Saddle Points and Accelerated Perceptron Algorithms
Authors: Adams Wei Yu, Fatma Kilinc-Karzan, Jaime Carbonell
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theoretical findings with an empirical study on synthetic and real data, highlighting the efficiency and numerical stability of our algorithm, especially on large-scale instances. |
| Researcher Affiliation | Academia | Adams Wei Yu WEIYU@CS.CMU.EDU Fatma Kılınc -Karzan FKILINC@ANDREW.CMU.EDU Jaime G. Carbonell JGC@CS.CMU.EDU School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA |
| Pseudocode | Yes | Algorithm 1 MPFP 1: Input: ω-center zω, step size {γt} and ϵ. 2: Output: zt(= [xt; yt]). 3: t = 1; v1 = zω; 4: while φ(yt) 0 and ϵsad(zt) > ϵ do 5: wt = Proxvt(γt F(vt)); 6: vt+1 = Proxvt(γt F(wt)); 7: zt = h Pt s=1 γs i 1 Pt s=1 γsws; 8: t = t + 1; 9: end while |
| Open Source Code | No | The paper states that 'All the codes are written in Matlab 2011b', but it does not provide an explicit statement about the public availability of the source code or a link to a repository. |
| Open Datasets | Yes | Our testbed is the CIFAR-10 (Krizhevsky, 2009) dataset. [...] We evenly pick around 1,000 images in each class to construct a training set of n = 10, 000 in total, and repeat this process to form a testing set of the same size. |
| Dataset Splits | No | The paper mentions a training set and a testing set, but it does not specify a separate validation set or describe a cross-validation strategy. |
| Hardware Specification | Yes | All the codes are written in Matlab 2011b, and are ran in a single-threaded fashion on a Linux server with 6 dual core 2.8GHz CPU and 64 GB memory. |
| Software Dependencies | Yes | All the codes are written in Matlab 2011b |
| Experiment Setup | Yes | In the implementation of MPLFP or MPKFP, we select αx, αy as described in the appendix, for a normalized matrix A, i.e., Aj 2 = 1 for all j. [...] The iteration limit is 106 and runtime limit is 5 104s. [...] We choose Radial Basis Function (RBF) kernel given by KΦ(Ai, Aj) = exp{ 5.5( Ai Aj 2)}. |