Instance Reweighting

Latest papers

15 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities
Oct 14, 2026Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian Möller +1Instance Reweighting Autonomous Driving
Automation and Systems Engineering Department, Federal University of Santa Catarina, Florianópolis, Brazil · Faculty of Science and Engineering, Information Technology Åbo Akademi University, Finland
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Oct 13, 2026Abdoulaye Sakho, Emmanuel Malherbe, Erwan ScornetClass-Imbalanced Learning Instance Reweighting
LPSM
Optimal Transport Reweighting for Robust Learning under Spurious Correlations and Label Noise
Oct 1, 2026Sung Ho Jo, Seonghwi Kim, Wonsang Yun +1Distribution Shift Robustness Spurious Correlation Robustness
Pohang University of Science and Technology (POSTECH)
Evaluating and improving crop-yield forecasting methods during extreme drought
Aug 18, 2026Shrey Gupta, Yi Ming, George MohlerInstance Reweighting Crop Yield Prediction
Boston College, US.
Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learning
Aug 4, 2026Wenxiao Fan, Kan LiInstance Reweighting Noisy-Label Learning
School of Computer Science, Beijing Institute of Technology
Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
Aug 1, 2026Ying Jin, Ying Jin, Dominik RothenhäuslerInverse Probability Weighting Distribution Shift Robustness
Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift
Jun 22, 2026Keisuke Sugawara, Kento Uchida, Shinichi ShirakawaClass-Imbalanced Learning Instance Reweighting
Yokohama National University, Kanagawa, Japan
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators
May 25, 2026Tongtong Fang, Nan Lu, Gang Niu +2Importance Weighting Instance Reweighting
The Institute of Statistical Mathematics, Tokyo, Japan. · University of Bristol, Bristol, U.K. · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. +1
Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection
May 20, 2026Zhu Liu, Yuanhang Yao, Ping Qian +2Instance Reweighting Infrared Small Target Detection
School of Software Technology, Dalian University of Technology, Dalian, China.
Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
May 8, 2026Matthias Schott, Lucie FlekInstance Reweighting Distribution Matching
Institute of Physics, University of Bonn, Germany · Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Germany
Model-Agnostic Meta Learning for Class Imbalance Adaptation
Apr 20, 2026Hanshu Rao, Guangzeng Han, Xiaolei HuangClass-Imbalanced Learning Instance Reweighting
University of Memphis
Evolution-Inspired Sample Competition for Deep Neural Network Optimization
Apr 14, 2026Ying Zheng, Yiyi Zhang, Yi Wang +1Instance Reweighting Deep Learning Optimization
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China. · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers
Mar 31, 2026Quanhao Li, Wei JiangInstance Reweighting Game-Playing Agents
Abbey Park High School · Shanghai Soong Ching Ling School
Conservative Offline Robot Policy Learning via Posterior-Transition Reweighting
Mar 17, 2026Wanpeng Zhang, Hao Luo, Sipeng Zheng +6Instance Reweighting Robot Policy Adaptation
Peking University · BeingBeyond · Tsinghua University
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
Feb 18, 2026Xu Zhang, Peng Wang, Yichen Li +1Instance Reweighting Time Series Forecasting
Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Department of Electrical and Computer Engineering, University of British Columbia (UBC), Vancouver, Canada

Oct 14, 2026J/K move · Enter open · S save

Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities

Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian Möller, Pedro Henrique Fernandes

Automation and Systems Engineering Department, Federal University of Santa Catarina, Florianópolis, Brazil · Faculty of Science and Engineering, Information Technology Åbo Akademi University, Finland

Instance Reweighting Autonomous Driving Behavior Cloning Imitation Learning Autonomous Driving Simulation

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This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environments. BC uses supervised learning to mimic expert driving behaviors. Previous works have applied BC in the CARLA simulator but did not fully address the challenges of traffic light compliance. Our approach enhances BC by integrating a kernel density estimator to adjust training sample weights based on action density, thereby improving the learning of rare but critical actions such as stopping at red lights and accelerating at green lights, specially in scenarios of scarce number of expert demonstrations. Using BEV inputs, which provide an abstract top-down view of the driving environment, our method simplifies the policy learning process. The trained convolutional neural network (CNN) outputs steering and acceleration actions based on these BEV inputs and additional state variables. Experimental results in the CARLA simulator demonstrate that our weighted BC method significantly improves driving performance, achieving higher route completion compared to standard BC. This weighted approach proved to be crucial in learning correct driving behaviors, particularly in test environments not encountered during training, highlighting its potential for enhancing autonomous vehicle navigation.