Sliding mode PID control of buck converters. - Khalifa Al-Hosani; Andrey Malinin; Vadim I. Utkin (ECC, 2009)

Chattering reduction using multiphase sliding mode control. - Hoon Lee; Vadim I. Utkin; Andrey Malinin (Int. J. Control, 2009)

Off-topic Response Detection for Spontaneous Spoken English Assessment. - Andrey Malinin; Rogier C. van Dalen; Kate M. Knill; Yu Wang 027; Mark J. F. Gales (ACL (1), 2016)

Incorporating Uncertainty into Deep Learning for Spoken Language Assessment. - Andrey Malinin; Anton Ragni; Kate M. Knill; Mark J. F. Gales (ACL (2), 2017)

A hierarchical attention based model for off-topic spontaneous spoken response detection. - Andrey Malinin; Kate M. Knill; Mark J. F. Gales (ASRU, 2017)

An attention based model for off-topic spontaneous spoken response detection: An Initial Study. - Andrey Malinin; Kate M. Knill; Anton Ragni; Yu Wang 027; Mark J. F. Gales (SLaTE, 2017)

Impact of ASR Performance on Free Speaking Language Assessment. - Kate M. Knill; Mark J. F. Gales; Konstantinos Kyriakopoulos; Andrey Malinin; Anton Ragni; Yu Wang 027; Andrew Caines (INTERSPEECH, 2018)

Predictive Uncertainty Estimation via Prior Networks. - Andrey Malinin; Mark J. F. Gales (NeurIPS, 2018)

Improved Auto-Marking Confidence for Spoken Language Assessment. - Marco Del Vecchio; Andrey Malinin; Mark J. F. Gales (SLT, 2018)

Prior Networks for Detection of Adversarial Attacks. - Andrey Malinin; Mark J. F. Gales (CoRR, 2018)

Towards automatic assessment of spontaneous spoken English. - Yu Wang 027; Mark J. F. Gales; Kate M. Knill; Konstantinos Kyriakopoulos; Andrey Malinin; Rogier C. van Dalen; M. Rashid (Speech Commun., 2018)

Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness. - Andrey Malinin; Mark J. F. Gales (NeurIPS, 2019)

Uncertainty estimation in deep learning with application to spoken language assessment. - Andrey Malinin (2019)

Ensemble Distribution Distillation. - Andrey Malinin; Bruno Mlodozeniec; Mark J. F. Gales (ICLR, 2020)

Ensemble Approaches for Uncertainty in Spoken Language Assessment. - Xixin Wu; Kate M. Knill; Mark J. F. Gales; Andrey Malinin (INTERSPEECH, 2020)

Uncertainty in Structured Prediction. - Andrey Malinin; Mark J. F. Gales (CoRR, 2020)

Regression Prior Networks. - Andrey Malinin; Sergey Chervontsev; Ivan Provilkov; Mark J. F. Gales (CoRR, 2020)

Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance. - Carel van Niekerk; Andrey Malinin; Christian Geishauser; Michael Heck; Hsien-Chin Lin; Nurul Lubis; Shutong Feng; Milica Gasic (EMNLP (1), 2021)

Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation. - Ivan Provilkov; Andrey Malinin (EMNLP (1), 2021)

Ensemble Distillation Approaches for Grammatical Error Correction. - Yassir Fathullah; Mark J. F. Gales; Andrey Malinin (ICASSP, 2021)

Uncertainty Estimation in Autoregressive Structured Prediction. - Andrey Malinin; Mark J. F. Gales (ICLR, 2021)

Uncertainty in Gradient Boosting via Ensembles. - Andrey Malinin; Liudmila Prokhorenkova; Aleksei Ustimenko (ICLR, 2021)

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. - Ekaterina Lobacheva; Maxim Kodryan; Nadezhda Chirkova; Andrey Malinin; Dmitry P. Vetrov (NeurIPS, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Yarin Gal; Mark J. F. Gales; Alexander Ganshin; German Chesnokov; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (NeurIPS Datasets and Benchmarks, 2021)

Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets. - Max Ryabinin; Andrey Malinin; Mark J. F. Gales (NeurIPS, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Alexander Ganshin; German Chesnokov; Yarin Gal; Mark J. F. Gales; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (CoRR, 2021)

Shifts 2.0: Extending The Dataset of Real Distributional Shifts. - Andrey Malinin; Andreas Athanasopoulos; Muhamed Barakovic; Meritxell Bach Cuadra; Mark J. F. Gales; Cristina Granziera; Mara Graziani; Nikolay Kartashev; Konstantinos Kyriakopoulos; Po-Jui Lu; Nataliia Molchanova; Antonis Nikitakis; et al. (CoRR, 2022)

Novel Structural-Scale Uncertainty Measures and Error Retention Curves: Application to Multiple Sclerosis. - Nataliia Molchanova; Vatsal Raina; Andrey Malinin; Francesco La Rosa; Henning Müller; Mark J. F. Gales; Cristina Granziera; Mara Graziani; Meritxell Bach Cuadra (ISBI, 2023)

Tackling Bias in the Dice Similarity Coefficient: Introducing NDSC for White Matter Lesion Segmentation. - Vatsal Raina; Nataliia Molchanova; Mara Graziani; Andrey Malinin; Henning Müller; Meritxell Bach Cuadra; Mark J. F. Gales (ISBI, 2023)

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. - Gleb Bazhenov; Denis Kuznedelev; Andrey Malinin; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation. - Nataliia Molchanova; Vatsal Raina; Andrey Malinin; Francesco La Rosa; Adrien Depeursinge; Mark J. F. Gales; Cristina Granziera; Henning Müller; Mara Graziani; Meritxell Bach Cuadra (Comput. Biol. Medicine, 2025)