Search Results for author:
Found 21 papers, 3 papers with code
Synthesizing Physically Plausible Human Motions in 3D Scenes
Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes.
Symmetry-Aware Robot Design with Structured Subgroups
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently.
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
We find a return-based uncertainty quantification for IDAQ that performs effectively.
Highly accurate model for prediction of lung nodule malignancy with CT scans
Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN).
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis
For prohibitively large-size tensor or machine learning models, we present a sampling-based stochastic algorithm with the same iteration complexity bound in expectation.
A Stochastic Composite Gradient Method with Incremental Variance Reduction
We show that this method achieves the same orders of complexity as the best known first-order methods for minimizing expected-value and finite-sum nonconvex functions, despite the additional outer composition which renders the composite gradient estimator biased.
From low probability to high confidence in stochastic convex optimization
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation.
Multi-Level Composite Stochastic Optimization via Nested Variance Reduction
We consider multi-level composite optimization problems where each mapping in the composition is the expectation over a family of random smooth mappings or the sum of some finite number of smooth mappings.
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain
To ameliorate this issue, we propose a new definition of risk, which we call caution, as a penalty function added to the dual objective of the linear programming (LP) formulation of reinforcement learning.
On the Divergence of Decentralized Non-Convex Optimization
However, by constructing some counter-examples, we show that when certain local Lipschitz conditions (LLC) on the local function gradient $\nabla f_i$'s are not satisfied, most of the existing decentralized algorithms diverge, even if the global Lipschitz condition (GLC) is satisfied, where the sum function $f$ has Lipschitz gradient.
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
Analogously to the Policy Gradient Theorem \cite{sutton2000policy} available for RL with cumulative rewards, we derive a new Variational Policy Gradient Theorem for RL with general utilities, which establishes that the parametrized policy gradient may be obtained as the solution of a stochastic saddle point problem involving the Fenchel dual of the utility function.
Wireless Communication Based on Microwave Photon-Level Detection With Superconducting Devices: Achievable Rate Prediction
Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level.
Quantumized Microwave Detection Based on $Λ$-Type Three-level Superconducting System: HMM Modeling and Performance Prediction
Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection.
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
By assuming the overparameterizaiton of policy and exploiting the hidden convexity of the problem, we further show that TSIVR-PG converges to global $\epsilon$-optimal policy with $\tilde{\mathcal{O}}(\epsilon^{-2})$ samples.
Weak Radio Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction
We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module.
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic
DSAC augments the classic critic step by requiring agents to (i) estimate their local occupancy measure in order to (ii) estimate the derivative of the local utility with respect to their occupancy measure, i. e., the "shadow reward".
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter alpha, which increases the likelihood of jumping in state space.
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature.
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
Under mild conditions, non-asymptotic finite-sample convergence to the globally optimal Q function is derived for various nonlinear function approximations.
Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks.
Operation Scheme Optimizations to Achieve Ultra-high Endurance (1010) in Flash Memory with Robust Reliabilities
Flash memory has been widely adopted as stand-alone memory and embedded memory due to its robust reliability.
