## Posts

### Books of 2021

### How do SVMs and least-squares regression behave in high-dimensional settings? (NeurIPS 2021 paper with Navid and Daniel)

### My candidacy exam is done!

### [OPML#10] MNSBHS20: Classification vs regression in overparameterized regimes: Does the loss function matter?

### [OPML#9] CL20: Finite-sample analysis of interpolating linear classifiers in the overparameterized regime

### [OPML#8] FS97 & BFLS98: Benign overfitting in boosting

### [OPML#7] BLN20 & BS21: Smoothness and robustness of neural net interpolators

### [OPML#6] XH19: On the number of variables to use in principal component regression

### How many neurons are needed to approximate smooth functions? A summary of our COLT 2021 paper

### [OPML#5] BL20: Failures of model-dependent generalization bounds for least-norm interpolation

### [OPML#4] HMRT19: Surprises in high-dimensional ridgeless least squares interpolation

### Orthonormal function bases: what they are and why we care

### [OPML#3] MVSS19: Harmless interpolation of noisy data in regression

### [OPML#2] BLLT19: Benign overfitting in linear regression

### [OPML#1] BHX19: Two models of double descent for weak features

### [OPML#0] A series of posts on over-parameterized machine learning models

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