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Found 31 papers, 7 papers with code
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Diffusion handles is a novel approach to enable 3D object edits on diffusion images requiring only existing pre-trained diffusion models depth estimation without any fine-tuning or 3D object retrieval.
Improved Differentially Private and Lazy Online Convex Optimization
We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO).
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Our key insight is to lift diffusion activations for an object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space.
Variance-Reduced Conservative Policy Iteration
We study the sample complexity of reducing reinforcement learning to a sequence of empirical risk minimization problems over the policy space.
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret
We consider the fundamental problem of online control of a linear dynamical system from two different viewpoints: regret minimization and competitive analysis.
Introduction to Online Nonstochastic Control
In online nonstochastic control, both the cost functions as well as the perturbations from the assumed dynamical model are chosen by an adversary.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
A Boosting Approach to Reinforcement Learning
Reducing reinforcement learning to supervised learning is a well-studied and effective approach that leverages the benefits of compact function approximation to deal with large-scale Markov decision processes.
SurgeonAssist-Net: Towards Context-Aware Head-Mounted Display-Based Augmented Reality for Surgical Guidance
On a widely used benchmark dataset for laparoscopic surgical workflow, our implementation competes with state-of-the-art approaches in prediction accuracy for automated task recognition, and yet requires 7. 4x fewer parameters, 10. 2x fewer floating point operations per second (FLOPS), is 7. 0x faster for inference on a CPU, and is capable of near real-time performance on the Microsoft HoloLens 2 OST-HMD.
A Regret Minimization Approach to Iterative Learning Control
We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics.
Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite.
Boosting for Online Convex Optimization
In this access model, we give an efficient boosting algorithm that guarantees near-optimal regret against the convex hull of the base class.
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
Coexistance of non-Fermi liquid behavior and bi-quadratic exchange coupling in La-substituted CeGe: Non-linear susceptibility and DFT + DMFT study
Under the application of magnetic fields, local moments interact spatially through conduction electrons resulting in magnetic fluctuations.
Strongly Correlated Electrons
RigNet: Neural Rigging for Articulated Characters
We present RigNet, an end-to-end automated method for producing animation rigs from input character models.
No-Regret Prediction in Marginally Stable Systems
This requires a refined regret analysis, including a structural lemma showing the current state of the system to be a small linear combination of past states, even if the state grows polynomially.
Improper Learning for Non-Stochastic Control
We consider the problem of controlling a possibly unknown linear dynamical system with adversarial perturbations, adversarially chosen convex loss functions, and partially observed states, known as non-stochastic control.
The Nonstochastic Control Problem
We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions.
Logarithmic Regret for Online Control
We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics.
Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets
We present a learning method for predicting animation skeletons for input 3D models of articulated characters.
Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise).
Provably Efficient Maximum Entropy Exploration
Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do?
Efficient Full-Matrix Adaptive Regularization
Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.
Color Sails: Discrete-Continuous Palettes for Deep Color Exploration
Then, the user can adjust color sail parameters to change the base colors, their blending behavior and the number of colors, exploring a wide range of options for the original design.
VisemeNet: Audio-Driven Animator-Centric Speech Animation
We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio.
Graphics
Spectral Filtering for General Linear Dynamical Systems
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix.
Towards Provable Control for Unknown Linear Dynamical Systems
We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state.
Learning Linear Dynamical Systems via Spectral Filtering
We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix.
Efficient Regret Minimization in Non-Convex Games
We consider regret minimization in repeated games with non-convex loss functions.
Dynamic Task Allocation for Crowdsourcing Settings
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers.
The Price of Differential Privacy For Online Learning
We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $\tilde{O}(\sqrt{T})$ regret bounds.
