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# Questions tagged [stochastic-process]

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### Probabilistic Logic Programming vs Stochastic Logic Programming

I'm reading the paper DeepStochLog: Neural Stochastic Logic Programming. The authors differentiate between Probabilistic Logic Programming (PLP) and Stochastic Logic Programming (SLP), but I can't ...
• 3
136 views

### Consistent Sampling a Random Walk

Assume there's a random walk $S_k = X_1 + \dots + X_k$ where $X_i \in \{1, -1\}$ are uniformly iid. I want Alice and Bob to share a function $S(k) = S_k$. A straightforward approach would be to let ...
• 958
112 views

### Using martingale arguments to prove convergence of iterative algorithms

Can someone give me typical/educative examples of how martingales can be used to prove convergence of an iterative algorithmS? The examples I know of can only go so far as to show that there exists ...
• 1,453
1 vote
101 views

### What is the state of the art in first order stochastic convex optimization?

What is the optimally fastest convex risk minimizing algorithm which only uses a stochastic first order oracle? Is this SGD? What is the optimally fastest convex function minimizing algorithm which ...
• 1,453
224 views

I am trying to understand the argument in the proof of Lemmma 6.3 (page 18) of this paper https://arxiv.org/abs/1902.08179. Let me summarize the conceptual crux of the argument here using a slightly ...
• 1,453
229 views

### Martingale exit arguments for gradient Langevin dynamics

I am concerned about the proof of Lemma 6.3 (page 18) of this paper, https://arxiv.org/pdf/1902.08179.pdf which shows that for smooth convex functions the gradient Langevin dynamics has a high ...
• 1,453
1 vote
97 views

### The SQ argument in Balazs Szorenyi's paper

I am asking about the proof in Theorem 5 (page 6) of this paper, http://www.inf.u-szeged.hu/~szorenyi/Cikkek/sq_d0_ext.pdf Quite a few things about this short argument seem unclear to me, Towards the ...
• 1,453
299 views

### About assumptions needed to get convergence of stochastic gradient methods on non-convex objectives

What are the minimal conditions we know of under which we can prove that a stochastic gradient based algorithm can convergence to criticality on a non-convex objective? Are there any necessary ...
• 1,453
114 views

### Stochastic gradient methods and risk of neural nets

Under many situations it is currently provable that we can minimize the risk of neural nets using stochastic gradient based algorithms. For example : https://arxiv.org/abs/1811.03804, https://arxiv....
• 1,453
1 vote
33 views

### Getting to local/global minima with (stochastic) gradient descent on non-convex objectives

Is there any class of non-convex objective functions for which (stochastic) gradient descent can provably get to a local or a global minima? (..maybe in the approximate sense like a point such that ...
• 1,453
66 views

### Problem dependent lower bound for stochastic bandits with full information

Suppose you have a $K$ armed stochastic bandit problem but with full information. There are $K$ arms with mean rewards $\mu_1,...,\mu_K$. At each step we have to select an arm, collect the reward from ...
• 245
104 views

### Constant Width Max Sum Product Multi-objective Shortest path problem

This question is a follow-up on the question I asked three days ago here. For convenience I restate it here. I am given a graph. Each edge is labelled by a vector of numbers, called weights. They ...
271 views

### Max Sum Product Multi-objective Shortest path problem

Is anything known about the following problem: I am given a graph. Each edge is labelled by a vector of numbers, called weights. They are numbers between 0 and 1. A path is first assigned a vector, ...
45 views

### Stochastic optimization with erroneous oracles

I am interested in a class of optimization problems of which we know that the input variable is first subjected to noise $\xi$ before entering the data-producing process $f$. I write the objective in ...
• 131
212 views

### High-Probability bounds for Stochastic multi-armed Bandit Problems

This paper gives some high probability results for UCB algorithm of the form, \begin{align} \mathbb{P}(R_{n} > r_0.x) \leq O(n^{-2\rho x + 1} + x^{-2 \rho + 1} ) \end{align} where $\rho$ is a ...
• 245
3k views

### What is the probability of a virus spreading through a network given a virus source node?

Model: Consider an infinite undirected connected graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$. At time $t=0$, a given virus node $s\in\mathcal{V}$ starts infecting the network $\mathcal{G}$. ...
• 161
804 views

### Objective function for stochastic optimization

Stochastic Optimization problems in general deals with random variables in the 'loss function'. Incase of a Deterministic optimization problem with basic objective $\parallel Ax-b \parallel_2^2$, we ...
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