Kilian Q. Weinberger

Stephen Tyree, Kilian Q. Weinberger, Tammie L. Benzinger, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Hierarchical Distributed Representations for Statistical Language Modeling

John Blitzer, Fernando Pereira, Kilian Q. Weinberger, 2004, NIPS.

Supervised Word Mover's Distance

Matt J. Kusner, Kilian Q. Weinberger, Yu Sun, 2016, NIPS.

Co-Training for Domain Adaptation

John Blitzer, Kilian Q. Weinberger, Minmin Chen, 2011, NIPS.

ISMIR 2008 – Session 3a – Content-Based Retrieval, Categorization and Similarity 1 LEARNING A METRIC FOR MUSIC SIMILARITY

Kilian Q. Weinberger, Malcolm Slaney, William White, 2022 .

Densely Connected Convolutional Networks

Kilian Q. Weinberger, Gao Huang, Zhuang Liu, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Graph Laplacian Regularization for Large-Scale Semidefinite Programming

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2006, NIPS.

Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization

Kilian Q. Weinberger, Lawrence K. Saul, Benjamin Packer, 2005, AISTATS.

Fast Image Tagging

Kilian Q. Weinberger, Minmin Chen, Alice X. Zheng, 2013, ICML.

Marginalizing Corrupted Features pdf

Stephen Tyree, Kilian Q. Weinberger, Laurens van der Maaten, 2014, ArXiv.

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Andrew Gordon Wilson, Kilian Q. Weinberger, David Bindel, 2018, NeurIPS.

Differentially Private Bayesian Optimization

Roman Garnett, Matt J. Kusner, Kilian Q. Weinberger, 2015, ICML.

TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks pdf

Kilian Q. Weinberger, Chuan Guo, Ruihan Wu, 2020, ArXiv.

Compressing Convolutional Neural Networks pdf

Yixin Chen, Stephen Tyree, Wenlin Chen, 2015, ArXiv.

Learning a kernel matrix for nonlinear dimensionality reduction

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2004, ICML.

The Greedy Miser: Learning under Test-time Budgets

Kilian Q. Weinberger, Olivier Chapelle, Zhixiang Eddie Xu, 2012, ICML.

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Kilian Q. Weinberger, Laurens van der Maaten, Gao Huang, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Fast flux discriminant for large-scale sparse nonlinear classification

Yixin Chen, Wenlin Chen, Kilian Q. Weinberger, 2014, KDD.

Stochastic triplet embedding

Kilian Q. Weinberger, Laurens van der Maaten, L. Maaten, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Compressing Convolutional Neural Networks in the Frequency Domain

Yixin Chen, Stephen Tyree, Wenlin Chen, 2016, KDD.

Detecting Noisy Training Data with Loss Curves

Kilian Q. Weinberger, Tianyi Zhang, Ethan R. Elenberg, 2019 .

Low Frequency Adversarial Perturbation

Kilian Q. Weinberger, Chuan Guo, Jared S. Frank, 2018, UAI.

Large Margin Multi-Task Metric Learning

Kilian Q. Weinberger, Shibin Parameswaran, S. Parameswaran, 2010, NIPS.

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

Yixin Chen, Wenlin Chen, Kilian Q. Weinberger, 2014, AAAI.

On Feature Normalization and Data Augmentation pdf

Kilian Q. Weinberger, Ser-Nam Lim, Serge J. Belongie, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Resolving tag ambiguity

Kilian Q. Weinberger, Malcolm Slaney, Roelof van Zwol, 2008, ACM Multimedia.

Classifier Cascade for Minimizing Feature Evaluation Cost

Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle, 2012, AISTATS.

Discovering and Exploiting Additive Structure for Bayesian Optimization

Roman Garnett, Kilian Q. Weinberger, Roger B. Grosse, 2017, AISTATS.

Collaborative Email-Spam Filtering with the Hashing-Trick

Alexander J. Smola, Kilian Q. Weinberger, Martin Zinkevich, 2009 .

Revisiting Meta-Learning as Supervised Learning pdf

Kilian Q. Weinberger, De-Chuan Zhan, Han-Jia Ye, 2020, ArXiv.

Gradient Regularized Budgeted Boosting pdf

Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng, 2019, ArXiv.

Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation.

Yan Wang, Kilian Q. Weinberger, Ser-Nam Lim, 2020 .

Stochastic Covariance Compression pdf

Stephen Tyree, Matt J. Kusner, Kilian Q. Weinberger, 2014, ArXiv.

Unsupervised Learning of Image Manifolds by Semidefinite Programming

Kilian Q. Weinberger, Lawrence K. Saul, L. Saul, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Constant-Time Predictive Distributions for Gaussian Processes

Andrew Gordon Wilson, Kilian Q. Weinberger, Jacob R. Gardner, 2018, ICML.

Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data

Kilian Q. Weinberger, Lawrence K. Saul, Pincelli M. Hull, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Marginalized Denoising for Link Prediction and Multi-Label Learning

Weixiong Zhang, Kilian Q. Weinberger, Zheng Chen, 2015, AAAI.

Boosted multi-task learning

Ya Zhang, Kilian Q. Weinberger, Belle L. Tseng, 2010, Machine Learning.

Exact Gaussian Processes on a Million Data Points

Stephen Tyree, Andrew Gordon Wilson, Kilian Q. Weinberger, 2019, NeurIPS.

Fast Reading Comprehension with ConvNets pdf

John Blitzer, Kilian Q. Weinberger, Ni Lao, 2017, ArXiv.

Unsupervised image ranking

Marc'Aurelio Ranzato, Kilian Q. Weinberger, Malcolm Slaney, 2009, LS-MMRM '09.

Web-Search Ranking with Initialized Gradient Boosted Regression Trees

Kilian Q. Weinberger, Zheng Chen, Ananth Mohan, 2010, Yahoo! Learning to Rank Challenge.

Learning with Marginalized Corrupted Features

Stephen Tyree, Kilian Q. Weinberger, Laurens van der Maaten, 2013, ICML.

Spam or ham?: characterizing and detecting fraudulent "not spam" reports in web mail systems

Nick Feamster, Anirban Dasgupta, Kilian Q. Weinberger, 2011, CEAS '11.

On Fairness and Calibration

Jon M. Kleinberg, Kilian Q. Weinberger, Felix Wu, 2017, NIPS.

Metric Learning for Kernel Regression

Kilian Q. Weinberger, Gerald Tesauro, G. Tesauro, 2007, AISTATS.

Snapshot Ensembles: Train 1, get M for free pdf

Kilian Q. Weinberger, John E. Hopcroft, Yixuan Li, 2017, ICLR.

An alternative text representation to TF-IDF and Bag-of-Words pdf

Kilian Q. Weinberger, Minmin Chen, Fei Sha, 2013, ArXiv.

A New Defense Against Adversarial Images: Turning a Weakness into a Strength

Tao Yu, Kilian Q. Weinberger, Chuan Guo, 2019, NeurIPS.

Fast solvers and efficient implementations for distance metric learning

Kilian Q. Weinberger, Lawrence K. Saul, L. Saul, 2008, ICML '08.

Multi-Scale Dense Convolutional Networks for Efficient Prediction pdf

Kilian Q. Weinberger, Felix Wu, Laurens van der Maaten, 2017, ArXiv.

Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Yan Wang, Kilian Q. Weinberger, Bharath Hariharan, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Parallel Support Vector Machines in Practice pdf

Stephen Tyree, Kilian Q. Weinberger, John Tran, 2014, ArXiv.

On Calibration of Modern Neural Networks pdf

Kilian Q. Weinberger, Yu Sun, Chuan Guo, 2017, ICML.

Transductive Minimax Probability Machine

Kilian Q. Weinberger, Gao Huang, Shiji Song, 2014, ECML/PKDD.

Bayesian Active Model Selection with an Application to Automated Audiometry

Roman Garnett, Gustavo Malkomes, Kilian Q. Weinberger, 2015, NIPS.

Anytime Representation Learning

Matt J. Kusner, Kilian Q. Weinberger, Gao Huang, 2013, ICML.

Simple Black-box Adversarial Attacks

Andrew Gordon Wilson, Kilian Q. Weinberger, Chuan Guo, 2019, ICML.

Revisiting Few-sample BERT Fine-tuning pdf

Kilian Q. Weinberger, Yoav Artzi, Tianyi Zhang, 2020, ArXiv.

Memory-Efficient Implementation of DenseNets pdf

Kilian Q. Weinberger, Laurens van der Maaten, Gao Huang, 2017, ArXiv.

Cost-Sensitive Tree of Classifiers

Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, 2012, ICML.

Goal-Oriented Euclidean Heuristics with Manifold Learning

Yixin Chen, Xiaoping Chen, Wenlin Chen, 2013, AAAI.

Marginalized Denoising Autoencoders for Domain Adaptation pdf

Kilian Q. Weinberger, Minmin Chen, Fei Sha, 2012, ICML.

Bayesian Optimization with Inequality Constraints

Matt J. Kusner, Kilian Q. Weinberger, John P. Cunningham, 2014, ICML.

Inferring the Causal Direction Privately

Matt J. Kusner, Kilian Q. Weinberger, Yu Sun, 2015 .

Classifier cascades and trees for minimizing feature evaluation cost

Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, 2014, J. Mach. Learn. Res..

Multi-task learning for boosting with application to web search ranking

Ya Zhang, Kilian Q. Weinberger, Belle L. Tseng, 2010, KDD.

Distance Metric Learning for Kernel Machines pdf

Kilian Q. Weinberger, Olivier Chapelle, Zhixiang Eddie Xu, 2012, ArXiv.

Integrated Triaging for Fast Reading Comprehension pdf

John Blitzer, Kilian Q. Weinberger, Ni Lao, 2019, ArXiv.

Rapid Feature Learning with Stacked Linear Denoisers pdf

Kilian Q. Weinberger, Fei Sha, Zhixiang Eddie Xu, 2011, ArXiv.

Learning to rank with (a lot of) word features

Yanjun Qi, Jason Weston, Bing Bai, 2010, Information Retrieval.

Distance Metric Learning for Large Margin Nearest Neighbor Classification

Kilian Q. Weinberger, Lawrence K. Saul, L. Saul, 2005, NIPS.

COLLABORATIVE SPAM FILTERING WITH THE HASHING TRICK

Alexander J. Smola, Kilian Q. Weinberger, Martin Zinkevich, 2009 .

Multi-Scale Dense Networks for Resource Efficient Image Classification

Kilian Q. Weinberger, Felix Wu, Laurens van der Maaten, 2017, ICLR.

LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

Kilian Q. Weinberger, Mark Campbell, Bharath Hariharan, 2019, IEEE Robotics and Automation Letters.

Compressing Neural Networks with the Hashing Trick

Yixin Chen, Stephen Tyree, Wenlin Chen, 2015, ICML.

Large Margin Taxonomy Embedding for Document Categorization

Kilian Q. Weinberger, Olivier Chapelle, O. Chapelle, 2008, NIPS.

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning pdf

Yan Wang, Kilian Q. Weinberger, Laurens van der Maaten, 2019, ArXiv.

A Recap of the AAAI and IAAI 2018 Conferences and the EAAI Symposium

Eric Eaton, Sheila A. McIlraith, Kilian Q. Weinberger, 2018, AI Mag..

Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2010, IEEE Signal Processing Magazine.

Unsupervised Learning of Image Manifolds by Semidefinite Programming

Kilian Q. Weinberger, Lawrence K. Saul, L. Saul, 2004, CVPR.

Product Kernel Interpolation for Scalable Gaussian Processes

Andrew Gordon Wilson, Kilian Q. Weinberger, Jacob R. Gardner, 2018, AISTATS.

Deep Networks with Stochastic Depth

Kilian Q. Weinberger, Yu Sun, Gao Huang, 2016, ECCV.

An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding

Kilian Q. Weinberger, Lawrence K. Saul, L. Saul, 2006, AAAI.

Neural Network Out-of-Distribution Detection for Regression Tasks

Kilian Q. Weinberger, Joseph Kim, Geoff Pleiss, 2019 .

Automatic Feature Decomposition for Single View Co-training

Yixin Chen, Kilian Q. Weinberger, Minmin Chen, 2011, ICML.

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Yan Wang, Kilian Q. Weinberger, Serge J. Belongie, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Filtered Search for Submodular Maximization with Controllable Approximation Bounds

Yixin Chen, Wenlin Chen, Kilian Q. Weinberger, 2015, AISTATS.

Characterizing the Loss Landscape in Non-Negative Matrix Factorization

Kilian Q. Weinberger, Johan Bjorck, Anmol Kabra, 2021, AAAI.

Online Adaptation to Label Distribution Shift pdf

Kilian Q. Weinberger, Ruihan Wu, Yi Su, 2021, NeurIPS.

Towards Deeper Deep Reinforcement Learning pdf

Kilian Q. Weinberger, Johan Bjorck, Carla P. Gomes, 2021, ArXiv.

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Kilian Q. Weinberger, Cole Miles, Eun-Ah Kim, 2021, Nature communications.

Performance-Efficiency Trade-Offs in Unsupervised Pre-Training for Speech Recognition pdf

Kyu J. Han, Kilian Q. Weinberger, Yoav Artzi, 2021, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Language-driven Semantic Segmentation pdf

Kilian Q. Weinberger, Vladlen Koltun, Ren'e Ranftl, 2022, ArXiv.

Towards Deeper Deep Reinforcement Learning

Kilian Q. Weinberger, Johan Bjorck, Carla P. Gomes, 2021, NeurIPS.

Low-Precision Reinforcement Learning pdf

Christopher De Sa, Kilian Q. Weinberger, Johan Bjorck, 2021, ArXiv.

Is High Variance Unavoidable in RL? A Case Study in Continuous Control pdf

Kilian Q. Weinberger, Johan Bjorck, Carla P. Gomes, 2021, ArXiv.

On Feature Normalization and Data Augmentation

Kilian Q. Weinberger, Ser-Nam Lim, Serge Belongie, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Making Paper Reviewing Robust to Bid Manipulation Attacks

Kilian Q. Weinberger, Laurens van der Maaten, Rahul Kidambi, 2021, ICML.

Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

Christopher De Sa, Kilian Q. Weinberger, Johan Bjorck, 2021, ICML.

Machine learning discovery of new phases in programmable quantum simulator snapshots pdf

Kilian Q. Weinberger, Cole Miles, Subir Sachdev, 2021, Physical Review Research.

Simplifying Graph Convolutional Networks

Kilian Q. Weinberger, Tao Yu, Felix Wu, 2019, ICML.

Large margin taxonomy embedding with an application to document categorization

Kilian Q. Weinberger, O. Chapelle, 2008, NIPS 2008.

Unsupervised learning of image manifolds by semidefinite programming

Kilian Q. Weinberger, L. Saul, 2004, CVPR 2004.

Marginalized Stacked Denoising Autoencoders

Kilian Q. Weinberger, Minmin Chen, Eddie Xu, 2012 .

Proceedings of the 27th International Conference on Neural Information Processing Systems

Kilian Q. Weinberger, K. Q. Weinberger, Z. Ghahramani, 2014 .

Feature hashing for large scale multitask learning

Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, 2009, ICML '09.

An empirical study on evaluation metrics of generative adversarial networks

Kilian Q. Weinberger, Felix Wu, Gao Huang, 2018, ArXiv.

Deep Feature Interpolation for Image Content Changes

Robert Pless, Kilian Q. Weinberger, Noah Snavely, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Fast, Continuous Audiogram Estimation Using Machine Learning

Kilian Q. Weinberger, Jacob R. Gardner, D. Barbour, 2015, Ear and hearing.

Learning from Incomplete Image Tags

Kilian Q. Weinberger, Minmin Chen, 2012 .

Metric learning with convex optimization

Kilian Q. Weinberger, L. Saul, 2007 .

A Meta Understanding of Meta-Learning

Kilian Q. Weinberger, M. Campbell, De-Chuan Zhan, 2019 .

Table of Contents.

Kilian Q. Weinberger, M. Bethge, J. Abernethy, 2017, Australian journal of primary health.

UvA-DARE The time-marginalized coalescent prior for hierarchical clustering The Time-Marginalized Coalescent Prior for Hierarchical Clustering

Kilian Q. Weinberger, L. Bottou, C. Burges, 2012 .

Optimal Convergence Rates for Convex Distributed Optimization in Networks

Kilian Q. Weinberger, Francis Bach, Kevin Scaman, 2019, J. Mach. Learn. Res..

Graphical Models for Inference with Missing Data

Kilian Q. Weinberger, M. Welling, Zoubin Ghahramani, 2014 .

Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

Antonello Baldassarre, Maurizio Corbetta, Kilian Q. Weinberger, 2016, Proceedings of the National Academy of Sciences.

Identifying Mislabeled Data using the Area Under the Margin Ranking

Kilian Q. Weinberger, Ethan R. Elenberg, Tianyi Zhang, 2020, NeurIPS.

Sparse Orthogonal Variational Inference for Gaussian Processes

Kilian Q. Weinberger, A. G. Wilson, Stephen Tyree, 2019 .

LARGE MARGIN DISCRIMINATIVE LEARNING METHODS FOR ACOUSTIC MODELING

Kilian Q. Weinberger, L. Saul, S. Roweis, 2006 .

Gradient Boosted Large Margin Nearest Neighbors

Kilian Q. Weinberger, Zhixiang Eddie Xu, Dor Kedem, 2011 .

Contrastive Learning Using Spectral Methods

Kilian Q. Weinberger, Zoubin Ghahramani, 2013 .

Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button

Kilian Q. Weinberger, L. Bottou, C. Burges, 2018 .

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain

John Shawe-Taylor, Kilian Q. Weinberger, Peter L. Bartlett, 2011, NIPS.

Wav2Seq: Pre-Training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages

Kyu J. Han, Kilian Q. Weinberger, Ryan T. McDonald, 2022, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM

Kilian Q. Weinberger, Jacob R. Gardner, Jared M. Andrews, 2020, bioRxiv.

Differentially Private Multi-Party Data Release for Linear Regression

Kilian Q. Weinberger, Yuanshun Yao, Ruihan Wu, 2022, 2206.07998.

Differentially Private Multi-Party Data Release for Linear Regression

Kilian Q. Weinberger, Yuanshun Yao, Ruihan Wu, 2022, ArXiv.

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Kilian Q. Weinberger, Bharath Hariharan, Yurong You, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection in Self-Driving Cars

Kilian Q. Weinberger, B. Hariharan, Yurong You, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Email-Spam Filtering with the Hashing-Trick

Kilian Q. Weinberger, Alex Smola, A. Dasgupta, 2009 .

Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs

Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Differentially Private Multi-Party Data Release for Linear Regression

Kilian Q. Weinberger, Yuanshun Yao, Ruihan Wu, 2022, ArXiv.

Long-term Control for Dialogue Generation: Methods and Evaluation

Kilian Q. Weinberger, Ryan T. McDonald, Ramya Ramakrishnan, 2022, NAACL.

Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning

Kilian Q. Weinberger, Chuan Guo, Ruihan Wu, 2022, ArXiv.

FIXED NEURAL NETWORK STEGANOGRAPHY: TRAIN

Kilian Q. Weinberger, V. Kishore, Xiangyu Chen, 2022 .

Does unsupervised grammar induction need pixels?

Kilian Q. Weinberger, Trevor Darrell, J. Malik, 2022, ArXiv.

Latent Diffusion for Language Generation

Kilian Q. Weinberger, V. Kishore, Justin Lovelace, 2022, NeurIPS.

Language-driven Semantic Segmentation

Kilian Q. Weinberger, V. Koltun, René Ranftl, 2022, ICLR.

Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

Kilian Q. Weinberger, Geoff Pleiss, M. Krogstad, 2020, Proceedings of the National Academy of Sciences of the United States of America.

Revisiting Few-sample BERT Fine-tuning

Kilian Q. Weinberger, Felix Wu, Tianyi Zhang, 2020, ICLR.

Convolutional Networks with Dense Connectivity

Kilian Q. Weinberger, Gao Huang, Zhuang Liu, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation

Kilian Q. Weinberger, Abhinav Shrivastava, Ser-Nam Lim, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Image Data Compression for Covariance and Histogram Descriptors

Matt J. Kusner, Kilian Q. Weinberger, Nicholas I. Kolkin, 2014 .

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Kilian Q. Weinberger, Yufan Wang, Jinjie Zhou, 2023, ArXiv.

Making Paper Reviewing Robust to Bid Manipulation Attacks

Kilian Q. Weinberger, Rahul Kidambi, Ruihan Wu, 2021, ICML.

Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications

Matt J. Kusner, Kilian Q. Weinberger, David T Bundy, 2015, PloS one.

Fast-tracking the SDGs: Driving Asia-Pacific Transformations

Kilian Q. Weinberger, O. Aijazi, H. Rankin, 2020 .

Low-Precision Reinforcement Learning

Christopher De Sa, Kilian Q. Weinberger, Carla P. Gomes, 2021, ICML.

Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao, 2022, ICLR.

Understanding Decoupled and Early Weight Decay pdf

Kilian Q. Weinberger, Johan Bjorck, Kilian Weinberger, 2020, AAAI.

Wasserstein Distances for Stereo Disparity Estimation

Kilian Q. Weinberger, B. Hariharan, Wei-Lun Chao, 2020, NeurIPS.

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

Kilian Q. Weinberger, Cheng Perng Phoo, Bharath Hariharan, 2023, NeurIPS.

Learning to Detect Mobile Objects from LiDAR Scans Without Labels

Kilian Q. Weinberger, Cheng Perng Phoo, Bharath Hariharan, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Fixed Neural Network Steganography: Train the images, not the network

Kilian Q. Weinberger, Yan Wang, Boyi Li, 2022, ICLR.

Proceedings of the 24th International Conference on Neural Information Processing Systems

Kilian Q. Weinberger, K. Q. Weinberger, J. Shawe-Taylor, 2011 .

Learning Iterative Neural Optimizers for Image Steganography

Kilian Q. Weinberger, Xiangyu Chen, Varsha Kishore, 2023, ICLR.

H INDSIGHT IS 20/20: L EVERAGING P AST T RAVER SALS TO A ID 3D P ERCEPTION

Kilian Q. Weinberger, Bharath Hariharan, Yurong You, 2022 .

Does Label Differential Privacy Prevent Label Inference Attacks?

Kilian Q. Weinberger, Chuan Guo, Ruihan Wu, 2022, AISTATS.

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

Claire Cardie, Kilian Q. Weinberger, Xilun Chen, 2016, TACL.

Physicochemical signatures of nanoparticle-dependent complement activation.

Kilian Q. Weinberger, Nathan A. Baker, Zhixiang Xu, 2014, Computational science & discovery.

IncDSI: Incrementally Updatable Document Retrieval

Kilian Q. Weinberger, Yoav Artzi, Justin Lovelace, 2023, ICML.

Supplementary Material: Wasserstein Distances for Stereo Disparity Estimation

Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao, 2020 .

Reliable tags using image similarity: mining specificity and expertise from large-scale multimedia databases

Kilian Q. Weinberger, Malcolm Slaney, Lyndon Kennedy, 2009, WSMC '09.

On the Effectiveness of Offline RL for Dialogue Response Generation

Kilian Q. Weinberger, Ryan T. McDonald, Ethan R. Elenberg, 2023, ICML.

Research Principles for Developing Country Food Value Chains

Kilian Q. Weinberger, H. Groote, C. Barrett, 2011, Science.

Is High Variance Unavoidable in RL? A Case Study in Continuous Control

Kilian Q. Weinberger, Carla P. Gomes, Johan Bjorck, 2021, ICLR.

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators

Kilian Q. Weinberger, Tom Rainforth, Frank Wood, 2020, J. Mach. Learn. Res..

Proceedings of the 25th International Conference on Neural Information Processing Systems

Kilian Q. Weinberger, K. Q. Weinberger, C. J. C. Burges, 2012 .

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Kilian Q. Weinberger, Yufan Wang, Jinjie Zhou, 2023, ICML.

A Vision-free Baseline for Multimodal Grammar Induction

Kilian Q. Weinberger, Serge J. Belongie, Trevor Darrell, 2022, 2212.10564.

Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery

Kilian Q. Weinberger, B. Hariharan, Yurong You, 2023, arXiv.org.

Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps

Kilian Q. Weinberger, Xinshuo Weng, Marco Pavone, 2023, ArXiv.

Deep Feature Interpolation for Image Content Changes

Kilian Q. Weinberger, K. Bala, Noah Snavely, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Graphical Models for Inference with Missing Data

Jin Tian, Kilian Q. Weinberger, Judea Pearl, 2013, NIPS.

Zero-shot Object-Level OOD Detection with Context-Aware Inpainting

Kilian Q. Weinberger, Dung D. Le, Zhenzhen Liu, 2024, ArXiv.

Don't Trust: Verify - Grounding LLM Quantitative Reasoning with Autoformalization

Kilian Q. Weinberger, Christian Szegedy, Charles Staats, 2024, ArXiv.