1.4k citations · 3k across the 100 of their papers we have counts for
105 papers
Learning to Drive Anywhere
Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar +1
Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contra…
Filtering Context Mitigates Scarcity and Selection Bias in Political Ideology Prediction
Chen Chen, Dylan Walker, Venkatesh Saligrama
We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the f…
Safe Linear Bandits over Unknown Polytopes
Aditya Gangrade, Tianrui Chen, Venkatesh Saligrama
The safe linear bandit problem (SLB) is an online approach to linear programming with unknown objective and unknown roundwise constraints, under stochastic bandit feedback of rewar…
FedHeN: Federated Learning in Heterogeneous Networks
Durmus Alp Emre Acar, Venkatesh Saligrama
We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objectiv…
Fine-grained Few-shot Recognition by Deep Object Parsing
Ruizhao Zhu, Pengkai Zhu, Samarth Mishra +1
We propose a new method for fine-grained few-shot recognition via deep object parsing. In our framework, an object is made up of K distinct parts and for each part, we learn a dict…
Learning Compositional Representations for Effective Low-Shot Generalization
Samarth Mishra, Pengkai Zhu, Venkatesh Saligrama
We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects…