Government
Multi-Resolution Weak Supervision for Sequential Data
Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré
Neural Information Processing Systems http://nips.cc/
Inference for Batched Bandits
However, for many real-world problems it is not enough to just minimize regret on a particular problem instance. For example, suppose we have run an online education experiment using a bandit algorithm where we test different types of teaching strategies.
Triad Constraints for Learning Causal Structure of Latent Variables
Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables).
End to end learning and optimization on graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
Neural Information Processing Systems http://nips.cc/
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
We demonstrate that the proposed method successfully learns to construct an unseen object conditioned on a single image or multiple views of a target object.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis
However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures.
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Today's methods are either fast but brittle (gradient-based
Learning Rich Rankings
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e.g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering. Explicit mixture models have provided some tools for modelling multimodal ranking data, though learning such models from data is often difficult.
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher
We propose a practical inexact augmented Lagrangian method (iALM) for noncon-vex problems with nonlinear constraints.
Appendices to " GNNGUARD: Defending Graph Neural Networks against Adversarial Attacks "
Results are shown in Table 6. T able 6: Defense performance (multi-class classification accuracy) against influence targeted attacks. Results are shown in Table 7. To evaluate how harmful non-targeted attacks can be for GNNs, we first give results without attack and under attack (without defense), i.e., "Attack" vs. "No Attack" columns The accuracy of even the strongest GNN is reduced by 18.7% on GNN if the defender is used on clean, non-attacked graphs. GNNs when they are attacked.