Papers
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Assaf Dauber, Meir Feder, Tomer Koren et al.
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Disi Ji, Padhraic Smyth, Mark Steyvers
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
David Novotny, Roman Shapovalov, Andrea Vedaldi
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Vitaly Kurin, Saad Godil, Shimon Whiteson et al.
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang, Qi Cai, Zhuoran Yang et al.
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks
Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu et al.
Cascaded Text Generation with Markov Transformers
Yuntian Deng, Alexander Rush
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Davis Rempe, Tolga Birdal, Yongheng Zhao et al.
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Causal analysis of Covid-19 Spread in Germany
Atalanti Mastakouri, Bernhard Schölkopf
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam et al.
Causal Discovery in Physical Systems from Videos
Yunzhu Li, Antonio Torralba, Anima Anandkumar et al.
Causal Estimation with Functional Confounders
Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
Causal Imitation Learning With Unobserved Confounders
Junzhe Zhang, Daniel Kumor, Elias Bareinboim
Causal Intervention for Weakly-Supervised Semantic Segmentation
Dong Zhang, Hanwang Zhang, Jinhui Tang et al.
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur et al.
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Julian Bitterwolf, Alexander Meinke, Matthias Hein
Certified Defense to Image Transformations via Randomized Smoothing
Marc Fischer, Maximilian Baader, Martin Vechev
Certified Monotonic Neural Networks
Xingchao Liu, Xing Han, Na Zhang et al.
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri et al.
Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi et al.
Certifying Strategyproof Auction Networks
Michael Curry, Ping-yeh Chiang, Tom Goldstein et al.
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games
Yun Kuen Cheung, Georgios Piliouras
Characterizing emergent representations in a space of candidate learning rules for deep networks
Yinan Cao, Christopher Summerfield, Andrew Saxe
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Sanghack Lee, Elias Bareinboim