Neural Information Processing Systems http://nips.cc/

We design a suite of benchmarks to evaluate the quality of the generated MILP instances.

We design a suite of benchmarks to evaluate the quality of the generated MILP instances.

We develop new models and algorithms for learning the temporal dynamics of thetopic polytopes andrelated geometric objects thatarise intopic model based inference.

In many sequential decision making applications, the change of decision would bring an additional cost, such as the wear-and-tear cost associated with changing server status. To control the switching cost, we introduce the problem of online convex optimization with continuous switching constraint, where the goal is to achieve a small regret given a budget on the overall switching cost. We first investigate the hardness of the problem, and provide a lower bound of orderΩ( T)whentheswitchingcostbudgetS = Ω( T),andΩ(min{T/S,T}) whenS = O( T), where T is the time horizon. The essential idea is to carefully design an adaptive adversary, who can adjust the loss function according to thecumulative switchingcostofthe playerincurredso farbasedonthe orthogonal technique. We then develop a simple gradient-based algorithm which enjoys the minimax optimal regret bound.

Acommon approach istoplace aGaussian process prior on the unknown constraint and allowevaluations only inregions that are safe with high probability. Most current methods rely on a discretization of the domain and cannot be directly extended to the continuous case. Moreover, the way in which they exploit regularity assumptions about the constraint introduces an additional critical hyperparameter.

Changes inimage statistics ordistracting background elements are pitfalls that prevent generalization and real-world applicability of such control policies.

The biggerµ is,thebetter the error correction. For the set of( x) that form a group, a matrix representationM( x) is equivalent to another representation M( x)if there exists an invertible matrixP such that M( x)=PM( x)P 1 for each x. A matrix representation is reducible if it is equivalent to a block diagonal matrix representation, i.e., we can find a matrixP, such thatPM( x)P 1 is block diagonal for every x. IfM is block-diagonal,M =diag(Mk,k=1,...,K), with nonequivalentblocks,andeachblock Mkcannotbefurtherreduced,thenthematrixelements (Mkij( x)) are orthogonal basis functions of x. Such orthogonality relations are proved by Schur [15] for finite group, and by Peter-Weyl for compact Lie group [13].