conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Stochastic Processes

2,667 papers

Papers per year

Papers

Analysis of one-hidden-layer neural networks via the resolvent method NIPS 2021
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines NIPS 2021
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs NIPS 2021
Conditioning Sparse Variational Gaussian Processes for Online Decision-making NIPS 2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference NIPS 2021
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning NIPS 2021
Indexed Minimum Empirical Divergence for Unimodal Bandits NIPS 2021
Multi-Agent Reinforcement Learning in Stochastic Networked Systems NIPS 2021
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization NIPS 2021
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning NIPS 2021
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems NIPS 2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret NIPS 2021
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data NIPS 2021
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models NIPS 2021
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination NIPS 2021
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation NIPS 2021
Latent Matters: Learning Deep State-Space Models NIPS 2021
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models NIPS 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling NIPS 2021
Making the most of your day: online learning for optimal allocation of time NIPS 2021
Numerical Composition of Differential Privacy NIPS 2021
Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees NIPS 2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) NIPS 2021
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent NIPS 2021
Large-Scale Wasserstein Gradient Flows NIPS 2021