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1. Implicit Bias ≠ Bias on Implicit Measures

   https://doi.org/10.1080/1047840X.2022.2106750  

   Gawronski, Bertram; Ledgerwood, Alison; Eastwick, Paul W. (July 2022, Psychological Inquiry)
2. Reflections on the Difference Between Implicit Bias and Bias on Implicit Measures

   https://doi.org/10.1080/1047840X.2022.2115729  

   Gawronski, Bertram; Ledgerwood, Alison; Eastwick, Paul W. (July 2022, Psychological Inquiry)
3. CoIR: Compressive Implicit Radar

   https://doi.org/10.1109/TPAMI.2023.3301553  

   Farrell, S.; Boominathan, V.; Raymondi, N.; Sabharwal, A.; Veeraraghavan, A. (August 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data. 

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4. The Implicit Delta Method

   Nathan Kallus, James McInerney (January 2022, Advances in neural information processing systems)

   Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downstream evaluation that uses the model as input. When the predictive model is simple and its evaluation differentiable, this task is solved by the delta method, where we propagate the asymptotically-normal uncertainty in the predictive model through the evaluation to compute standard errors and Wald confidence intervals. However, this becomes difficult when the model and/or evaluation becomes more complex. Remedies include the bootstrap, but it can be computationally infeasible when training the model even once is costly. In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of the predictive model to automatically assess downstream uncertainty. We show that the change in the evaluation due to regularization is consistent for the asymptotic variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference. This provides both a reliable quantification of uncertainty in terms of standard errors as well as permits the construction of calibrated confidence intervals. We discuss connections to other approaches to uncertainty quantification, both Bayesian and frequentist, and demonstrate our approach empirically. 

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5. Implicit Distributional Reinforcement Learning

   Yue, Yuguang; Wang, Zhendong; Zhou, Mingyuan (December 2020, Advances in neural information processing systems)

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