# Covariance Prediction via Convex Optimization

1 code implementation29 Jan 2021,

We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector.

18

# Low Rank Forecasting

no code implementations29 Jan 2021, ,

Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate.

# Portfolio Construction Using Stratified Models

In this paper we develop models of asset return mean and covariance that depend on some observable market conditions, and use these to construct a trading policy that depends on these conditions, and the current portfolio holdings.

# Learning Convex Optimization Models

A convex optimization model predicts an output from an input by solving a convex optimization problem.

1,740

# Optimal Representative Sample Weighting

We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values.

16

# Multi-Period Liability Clearing via Convex Optimal Control

1 code implementation18 May 2020,

We consider the problem of determining a sequence of payments among a set of entities that clear (if possible) the liabilities among them.

4

# Convex Optimization Over Risk-Neutral Probabilities

We describe a number of convex optimization problems over the convex set of risk neutral price probabilities.

12

# Automatic Repair of Convex Optimization Problems

Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem's parameters such that the problem becomes solvable?

Optimization and Control

14

# Learning Convex Optimization Control Policies

Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies.

# Differentiable Convex Optimization Layers

In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.

1,740

# Minimizing a Sum of Clipped Convex Functions

We consider the problem of minimizing a sum of clipped convex functions; applications include clipped empirical risk minimization and clipped control.

5

# A Distributed Method for Fitting Laplacian Regularized Stratified Models

2 code implementations26 Apr 2019, ,

In a basic and traditional formulation a separate model is fit for each value of the categorical feature, using only the data that has the specific categorical value.

25

# Differentiating Through a Cone Program

These correspond to computing an approximate new solution, given a perturbation to the cone program coefficients (i. e., perturbation analysis), and to computing the gradient of a function of the solution with respect to the coefficients.

Optimization and Control

87

# Least Squares Auto-Tuning

1 code implementation10 Apr 2019,

Least squares is by far the simplest and most commonly applied computational method in many fields.

9

# Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data

Models for predicting aircraft motion are an important component of modern aeronautical systems.

15

# Improved Training with Curriculum GANs

We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.

# Optimizing for Generalization in Machine Learning with Cross-Validation Gradients

1 code implementation18 May 2018,

Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance.

5

# Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication

Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for compatibility and efficiency.

# A Note on the Inception Score

8 code implementations6 Jan 2018,

Deep generative models are powerful tools that have produced impressive results in recent years.

691

# InterpNET: Neural Introspection for Interpretable Deep Learning

1 code implementation26 Oct 2017

This paper proposes a neural network design paradigm, termed InterpNET, which can be combined with any existing classification architecture to generate natural language explanations of the classifications.

10
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