16 citations · 79 across the 28 of their papers we have counts for
30 papers
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth +2
How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused on agent skills: reusable capab…
Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
Grigory Bartosh, Teodora Pandeva, Sushrut Karmalkar +1
Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete diffusion typically parameterizes…
Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task
Alicia Curth, Rachel Lawrence, Sushrut Karmalkar +1
We investigate whether transformers use their depth adaptively across tasks of increasing difficulty. Using a controlled multi-hop relational reasoning task based on family stories…
Robust Learning of a Group DRO Neuron
Guyang Cao, Shuyao Li, Sushrut Karmalkar +1
We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional shifts, for a broad family of c…
Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing
Iskander Azangulov, Teodora Pandeva, Niranjani Prasad +2
Masked diffusion models (MDMs) offer a compelling alternative to autoregressive models (ARMs) for discrete text generation because they enable parallel token sampling, rather than…
A Fourier Space Perspective on Diffusion Models
Fabian Falck, Teodora Pandeva, Kiarash Zahirnia +5
Diffusion models are state-of-the-art generative models on data modalities such as images, audio, proteins and materials. These modalities share the property of exponentially decay…
On Learning Parallel Pancakes with Mostly Uniform Weights
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the complexity of learning -mixtures of Gaussians (-GMMs) on . This task is known to have complexity in full generality. To circumvent this…
Efficient Knowledge Distillation via Curriculum Extraction
Shivam Gupta, Sushrut Karmalkar
Knowledge distillation is a technique used to train a small student network using the output generated by a large teacher network, and has many empirical advantages~\citep{Hinton20…
Batch List-Decodable Linear Regression via Higher Moments
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the task of list-decodable linear regression using batches. A batch is called clean if it consists of i.i.d. samples from an unknown linear regression distribution. For a…
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas +1
We study the problem of learning a single neuron with respect to the -loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goa…
Sum-of-squares lower bounds for Non-Gaussian Component Analysis
Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang +1
Non-Gaussian Component Analysis (NGCA) is the statistical task of finding a non-Gaussian direction in a high-dimensional dataset. Specifically, given i.i.d.\ samples from a distrib…
First Order Stochastic Optimization with Oblivious Noise
Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park +1
We initiate the study of stochastic optimization with oblivious noise, broadly generalizing the standard heavy-tailed noise setup. In our setting, in addition to random observation…
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study Gaussian sparse estimation tasks in Huber's contamination model with a focus on mean estimation, PCA, and linear regression. For each of these tasks, we give the first sam…
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds
David Jin, Sushrut Karmalkar, Harry Zhang +1
We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set…
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions
Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park +1
We demonstrate the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise. We assume we have sample access…
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the problem of list-decodable sparse mean estimation. Specifically, for a parameter , we are given points in , of whi…
Robust Sparse Mean Estimation via Sum of Squares
Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar +2
We study the problem of high-dimensional sparse mean estimation in the presence of an -fraction of adversarial outliers. Prior work obtained sample and computationally efficient…
Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann +2
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification…
Instance-Optimal Compressed Sensing via Posterior Sampling
Ajil Jalal, Sushrut Karmalkar, Alexandros G. Dimakis +1
We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather th…
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning
Aravind Gollakota, Sushrut Karmalkar, Adam Klivans
We consider the problem of distribution-free learning for Boolean function classes in the PAC and agnostic models. Generalizing a beautiful work of Malach and Shalev-Shwartz (2022)…
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin +2
We prove the first superpolynomial lower bounds for learning one-layer neural networks with respect to the Gaussian distribution using gradient descent. We show that any classifier…
Robustly Learning any Clusterable Mixture of Gaussians
Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane +1
We study the efficient learnability of high-dimensional Gaussian mixtures in the outlier-robust setting, where a small constant fraction of the data is adversarially corrupted. We…
Approximation Schemes for ReLU Regression
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar +2
We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distri…
Lower Bounds for Compressed Sensing with Generative Models
Akshay Kamath, Sushrut Karmalkar, Eric Price
The goal of compressed sensing is to learn a structured signal from a limited number of noisy linear measurements . In traditional compressed sensing, "structure"…