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Found 27 papers, 4 papers with code
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
This is a corollary of the major contribution of the current paper: the first sample complexity upper bound for the problem (introduced in \cite{BK20}) of learning the vertices of a Latent $k-$ Polytope in ${\bf R}^d$, given perturbed points from it.
Random Separating Hyperplane Theorem and Learning Polytopes
Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes.
BNSynth: Bounded Boolean Functional Synthesis
In this paper, we introduce a tool BNSynth, that is the first to solve the BFS problem under a given bound on the solution space.
Shot-frugal and Robust quantum kernel classifiers
Quantum kernel methods are a candidate for quantum speed-ups in supervised machine learning.
Rawlsian Fair Adaptation of Deep Learning Classifiers
Given any score function or feature representation and only its second-order statistics on the sensitive sub-populations, we seek a threshold classifier on the given score or a linear threshold classifier on the given feature representation that achieves the Rawls error rate restricted to this hypothesis class.
DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.
Learning a Latent Simplex in Input-Sparsity Time
We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$).
Learning a Latent Simplex in Input Sparsity Time
Bhattacharyya and Kannan (SODA 2020) give an algorithm for learning such a $k$-vertex latent simplex in time roughly $O(k\cdot\text{nnz}(\mathbf{A}))$, where $\text{nnz}(\mathbf{A})$ is the number of non-zeros in $\mathbf{A}$.
Algorithms for finding $k$ in $k$-means
Two challenges are open: (i) Is there a data-determined definition of $k$ which is provably correct and (ii) Is there a polynomial time algorithm to find $k$ from data ?
Analysis of Knowledge Transfer in Kernel Regime
Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network.
Word2Sense: Sparse Interpretable Word Embeddings
We present an unsupervised method to generate Word2Sense word embeddings that are interpretable {---} each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word.
Finding a latent k-simplex in O(k . nnz(data)) time via Subset Smoothing
In this paper we show that a large class of Latent variable models, such as Mixed Membership Stochastic Block(MMSB) Models, Topic Models, and Adversarial Clustering, can be unified through a geometric perspective, replacing model specific assumptions and algorithms for individual models.
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.
Ranked #5 on
Relation Extraction
on NYT Corpus
How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?
Our proposed algorithm, {\it Pref-Rank}, predicts the underlying ranking using an SVM based approach over the chosen embedding of the product graph, and is the first to provide \emph{statistical consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's footrule}, with a required sample complexity of $O(n^2 \chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic number} of the complement graph $\bar{G}$.
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Word embeddings have been widely adopted across several NLP applications.
Using Inherent Structures to design Lean 2-layer RBMs
Understanding the representational power of Restricted Boltzmann Machines (RBMs) with multiple layers is an ill-understood problem and is an area of active research.
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering are beset by local minima, which are sometimes drastically suboptimal.
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov\'{a}sz~$\vartheta$ function.
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
We introduce a unifying generalization of the Lovász theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges.
Learning on graphs using Orthonormal Representation is Statistically Consistent
This, for the first time, relates labelled sample complexity to graph connectivity properties, such as the density of graphs.
A provable SVD-based algorithm for learning topics in dominant admixture corpus
Our aim is to develop a model which makes intuitive and empirically supported assumptions and to design an algorithm with natural, simple components such as SVD, which provably solves the inference problem for the model with bounded $l_1$ error.
Mining Block I/O Traces for Cache Preloading with Sparse Temporal Non-parametric Mixture of Multivariate Poisson
Our first contribution addresses this gap by proposing a DP based mixture model of Multivariate Poisson (DP-MMVP) and its temporal extension(HMM-DP-MMVP) that captures the full covariance structure of multivariate count data.
Temporally Coherent Bayesian Models for Entity Discovery in Videos by Tracklet Clustering
The task of \emph{Entity Discovery} in videos can be naturally posed as tracklet clustering.
Controlled Sparsity Kernel Learning
The CSKL formulation introduces a parameter t which directly corresponds to the number of kernels selected.
The Lovász ϑ function, SVMs and finding large dense subgraphs
We show that the random graph with a planted clique is an example of $SVM-\theta$ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art.
Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions
We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case.
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Motivated from real world problems, like object categorization, we study a particular mixed-norm regularization for Multiple Kernel Learning (MKL).
