of Waterloo, Waterloo, Ontario, Canada {mhzokaei,skushagr,shai}@uwaterloo.ca Abstract We propose a framework for Semi-Supervised Active Clustering framework (... | 2016 | 339 |
6,260 | Single-Image Depth Perception in the Wild Weifeng Chen Zhao Fu Dawei Yang Jia Deng University of Michigan, Ann Arbor {wfchen,zhaofu,ydawei,jiadeng}@umich.edu Abstract This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained setting... | 2016 | 34 |
6,261 | Deconvolving Feedback Loops in Recommender Systems Ayan Sinha Purdue University sinhayan@mit.edu David F. Gleich Purdue University dgleich@purdue.edu Karthik Ramani Purdue University ramani@purdue.edu Abstract Collaborative filtering is a popular technique to infer users’ preferences on new con... | 2016 | 340 |
6,262 | Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates Yuanzhi Li, Yingyu Liang, Andrej Risteski Computer Science Department at Princeton University 35 Olden St, Princeton, NJ 08540 {yuanzhil, yingyul, risteski}@cs.princeton.edu Abstract Non-negative matrix factorization is a popu... | 2016 | 341 |
6,263 | Threshold Learning for Optimal Decision Making Nathan F. Lepora Department of Engineering Mathematics, University of Bristol, UK n.lepora@bristol.ac.uk Abstract Decision making under uncertainty is commonly modelled as a process of competitive stochastic evidence accumulation to threshold (the drift-diffusion... | 2016 | 342 |
6,264 | Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Alexander Kirillov1 Alexander Shekhovtsov2 Carsten Rother1 Bogdan Savchynskyy1 1 TU Dresden, Dresden, Germany 2 TU Graz, Graz, Austria alexander.kirillov@tu-dresden.de Abstract We consider the problem of jointly inferring the M-b... | 2016 | 343 |
6,265 | Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making Himabindu Lakkaraju Department of Computer Science Stanford University himalv@cs.stanford.edu Jure Leskovec Department of Computer Science Stanford University jure@cs.stanford.edu Abstract We propose Co... | 2016 | 344 |
6,266 | Measuring Neural Net Robustness with Constraints Osbert Bastani Stanford University obastani@cs.stanford.edu Yani Ioannou University of Cambridge yai20@cam.ac.uk Leonidas Lampropoulos University of Pennsylvania llamp@seas.upenn.edu Dimitrios Vytiniotis Microsoft Research dimitris@microsoft.com ... | 2016 | 345 |
6,267 | The Power of Adaptivity in Identifying Statistical Alternatives Kevin Jamieson, Daniel Haas, Ben Recht University of California, Berkeley Berkeley, CA 94720 {kjamieson,dhaas,brecht}@eecs.berkeley.edu Abstract This paper studies the trade-off between two different kinds of pure exploration: breadth versu... | 2016 | 346 |
6,268 | Adaptive Skills Adaptive Partitions (ASAP) Daniel J. Mankowitz, Timothy A. Mann∗and Shie Mannor The Technion - Israel Institute of Technology, Haifa, Israel danielm@tx.technion.ac.il, mann.timothy@acm.org, shie@ee.technion.ac.il ∗Timothy Mann now works at Google Deepmind. Abstract We introduce the Adaptiv... | 2016 | 347 |
6,269 | Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang MIT Sashank J. Reddi Carnegie Mellon University Suvrit Sra MIT Abstract We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing... | 2016 | 348 |
6,270 | Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer’s Disease Hao Henry Zhou† Sathya N. Ravi† Vamsi K. Ithapu† Sterling C. Johnson§,† Grace Wahba† Vikas Singh† §William S. Middleton Memorial VA Hospital †University of Wisconsin–Madison Abstract Consider samples from... | 2016 | 349 |
6,271 | Single Pass PCA of Matrix Products Shanshan Wu The University of Texas at Austin shanshan@utexas.edu Srinadh Bhojanapalli Toyota Technological Institute at Chicago srinadh@ttic.edu Sujay Sanghavi The University of Texas at Austin sanghavi@mail.utexas.edu Alexandros G. Dimakis The University of Tex... | 2016 | 35 |
6,272 | Review Networks for Caption Generation Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen School of Computer Science Carnegie Mellon University {zhiliny,yey1,yuexinw,rsalakhu,wcohen}@cs.cmu.edu Abstract We propose a novel extension of the encoder-decoder framework, called a review net... | 2016 | 350 |
6,273 | Distributed Flexible Nonlinear Tensor Factorization Shandian Zhe§, Kai Zhang†, Pengyuan Wang‡, Kuang-chih Lee♯, Zenglin Xu♮, Yuan Qi♭, Zoubin Gharamani⋆ §Dept. Computer Science, Purdue University, †NEC Laboratories America, Princeton NJ, ‡Dept. Marketing, University of Georgia at Athens, ♯Yahoo! Research, ♮Bi... | 2016 | 351 |
6,274 | Safe Policy Improvement by Minimizing Robust Baseline Regret Marek Petrik University of New Hampshire mpetrik@cs.unh.edu Mohammad Ghavamzadeh Adobe Research & INRIA Lille ghavamza@adobe.com Yinlam Chow Stanford University ychow@stanford.edu Abstract An important problem in sequential decision-ma... | 2016 | 352 |
6,275 | Safe Exploration in Finite Markov Decision Processes with Gaussian Processes Matteo Turchetta ETH Zurich matteotu@ethz.ch Felix Berkenkamp ETH Zurich befelix@ethz.ch Andreas Krause ETH Zurich krausea@ethz.ch Abstract In classical reinforcement learning agents accept arbitrary short term loss for... | 2016 | 353 |
6,276 | Multimodal Residual Learning for Visual QA Jin-Hwa Kim Sang-Woo Lee Donghyun Kwak Min-Oh Heo Seoul National University {jhkim,slee,dhkwak,moheo}@bi.snu.ac.kr Jeonghee Kim Jung-Woo Ha Naver Labs, Naver Corp. {jeonghee.kim,jungwoo.ha}@navercorp.com Byoung-Tak Zhang Seoul National University & Surr... | 2016 | 354 |
6,277 | Variance Reduction in Stochastic Gradient Langevin Dynamics Avinava Dubey∗, Sashank J. Reddi∗, Barnab´as P´oczos, Alexander J. Smola, Eric P. Xing Department of Machine Learning Carnegie-Mellon University Pittsburgh, PA 15213 {akdubey, sjakkamr, bapoczos, alex, epxing}@cs.cmu.edu Sinead A. Williamson IR... | 2016 | 355 |
6,278 | On Regularizing Rademacher Observation Losses Richard Nock Data61, The Australian National University & The University of Sydney richard.nock@data61.csiro.au Abstract It has recently been shown that supervised learning linear classifiers with two of the most popular losses, the logistic and square loss, is e... | 2016 | 356 |
6,279 | A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification Steven Cheng-Xian Li Benjamin Marlin College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 {cxl,marlin}@cs.umass.edu Abstract We present a general framework for... | 2016 | 357 |
6,280 | x2 x1 0 0.2 0.4 0.6 0.8 1 0 10 20 30 40 50 F−Score OKM OKM* OKM*+LPE OKM*+NPE 0 0.2 0.4 0.6 0.8 1 0 20 40 60 80 100 F−Score OKM* OKM*+LPE OKM*+NPE | 2016 | 358 |
6,281 | Equality of Opportunity in Supervised Learning Moritz Hardt Google m@mrtz.org Eric Price∗ UT Austin ecprice@cs.utexas.edu Nathan Srebro TTI-Chicago nati@ttic.edu Abstract We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to... | 2016 | 359 |
6,282 | Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail Rensselaer Polytechnic Institute, Troy, NY 12211 magdon@cs.rpi.edu Christos Boutsidis New York, NY christos.boutsidis@gmail.com Abstract Principal components analysis (PCA) is the optimal linear encoder of data. Sparse linear encoders (... | 2016 | 36 |
6,283 | Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes Hassan A. Kingravi Pindrop Atlanta, GA 30308 hkingravi@pindrop.com Harshal Maske and Girish Chowdhary University of Illinois at Urbana Champaign Urbana, IL 61801 hmaske2@illinois.edu, girishc@illinois.edu... | 2016 | 360 |
6,284 | Hierarchical Question-Image Co-Attention for Visual Question Answering Jiasen Lu∗, Jianwei Yang∗, Dhruv Batra∗† , Devi Parikh∗† ∗Virginia Tech, † Georgia Institute of Technology {jiasenlu, jw2yang, dbatra, parikh}@vt.edu Abstract A number of recent works have proposed attention models for Visual Question ... | 2016 | 361 |
6,285 | Double Thompson Sampling for Dueling Bandits Huasen Wu University of California, Davis hswu@ucdavis.edu Xin Liu University of California, Davis xinliu@ucdavis.edu Abstract In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As its name suggests, D-TS sele... | 2016 | 362 |
6,286 | A state-space model of cross-region dynamic connectivity in MEG/EEG Ying Yang∗ Elissa M. Aminoff† Michael J. Tarr∗ Robert E. Kass∗ ∗Carnegie Mellon University, †Fordham University ying.yang.cnbc.cmu@gmail.com, {eaminoff@fordham, michaeltarr@cmu, kass@stat.cmu}.edu Abstract Cross-region dynamic connect... | 2016 | 363 |
6,287 | Using Fast Weights to Attend to the Recent Past Jimmy Ba University of Toronto jimmy@psi.toronto.edu Geoffrey Hinton University of Toronto and Google Brain geoffhinton@google.com Volodymyr Mnih Google DeepMind vmnih@google.com Joel Z. Leibo Google DeepMind jzl@google.com Catalin Ionescu Goog... | 2016 | 364 |
6,288 | High-Rank Matrix Completion and Clustering under Self-Expressive Models E. Elhamifar∗ College of Computer and Information Science Northeastern University Boston, MA 02115 eelhami@ccs.neu.edu Abstract We propose efficient algorithms for simultaneous clustering and completion of incomplete high-dimension... | 2016 | 365 |
6,289 | Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari? University of Pennsylvania aryanm@seas.upenn.edu Hadi Daneshmand? ETH Zurich, Switzerland hadi.daneshmand@inf.ethz.ch Aurelien Lucchi ETH Zurich, Switzerland aurelien.lucchi@inf.ethz.ch Thomas Hofmann ... | 2016 | 366 |
6,290 | Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale Firas Abuzaid1, Joseph Bradley2, Feynman Liang3, Andrew Feng4, Lee Yang4, Matei Zaharia1, Ameet Talwalkar5 1MIT CSAIL, 2Databricks, 3University of Cambridge, 4Yahoo, 5UCLA Abstract Deep distributed decision trees and tree ensembles hav... | 2016 | 367 |
6,291 | Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Nguyen Viet Cuong1 Huan Xu2 1Department of Engineering, University of Cambridge, vcn22@cam.ac.uk 2Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, huan.xu@isye.gatech.edu Abstract We study ... | 2016 | 368 |
6,292 | Guided Policy Search via Approximate Mirror Descent William Montgomery Dept. of Computer Science and Engineering University of Washington wmonty@cs.washington.edu Sergey Levine Dept. of Computer Science and Engineering University of Washington svlevine@cs.washington.edu Abstract Guided policy sear... | 2016 | 369 |
6,293 | Measuring the reliability of MCMC inference with bidirectional Monte Carlo Roger B. Grosse Department of Computer Science University of Toronto Siddharth Ancha Department of Computer Science University of Toronto Daniel M. Roy Department of Statistics University of Toronto Abstract Markov chain ... | 2016 | 37 |
6,294 | On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability Guillaume Papa, Stéphan Clémençon LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay 75013, Paris, France first.last@telecom-paristech.fr Aurélien Bellet INRIA 59650 Villeneuve d’Ascq, France aurelien.bell... | 2016 | 370 |
6,295 | Geometric Dirichlet Means algorithm for topic inference Mikhail Yurochkin Department of Statistics University of Michigan moonfolk@umich.edu XuanLong Nguyen Department of Statistics University of Michigan xuanlong@umich.edu Abstract We propose a geometric algorithm for topic learning and inference... | 2016 | 371 |
6,296 | Learned Region Sparsity and Diversity Also Predict Visual Attention Zijun Wei1∗, Hossein Adeli2∗, Gregory Zelinsky1,2, Minh Hoai1, Dimitris Samaras1 1. Department of Computer Science 2. Department of Psychology – Stony Brook University 1.{zijwei, minhhoai, samaras}@cs.stonybrook.edu 2.{hossein.adelije... | 2016 | 372 |
6,297 | Deep Learning Models of the Retinal Response to Natural Scenes Lane T. McIntosh∗1, Niru Maheswaranathan∗1, Aran Nayebi1, Surya Ganguli2,3, Stephen A. Baccus3 1Neurosciences PhD Program, 2Department of Applied Physics, 3Neurobiology Department Stanford University {lmcintosh, nirum, anayebi, sganguli, baccus}... | 2016 | 373 |
6,298 | Batched Gaussian Process Bandit Optimization via Determinantal Point Processes Tarun Kathuria, Amit Deshpande, Pushmeet Kohli Microsoft Research t-takat@microsoft.com, amitdesh@microsoft.com, pkohli@microsoft.com Abstract Gaussian Process bandit optimization has emerged as a powerful tool for optimizing n... | 2016 | 374 |
6,299 | Inference by Reparameterization in Neural Population Codes Rajkumar V. Raju Department of ECE Rice University Houston, TX 77005 rv12@rice.edu Xaq Pitkow Dept. of Neuroscience, Dept. of ECE Baylor College of Medicine, Rice University Houston, TX 77005 xaq@rice.edu Abstract Behavioral experiment... | 2016 | 375 |
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