Our Reading Group

This is a seminar/reading group focused on recent trends as well as basic concepts in machine learning. Each week one of our group members will present a paper from venues including conferences such as NeurIPS, ICLR, ICML, ICCV, CVPR, ECCV, and journals such as TPAMI, JMLR, IJCV. The seminar is open to all staff from the University of Sussex. Researchers from other groups of University of Sussex are welcome to attend or present at this seminar.

Time and Location

• Friday 11:00-12:30, Meeting Room 1, Chichester 1

• 16 Jan, 2024, 12:00-13:00 — Thomas Kehrenberg
  McGrath et al., Acquisition of Chess Knowledge in AlphaZero, PNAS
• 5 Dec, 2023, 12:00-13:00 — Myles Bartlett
  Azar et al., A General Theoretical Paradigm to Understand Learning from Human Preferences, arXiv
• 31 Oct, 2023, 12:00-13:00 — Yeat Jeng Ng
  Matthew Tancik et al., Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains, NeurIPS 2020
• 24 Oct, 2023, 12:00-13:00 — Ainhize Barrainkua
  Hongyi Ling et al., Learning Fair Graph Representations via Automated Data Augmentations, ICLR 2023
• 17 Oct, 2023, 12:00-13:00 — Oliver Thomas
  Alaa, A.M. et al, Conformal Meta-learners for Predictive Inference of Individual Treatment Effects, NeurIPS 2023

  See also: NeurIPS 2023 Oral Presentation

• 11 Oct, 2023, 10:00-11:00 — Leonidas Gee
  Bachmann et al., MultiMAE: Multi-modal Multi-task Masked Autoencoders
• 27 Sep, 2023, 10:00-11:00 — Thomas Kehrenberg
  Varma et al., Explaining grokking through circuit efficiency
• 20 Sep, 2023, 10:00-11:00 — Kieran Gibb
  Wei-Ning Hsu et al., Hierarchical Generative Modeling for Controllable Speech Synthesis, ICLR, 2019
• 13 Sep, 2023, 10:00-11:00 — Myles Bartlett
  Marco Cuturi, Sinkhorn Distances: Lightspeed Computation of Optimal Transport, NIPS 2013
• 23 Aug, 2023, 10:00-11:00 — Peter Wijeratne
  Linderman S.W et al., Reparametrising the Birkhoff polytope for variational permutation inference
• 2 Aug, 2023, 10:00-11:00 — Leonidas Gee
  Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ICLR 2021
• 26 Jul, 2023, 10:00-11:00 — Aisha Lawal
  Arar M. et al., Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation, CVPR 2020
• 12 Jul, 2023, 11:00-12:00 — Gergely Németh
  Chan et al., FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
• 23 Jun, 2023, 10:00-11:00 — Ivor Simpson
  Vasconcelos et al., UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography, Transactions on Machine Learning Research 2023
• 21 Jun, 2023, 10:00-11:00 — Kieran Gibb
  Liu A.H. et al., DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning
• 7 Jun, 2023, 10:00-11:00 — Rohan
  Zielonka W. et al., Instant Volumetric Head Avatars
• 24 May, 2023, 10:00-11:00 — Pranav Deep
  Cheng Peng, Pengfei Guo, S. Kevin Zhou, Vishal M Patel, Rama Chellappa, Towards performant and reliable undersampled MR reconstruction via diffusion model sampling, MICCAI 2022
• 17 May, 2023, 10:00-11:00 — Sergey
  Takagi Y. et al., High-resolution image reconstruction with latent diffusion models from human brain activity
• 10 May, 2023, 10:00-11:00 — Peter Wijeratne
  Lorenzi M. et al., Constraining the dynamics of deep probabilistic models
• 28 Mar, 2023, 12:00-13:00 — Aisha Lawal
  Taco S. Cohen et al., Spherical CNNs, ICLR 2018
• 14 Mar, 2023, 11:00-12:00 — Ainhize Barrainkua
  Abernethy et al., Active Sampling for Min-Max Fairness, ICML 2022
• 7 Mar, 2023, 11:00-12:00 — Leonidas Gee
  Yang et al., AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning, NeurIPS 2022
• 28 Feb, 2023, 12:00-13:00 — Thomas Kehrenberg
  Burns et al., Discovering Latent Knowledge in Language Models Without Supervision
• 14 Feb, 2023, 12:00-13:00 — Sara Romiti
  Mildenhall et al., NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, ECCV 2020
• 31 Jan, 2023, 12:00-13:00 — Myles Bartlett
  Bai et al., Constitutional AI: Harmlessness from AI Feedback, arXiv
• 24 Jan, 2023, 12:00-13:00 — Yeat Jeng Ng
  Wei Hu, Neural Tangent Kernel (NTK) Made Practical
• 20 Jan, 2023, 12:00-13:00 — Chris Robinson
  Minimising Computation in BN Structure Learning with Hasse Diagrams
• 13 Jan, 2023, 12:00-13:00 — Pranav Deep
  Chang Gao et al, A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects, MICCAI 2022
• 9 Dec, 2022, 11:00-12:00 — Aisha Lawal
  Adrian V. Dalca et al., VoxelMorph: A Learning Framework for Deformable Medical Image Registration, MICCAI 2019
• 25 Nov, 2022, 11:00-12:00 — Bradley
  Self-conditioned Embedding Diffusion for Text Generation
• 18 Nov, 2022, 11:00-12:00 — Kieran Gibb
  van den Oord et al., Neural Discrete Representation learning, NIPS 2017
• 11 Nov, 2022, 11:00-12:00 — Thomas Kehrenberg
  Basics of axiomatic set theory
• 4 Nov, 2022, 11:00-12:00 — Myles Bartlett
  Fawzi et al., Discovering faster matrix multiplication algorithms with reinforcement learning, Nature
• 14 Oct, 2022, 11:00-12:00 — Sara Romiti
  Romiti et al., RealPatch: A Statistical Matching Framework for Model Patching with Real Samples, ECCV 2022
• 22 Jul, 2022, 16:00-17:00 — Ivor Simpson
  Simpson et al., Learning Structured Gaussians to Approximate Deep Ensembles, CVPR 2022
• 9 Jun, 2022, 13:30-14:30 — Yeat Jeng Ng
  Partial Monitoring
• 28 Apr, 2022, 13:00-14:00 — Thomas Kehrenberg
  Hoffmann et al., Training Compute-Optimal Large Language Models
• 21 Apr, 2022, 13:00-14:00 — Thomas Kehrenberg
  Statistical Time
• 31 Mar, 2022, 13:15-14:00 — Myles Bartlett
  Bengio et al., Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation, NeurIPS, 2021
• 17 Mar, 2022, 13:00-13:30 — Sara Romiti
  Seo et al., Unsupervised Learning of Debiased Representations with Pseudo-Attributes, CVPR 2022
• 17 Mar, 2022, 13:30-14:00 — Thomas Kehrenberg
  Hendrycks et al., Natural Adversarial Examples, CVPR 2021
• 10 Mar, 2022, 13:00-13:30 — Gergely Németh
  Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin, LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning, NeurIPS 2018
• 10 Mar, 2022, 13:30-14:00 — Chris Robinson
  Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola, Fast Counting in Machine Learning Applications, UAI 2018
• 3 Feb, 2022, 11:30-12:00 — Oliver Thomas
  Xiao et al., Tackling the Generative Learning Trilemma with Denoising Diffusion GANs, ICLR 2022

  Project page

• 16 Dec, 2021, 11:20-11:40 — Yeat Jeng Ng
  Roh et al., Sample Selection for Fair and Robust Training, NeurIPS 2021
• 16 Dec, 2021, 11:00-11:20 — Ainhize Barrainkua
  Zaidi et al., Neural Ensemble Search for Uncertainty Estimation and Dataset Shift, NeurIPS 2021
• 16 Dec, 2021, 11:40-12:00 — Sara Romiti
  Wald et al., On Calibration and Out-of-domain Generalization, NeurIPS 2021
• 18 Nov, 2021, 11:00-12:00 — All
  Khan and Rue, The Bayesian Learning Rule

  This will continue for at least 3 weeks.

• 4 Nov, 2021, 11:00-12:00 — Jack Willis
  Stammer et al., Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations, CVPR 2021
• 21 Oct, 2021, 11:00-12:00 — Oliver Thomas
  Ali et al., Accounting for Model Uncertainty in Algorithmic Discrimination, AIES 21
• 23 Sep, 2021, 11:00-12:00 — David Hurst
  Deep Learning Soundscapes for Ecoacoustics
• 9 Sep, 2021, 11:00-12:00 — Ng Yeat Jeng
  N. Puchkin and N. Zhivotovskiy., Exponential Savings in Agnostic Active Learning through Abstention, COLT 2021
• 12 Aug, 2021, 11:00-12:00 — Thomas Kehrenberg
  Chen et al., Evaluating Large Language Models Trained on Code, arXiv
• 15 Jul, 2021, 11:00-12:00 — Yuga Hikida
  Wang et al., Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation, CVPR 2020
• 1 Jul, 2021, 11:00-12:00 — Myles Bartlett
  Tsai et al., MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering, ICLR 2021
• 11 Jun, 2021, 16:00-17:00 — Thomas Kehrenberg, Myles Bartlett
  Kehrenberg et al., Addressing Missing Sources with Adversarial Support-Matching, Under review
• 14 May, 2021, 16:30-17:30 — Myles Bartlett
  Caron et al., Emerging Properties in Self-Supervised Vision Transformers, arXiv
• 7 May, 2021, 16:00-17:00 — Ali Unlu
  Introduction to MRI registration (Self-made slides)
• 26 Apr, 2021, 15:00-16:00 — Georgios Voulgaris
  Sixt et al., RenderGAN: Generating Realistic Labeled Data, Frontiers in Robotics and AI

  Included a presentation of the original GAN paper.

• 19 Apr, 2021, 15:00-16:00 — Sara Romiti
  Yu et al., Partial Feature Decorrelation for Non-I.I.D Image classification, arXiv
• 12 Apr, 2021, 15:00-16:00 — Ali Unlu
  Tucker et al., Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives, ICLR 2019
• 29 Mar, 2021, 15:00-16:00 — Thomas Kehrenberg
  Schott et al., Towards the first adversarially robust neural network model on MNIST, ICLR 2019
• 22 Mar, 2021, 15:00-16:00 — Oliver Thomas
  Bai et al, Multiscale Deep Equilibrium Models, NeurIPS '20
• 15 Feb, 2021, 15:00-16:00 — Miri Zilka
  Tutorial on Explainable Artificial Intelligence (XAI)
• 8 Feb, 2021, 15:00-16:00 — Ng Yeat Jeng
  T. Lattimore and C. Szepesvári., Bandit Algorithms
• 29 Jan, 2021, 15:00-16:00 — Ng Yeat Jeng
  T. Lattimore and C. Szepesvári., Bandit Algorithms
• 22 Jan, 2021, 15:00-16:00 — Ng Yeat Jeng
  T. Lattimore and C. Szepesvári., Bandit Algorithms
• 15 Jan, 2021, 16:00-17:00 — Everyone
  OpenAI, Discussion of OpenAI's recent image-text models: DALL-E and CLIP

  See also: CLIP paper

• 11 Dec, 2020, 16:00-17:00 — Everyone
  David Duvenaud, J. Zico Kolter, Matt Johnson, Deep Implicit Layers: Neural ODEs, Equilibrium Models and Beyond, NeurIPS 2020
• 4 Dec, 2020, 16:00-17:00 — Myles Bartlett
  Song et al., Score-Based Generative Modeling through Stochastic Differential Equations, ICLR 2021
• 27 Nov, 2020, 15:00-16:00 — Eleonora Grassucci
  Grassucci et al., Quaternion-Valued Variational Autoencoder, ICASSP
• 13 Nov, 2020, 16:00-17:00 — Chris Robinson
  Phase Diagram for Scores: Dissecting Bayesian Network Score Performance at Distinct Easy-Hard Phases, Submitted to AISTATS21, currently under (blind) review.
• 6 Nov, 2020, 16:00-17:00 — Ali Unlu
  Unlu and Aitchison, Gradient Regularisation as Approximate Variational Inference, arXiv
• 30 Oct, 2020, 15:00-16:00 — Chen Li
  Chazal et al., Persistence-Based Clustering in Riemannian Manifolds, Journal of the ACM
• 16 Oct, 2020, 16:00-17:00 — Thomas Kehrenberg
  Stiennon et al., Learning to summarize from human feedback, arXiv
• 9 Oct, 2020, 15:00-15:40 — Mary Phuong
  Phuong et al., The inductive bias of ReLU networks on orthogonally separable data, under review at ICLR 2021
• 18 Sep, 2020, 16:00-17:00 — Thomas Kehrenberg, Myles Bartlett, Oliver Thomas
  Kehrenberg et al., Null-sampling for Interpretable and Fair Representations, ECCV 2020
• 4 Sep, 2020, 16:00-17:00 — Sara Romiti
  Carion et al., End-to-End Object Detection with Transformers, ECCV 2020
• 28 Aug, 2020, 16:00-17:00 — Oliver Thomas
  Wick et al, Unlocking Fairness: a Trade-off Revisited, NeurIPS 2019

  See also: Author’s blog post

• 7 Aug, 2020, 16:00-17:00 — Oliver Thomas
  Arjovsky et al., Invariant Risk Minimization, arXiv

  See also: Author’s presentation slides

• 31 Jul, 2020, 16:00-17:00 — Georgios Voulgaris
  Achille and Soatto, Emergence of Invariance and Disentanglement in Deep Representations, Journal of Machine Learning Research, 2018
• 17 Jul, 2020, 16:00-17:00 — Miri Zilka
  Dakin et al., Built environment attributes and crime: an automated machine learning approach, Crime Science (Springer Nature)
• 10 Jul, 2020, 16:00-17:00 — Bradley Butcher
  Donahue et al., End-to-End Adversarial Text-to-Speech, arXiv
• 9 Jul, 2020, 11:00-11:40 — Ali Unlu
  Wu et al., Deterministic Variational Inference for Robust Bayesian Neural Networks, arXiv
• 3 Jul, 2020, 16:00-17:00 — Ivor Simpson
  Bahat and Michaeli, Explorable Super Resolution, CVPR 2020
• 26 Jun, 2020, 16:00-17:00 — Myles Bartlett
  Wu et al., Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild, CVPR 2020
• 19 Jun, 2020, 16:00-17:00 — Thomas Kehrenberg
  Veness et al., Gated Linear Networks, arXiv
• 10 Jun, 2020, 16:00-17:00 — Oliver Thomas
  Ghosh et al., From Variational to Deterministic Autoencoders, ICLR 2020
• 27 May, 2020, 16:00-17:00 — Ivor Simpson
  Toth et al., Hamiltonian Generative Networks, ICLR 2020
• 20 May, 2020, 16:00-17:00 — Myles Bartlett
  Grathwohl et al., Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One, ICLR, 2020
• 13 May, 2020, 16:00-17:00 — Thomas Kehrenberg
  McDuff et al., Characterizing Bias in Classifiers using Generative Models, NeurIPS, 2019
• 11 May, 2020, 16:00-16:30 — Charles Hepburn
  Gal et al., Deep Bayesian Active Learning with Image Data, ICML 2017
• 6 May, 2020, 16:00-17:00 — Georgios Voulgaris
  Forrest et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, CVPR, 2016
• 1 May, 2020, 14:00-14:30 — Qiuyi Hong
  Kilbertus et al., The Sensitivity of Counterfactual Fairness to Unmeasured Confounding, UAI 2019
• 1 May, 2020, 13:00-13:30 — Yeat Jeng
  Jabbari et al., Fairness in Reinforcement Learning, ICML 2017
• 30 Apr, 2020, 15:00-15:30 — Trust Paul
  Dimitrakakis et al., Bayesian fairness, AAAI 2019
• 25 Apr, 2020, 16:00-17:00 — Myles Bartlett
  Pinsler et al., Bayesian Batch Active Learning as Sparse Subset Approximation, NeurIPS, 2019
• 15 Apr, 2020, 15:00-16:00 — Thomas Kehrenberg
  Adel et al., Discovering Interpretable Representations for Both Deep Generative and Discriminative Models, ICML 2018
• 8 Apr, 2020, 16:00-17:00 — Miri Zilka
  Grgić-Hlača et al., Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction, WWW '18
• 1 Apr, 2020, 16:00-17:00 — Oliver Thomas
  Kirsch et al., BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning, NeurIPS 2019
• 25 Mar, 2020, 16:00-17:00 — Georgios Voulgaris
  Han et al., Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, ICLR, 2016
• 11 Mar, 2020, 16:00-17:00 — Myles Bartlett
  Balcan et al., Provable Guarantees for Gradient-Based Meta-Learning, ICML, 2020
• 31 Jan, 2020, 13:30-14:30 — Oliver Thomas
  Song et al., Learning Controllable Fair Representations, AISTATS, 2019

  See also: authors’ blog post.

• 24 Jan, 2020, 12:00-13:00 — Thomas Kehrenberg
  Zheng et al., Label Cleaning with a Likelihood Ratio Test, ICLR 2020
• 3 Dec, 2019, 16:00-17:00 — Myles Bartlett
  Vinyals et al., Grandmaster level in StarCraft II using multi-agent reinforcement learning, Nature, 2019
• 8 Nov, 2019, 16:00-17:00 — Sara Romiti
  Gu et al., Mask-Guided Portrait Editing with Conditional GANs, CVPR 2019
• 1 Nov, 2019, 16:00-17:00 — Oliver Thomas
  Creager et al., Causal Modeling for Fairness in Dynamical Systems, arXiv
• 28 Oct, 2019, 16:00-17:00 — Myles Bartlett
  Flennerhag et al., Meta-Learning with Warped Gradient Descent, Under review for ICLR, 2020
• 18 Oct, 2019, 16:00-17:00 — Thomas Kehrenberg
  Cornish et al., Localised Generative Flows, arXiv, 2019
• 11 Oct, 2019, 16:00-17:00 — Chris Robinson
  Scanagatta et al., Approximate structure learning for large Bayesian networks, ECML, 2018
• 4 Oct, 2019, 16:00-17:00 — Bradley Butcher
  Viinikka et al., Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth, AIStat, 2018
• 27 Sep, 2019, 16:00-17:00 — Thomas Kehrenberg
  Anonymous, Deep Learning For Symbolic Mathematics, Under review for ICLR, 2020
• 25 Sep, 2019, 16:00-17:00 — Myles Bartlett
  Rajeswaran et al., Meta-Learning with Implicit Gradients, NeurIPS, 2019
• 30 Aug, 2019, 16:00-17:00 — Oliver Thomas
  Liu et al., On the Variance of the Adaptive Learning Rate and Beyond, arXiv, 2019

  Zhang et al., Lookahead Optimizer: k steps forward, 1 step back, NeurIPS, 2019

• 16 Aug, 2019, 16:00-17:00 — Myles Bartlett
  van den Oord et al., Representation Learning with Contrastive Predictive Coding, ICLR, 2019
• 19 Jul, 2019, 16:00-17:00 — Thomas Kehrenberg
  Garnelo et al., Neural Processes, ICML, 2018
• 17 May, 2019, 16:00-17:00 — Sara Romiti, Chris Robinson
  Silander et al., Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures, AISTATS, 2018
• 3 May, 2019, 13:00-14:00 — Chris Robinson, Sara Romiti
  Kuipers, Suter, and Moffa, Efficient Structure Learning and Sampling of Bayesian Networks, arXiv, 2018
• 18 Apr, 2019, 12:30-13:30 — Thomas Kehrenberg
  Gal and Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, ICML, 2016
• 12 Apr, 2019, 16:00-17:00 — Myles Bartlett
  Jacobsen et al., Excessive Invariance Causes Adversarial Vulnerability, ICLR, 2019
• 5 Apr, 2019, 16:00-17:00 — Oliver Thomas
  Kallus and Zhou, Residual Unfairness in Fair Machine Learning from Prejudiced Data, ICML, 2018
• 29 Mar, 2019, 16:00-17:00 — Chris Robinson
  Kalainathan et al., SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning, arXiv, 2018
• 22 Mar, 2019, 16:00-17:00 — Chris Robinson
  Xun Zheng et al., DAGs with NO TEARS: Continuous Optimization for Structure Learning, NeurIPS, 2018
• 15 Mar, 2019, 10:00-11:00 — Chris Inskip, Bradley Butcher
  

  Survey of Graph Neural Network methods

• 1 Mar, 2019, 16:00-17:00 — Myles Bartlett
  Metz et al., Meta-Learning Update Rules for Unsupervised Representation Learning, ICLR, 2019
• 15 Feb, 2019, 16:00-17:00 — Thomas Kehrenberg
  Kingma and Dhariwal, Glow: Generative Flow with Invertible 1x1 Convolutions, NeurIPS, 2018
• 25 Jan, 2019, 16:00-17:00 — Luca Giacomoni
  Schulman et al., Trust Region Policy Optimization, arXiv, 2015
• 18 Jan, 2019, 16:00-17:00 — Sara Romiti
  Choi et al., StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, CVPR, 2018
• 11 Jan, 2019, 16:00-17:00 — Thomas Kehrenberg
  Chen et al., Neural Ordinary Differential Equations, NIPS, 2018
• Dec. 7, 2018, 16:00-17:00 — Myles Bartlett
  Yin and Zhou, Semi-Implicit Variational Inference, arXiv, 2018
• Nov. 30, 2018, 16:00-17:00 — Myles Bartlett, Chris Robinson and Sara Romiti
  Presentation of entry for Huawei denoising competition
• Nov. 9, 2018, 16:00-17:00 — Myles Bartlett
  Grathwohl et al., FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models, arXiv, 2018
• Nov. 2, 2018, 16:00-17:00 — Zexun Chen
  Richardson and Rubins, Single World Intervention Graphs: A Primer, 2013
• Oct. 19, 2018, 16:00-17:00 — Thomas Kehrenberg
  Everitt et al., Reinforcement Learning with a Corrupted Reward Channel, IJCAI, 2017
• Sep. 21, 2018, and Oct. 5, 2018 16:00-17:00 — Oliver Thomas
  Chiappa et al., Path-Specific Counterfactual Fairness, arXiv, 2018
• Sep. 14, 2018, 16:00-17:00 — Thomas Kehrenberg
  Christopher Olah, Understanding LSTMs, 2015
• Sep. 7, 2018, 16:00-17:00 — Viktoriia Sharmanska
  Hendricks et al., Women also Snowboard:Overcoming Bias in Captioning Models, arXiv, 2018
  Zhao, Reducing gender bias amplification using corpus-level constraints, arXiv, 2018
• Aug. 31, 2018, 16:00-17:00 — Oliver Thomas
  Lydia Liu et al., Delayed Impact of Fair Machine Learning, arXiv, 2018
• Aug. 24, 2018, 16:00-17:00 — Elliot Massen
  Sandra Wachter et al., Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, Harvard Journal of Law & Technology, 31 (2), 2018
• Aug. 17, 2018, 16:00-17:00 — Zexun Chen
  Peter Schulam, Suchi Saria, Reliable Decision Support using Counterfactual Models, arXiv, 2018
• Aug. 10, 2018, 10:00-11:00 — Thomas Kehrenberg
  Matt Kusner et al., Counterfactual Fairness, arXiv, 2018
• June 25, 2018, 16:00-17:00 — Myles Bartlett
  Hinton Geoffrey el al, Dynamic Routing Between Capsules, arXiv, 2017
• Mar. 30, 2018, 16:00-17:00 — Chris Robinson, Bradley Butcher, and Chris Inskip
  Xun Zheng et al., DAGs with NO TEARS: Smooth Optimization for Structure Learning, arXiv preprint, 2018
• Mar. 23, 2018, 16:00-17:00 — Zexun Chen
  Jaan Altosaar et al., Proximity Variational Inference, NIPS, 2016
• Mar. 16, 2018, 14:00-15:00 — Oliver Thomas Please note - change of time!
  David Madras et al., Learning Adversarially Fair and Transferable Representations, arXiv preprint, 2018
• Mar. 9, 2018 14:00-15:00 — Alec Tschantz Please note - change of time!
  Karl Friston et al., Active inference and epistemic value, Cognitive Neuroscience, 2015
• Feb. 21, 2018 16:00-17:00 — Alec Tschantz
  Karl Friston et al., Active inference and epistemic value Cognitive Neuroscience, 2015
• Feb. 9, 2018, 16:00-17:00 — Thomas Kehrenberg
  Scott Lundberg and Su-In Lee, A Unified Approach to Interpreting Model Predictions NIPS, 2017
• Jan. 18, 2018, 14:00-15:00 — Zexun Chen
  Edwin V Bonilla et al., Generic Inference in Latent Gaussian Process Models arXiv preprint, 2016
• Jan. 11, 2018, 14:00-15:00 — Abetharan Antony
  David Silver et al., Mastering the game of Go without human knowledge, Nature, 2017
• Dec. 14, 2017, 14:00-15:00 — Luca Giacomoni
  Volodymyr Mnih et al., Human-level control through deep reinforcement learning Nature, 2015
• Nov. 30, 2017, 14:00-15:00 — Chris Inskip
  Afshin Rahimi et al., Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks EMNLP, 2017
• Nov. 16, 2017, 14:00-15:00 — David Spence
  Yaroslav Ganin et al., Domain-Adversarial Training of Neural Networks JMLR, 2016
• Nov. 2, 2017, 14:00-15:00 — Bradley Butcher
  Francesco Orabona and Tatiana Tomassi Training Deep Networks without Learning Rates Through Coin Betting arXiv preprint, 2017
• Oct. 19, 2017, 14:00-15:00 — Oliver Thomas
  Weiyang Liu et al., Iterative Machine Teaching. ICML, 2017.
• Mar. 30, 2017, 12:00-13:00 — David Spence - please note: will be held in CALPS lab
  Discussing PhD work: Quantification under dataset shift (joint talk with NLP group)
• Friday Mar. 24, 2017, 10:00-11:00 — Richard Frost - please note: change of time and day!
  David Silver et al., Mastering the game of Go with deep neural networks and tree search. Nature, 2016 (Part 1 of 2)
• Mar. 16, 2017, 12:00-13:00 — David Spence
  Judy Hoffman, Brian Kulis, Trevor Darrell and Kate Saenko Discovering latent domains for multisource domain adaptation. ECCV, 2012
• Mar. 9, 2017, 12:00-13:00 — David Spence
  Judy Hoffman, Brian Kulis, Trevor Darrell and Kate Saenko Discovering latent domains for multisource domain adaptation. ECCV, 2012
• Mar. 2, 2017, 12:00-13:00 — Luca Giacomoni
  Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. MIT Press. (Part 2 of 2)
• Feb. 23, 2017, 12:00-13:00 — Luca Giacomoni
  Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. MIT Press. (Part 1 of 2)
• Jan. 25, 2017 — Oliver Thomas
  Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork, Learning Fair Representations. ICML, 2013.
• Jan. 10, 2017, 12:00-13:00 — David Spence
  Book chapter: Arthur Gretton, Alex Smole, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, Bernhard Schölkopf, Covariate Shift by Kernel Mean Matching.
• Dec. 14, 2016 — Pietro Galliani
  Paper: Chiyuan Zhang, Samy Bengio, Boritz Hardt, Benjamin Recht, Oriol Vinyals, Understanding Deep Thinking Requires Rethinking Generalization. arXiv preprint.
• Dec. 1, 2016 — Joseph Taylor
  Paper: Daniel Hernandez-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto Mind the Nuisance: Gaussian Process Classification using Privileged Noise. NIPS, 2014.
• Nov. 24, 2016 — Nick Jarzembowski
  Paper: Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling Bayesian Dark Knowledge. NIPS, 2015.
• Nov. 3, 2016 — Pietro Galliani
  Paper: Tommi Jaakkola, Marina Meila, and Tony Jebara, Maximum Entropy Discrimination. NIPS, 1999.
• Oct. 27, 2016 — David Spence
  Paper: Amos J Storkey, When Training and Test Sets are Different: Characterising Learning Transfer. Dataset shift in machine learning, 2009.
• Oct. 20, 2016 — Luca Giacomoni
  Paper: Eric Brochu, Vlad M. Cora, Nando de Freitas, A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv, 2010.
• Oct. 13, 2016 — Oliver Thomas (with Joe Taylor)
  Paper: Vladimir Vapnik and Rauf Izmailov, Learning Using Privileged Information: Similarity Control and Knowledge Transfer. JMLR, 2015.
• Oct. 6, 2016 — Richard Frost
  Paper: Novi Quadrianto, Alex J. Smola, Tiberio S. Caetano and Quoc V. Le, Estimating Labels from Label Proportions. JMLR, 2009.
• Aug. 18, 2016 — Pietro Galliani
  Paper: Geoffrey Hinton and Ruslan Salakhutdinov, Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. NIPS, 2007.
• Aug. 11, 2016 — David Spence
  Paper: Orsini et al., Quantifying randomness in real networks. Nature Communications, 2015.
• Aug. 4, 2016 — Joe Taylor
  Paper: David Isele, Eric Eaton and Mohammad Rostami, Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning, IJCAI 2016.
• Jul. 29, 2016 — Novi Quadrianto
  Paper: Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi, Learning Fair Classifiers, arXiv 2016.
• Jun. 23, 2016 — Pietro Galliani
  Paper: Matthew Richardson and Pedro Domingos, Markov Logic Networks, Machine Learning Journal 2005.
• Jun. 16, 2016 — Viktoriia Sharmanska
  Papers: Kiapour et al., Hipster Wars: Discovering Elements of Fashion Style, ECCV 2014; Parikh and Grauman, Relative Attributes, ICCV 2011; Herbrich et al., TrueSkill: A Bayesian Rating System, NIPS 2007.
• Jun. 9, 2016 — David Spence
  Paper: Gao, Wei, From classification to quantification in tweet sentiment analysis. Social Network Analysis and Mining, 2016.
• Apr. 28, 2016 — Joe Taylor
  Paper: Caruana, Rich and de Sa, Virginia R., Benefitting from the Variables that Variable Selection Discards. Journal of Machine Learning Research (JMLR), Vol. 3, March 2003, pp.1245-1264.
• Apr. 14, 2016 — Novi Quadrianto
  Paper: A. Defazio, F. Bach, and S. Lacoste-Julien, SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. NIPS 2014.
• Mar. 10, 2016 — Pietro Galliani
  Paper: I. Murray, R.P. Adams, and D.J.C. MacKay, Elliptical Slice Sampling. AISTATS 2010.
• Feb. 25, 2016 — Viktoriia Sharmanska
  Paper: Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang, Deep Fried Convnets. ICCV, 2015.
• Jan. 27, 2016 — David Spence
  Paper: Marco Saerens, Patrice Latinne, and Christine Decaestecker, Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure. Neural computation 14.1 (2002): 21-41.
• Jan. 13, 2016 — Joe Taylor
  Paper: David Lopez-Paz, Léon Bottou, Bernhard Schölkopf and Vladimir Vapnik, Unifying distillation and privileged information, arXiv preprint arXiv:1511.03643 (2015)
• Dec. 16, 2015 — Viktoriia Sharmanska (Viktoriia's notes)
  Paper: Jimmy Ba and Rich Caruana, Do deep nets really need to be deep? Advances in Neural Information Processing Systems, 2014.
  Related Paper: Geoffrey Hinton, Oriol Vinyals, and Jeff Dean, Distilling the knowledge in a neural network, arXiv preprint arXiv:1503.02531 (2015).
• Dec. 9, 2015 — http://videolectures.net/mlss09us_srebro_mdlwrmdfdss/
  Paper: Shai Shalev-Shwartz, Yoram Singer and Nathan Srebro. Pegasos: Primal estimated sub-gradient solver for svm. Mathematical programming 127.1 (2011): 3-30.
• Dec. 2, 2015 — David Spence
  Paper: Thorsten Joachims, A support vector method for multivariate performance measures. ICML '05 Proceedings, 2005.
• Nov. 25, 2015 — Pietro Galliani
  Paper: Yoshua Bengio, Olivier Delalleau, and Nicolas Le Roux. The curse of highly variable functions for local kernel machines. Advances in neural information processing systems. 2005.
• Nov. 18, 2015 — Roland Davis
  Paper: Kenton Murray and David Chiang. Auto-Sizing Neural Networks: With Applications to n-gram Language Models. arXiv preprint, 2015.
• Nov. 10, 2015 — David Spence
  Paper: Saikat Guha, Rajeev Rastogi and Kyuseok Shim. ROCK: A robust clustering algorithm for categorical attributes. Data Engineering, 1999.
  Background / counter-point: Carlos Ordonez, Clustering binary data streams with K-means. Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003.
• Nov. 3, 2015 — Pietro Galliani
  Paper: Rajesh Ranganath, Sean Gerrish, and David M. Blei. Black Box Variational Inference. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014.
• Oct. 28, 2015 — Joe Taylor
  Paper: Vladimir Vapnik and Akshay Vashist. A new learning paradigm: Learning using privileged information. Neural Networks, 2009
• Oct. 21, 2015 — Novi Quadrianto
  Paper: Jonathan S. Yedidia, William T. Freeman, Yair Weiss. Understanding Belief Propagation and its Generalizations. Technical Report, 2001
• Oct. 14, 2015 — Viktoriia Sharmanska
  Paper: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS, 2012
• Oct. 7, 2015 — David Spence
  Paper: Tom Fawcett and Peter A. Flach. A response to Webb and Ting's on the application of ROC analysis to predict classification performance under varying class distributions. Machine Learning, 2005
• Sep. 29, 2015 — David Spence
  Paper: Jose Barranquero, Jorge Diez, Juan Jose del Coz. Quantification-oriented learning based on reliable classifiers. Pattern Recognition, 2015
• Sep. 22, 2015 — Pietro Galliani (contd.)
  Paper: Jun Zhu, Ning Chen, Eric P. Xing. Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs. JMLR, 2014
• Sep. 15, 2015 — Pietro Galliani
  Paper: Jun Zhu, Ning Chen, Eric P. Xing. Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs. JMLR, 2014
• Sep. 8, 2015 — Joe Taylor
  Paper: Ga Wu, Scott Sanner, Rodrigo F.S.C. Oliveira. Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. AAAI, 2015.