Papers
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani et al.
Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Extreme Learning to Rank via Low Rank Assumption
Minhao Cheng, Ian Davidson, Cho-Jui Hsieh
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song
End-to-End Learning for the Deep Multivariate Probit Model
Di Chen, Yexiang Xue, Carla Gomes
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang et al.
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Zaiyi Chen, Yi Xu, Enhong Chen et al.
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles et al.
Variational Inference and Model Selection with Generalized Evidence Bounds
Liqun Chen, Chenyang Tao, Ruiyi Zhang et al.
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen, Le Song, Martin Wainwright et al.
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen, Jeffrey Pennington, Samuel Schoenholz
PixelSNAIL: An Improved Autoregressive Generative Model
XI Chen, Nikhil Mishra, Mostafa Rohaninejad et al.
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen, Martin Renqiang Min, Yizhou Sun
Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham et al.
Scalable Bilinear Pi Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen et al.
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen, Christopher Harshaw, Hamed Hassani et al.
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen, Moran Feldman, Amin Karbasi
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee et al.
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Zhengping Che, Sanjay Purushotham, Guangyu Li et al.
Hierarchical Clustering with Structural Constraints
Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri Chatterji, Nicolas Flammarion, Yian Ma et al.
Learning and Memorization
Satrajit Chatterjee
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles, Dimitris Papailiopoulos
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li et al.