Structural segmentation of Hindustani concert audio with posterior features. - Prateek Verma; Vinutha T. P.; Parthe Pandit; Preeti Rao (ICASSP, 2015)

Refinement of the Equilibrium of Public Goods Games over Networks: Efficiency and Effort of Specialized Equilibria. - Parthe Pandit; Ankur A. Kulkarni (CoRR, 2016)

Non-constructive lower bounds for binary asymmetric error correcting codes. - Parthe Pandit; Ankur A. Kulkarni (NCC, 2017)

Discount-Based Pricing and Capacity Planning for EV Charging Under Stochastic Demand. - Parthe Pandit; Samuel Coogan 001 (ACC, 2018)

Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis. - Alyson K. Fletcher; Parthe Pandit; Sundeep Rangan; Subrata Sarkar; Philip Schniter (NeurIPS, 2018)

A linear complementarity based characterization of the weighted independence number and the independent domination number in graphs. - Parthe Pandit; Ankur A. Kulkarni (Discret. Appl. Math., 2018)

Sparse Multivariate Bernoulli Processes in High Dimensions. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Arash A. Amini; Sundeep Rangan; Alyson K. Fletcher (AISTATS, 2019)

Asymptotics of MAP Inference in Deep Networks. - Parthe Pandit; Mojtaba Sahraee; Sundeep Rangan; Alyson K. Fletcher (ISIT, 2019)

High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Arash A. Amini; Sundeep Rangan; Alyson K. Fletcher (CoRR, 2019)

Generalization Error of Generalized Linear Models in High Dimensions. - Melikasadat Emami; Mojtaba Sahraee-Ardakan; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (ICML, 2020)

Matrix Inference and Estimation in Multi-Layer Models. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Sundeep Rangan; Philip Schniter; Alyson K. Fletcher (NeurIPS, 2020)

Inference in Multi-Layer Networks with Matrix-Valued Unknowns. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Sundeep Rangan; Philip Schniter; Alyson K. Fletcher (CoRR, 2020)

Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex. - Melikasadat Emami; Mojtaba Sahraee-Ardakan; Parthe Pandit; Alyson K. Fletcher; Sundeep Rangan; Michael Trumpis; Brinnae Bent; Chia-Han Chiang; Jonathan Viventi (CoRR, 2020)

Generalized Autoregressive Linear Models for Discrete High-Dimensional Data. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Arash A. Amini; Sundeep Rangan; Alyson K. Fletcher (IEEE J. Sel. Areas Inf. Theory, 2020)

Inference With Deep Generative Priors in High Dimensions. - Parthe Pandit; Mojtaba Sahraee-Ardakan; Sundeep Rangan; Philip Schniter; Alyson K. Fletcher (IEEE J. Sel. Areas Inf. Theory, 2020)

Implicit Bias of Linear RNNs. - Melikasadat Emami; Mojtaba Sahraee-Ardakan; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (ICML, 2021)

Instability and Local Minima in GAN Training with Kernel Discriminators. - Evan Becker; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (NeurIPS, 2022)

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (NeurIPS, 2022)

Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions. - Mojtaba Sahraee-Ardakan; Melikasadat Emami; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (CoRR, 2022)

Kernel Ridgeless Regression is Inconsistent for Low Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (CoRR, 2022)

A note on Linear Bottleneck networks and their Transition to Multilinearity. - Libin Zhu; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

Feature learning in neural networks and kernel machines that recursively learn features. - Adityanarayanan Radhakrishnan; Daniel Beaglehole; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks. - Evan Becker; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (ACSSC, 2023)

Toward Large Kernel Models. - Amirhesam Abedsoltan; Mikhail Belkin; Parthe Pandit (ICML, 2023)

Mechanism of feature learning in convolutional neural networks. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2023)

On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (SIAM J. Math. Data Sci., 2023)

On the Nyström Approximation for Preconditioning in Kernel Machines. - Amirhesam Abedsoltan; Parthe Pandit; Luis Rademacher; Mikhail Belkin (AISTATS, 2024)

Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. - Neil Mallinar; Daniel Beaglehole; Libin Zhu; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

Universality of kernel random matrices and kernel regression in the quadratic regime. - Parthe Pandit; Zhichao Wang; Yizhe Zhu (CoRR, 2024)

Mirror Descent on Reproducing Kernel Banach Spaces. - Akash Kumar 010; Mikhail Belkin; Parthe Pandit (CoRR, 2024)

Fast training of large kernel models with delayed projections. - Amirhesam Abedsoltan; Siyuan Ma; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

AxlePro: Momentum-Accelerated Batched Training of Kernel Machines. - Yiming Zhang; Parthe Pandit (AISTATS, 2025)

GAN Training With Kernel Discriminators: What Parameters Control Convergence Rates? - Evan Becker; Parthe Pandit; Sundeep Rangan; Alyson K. Fletcher (IEEE Trans. Signal Process., 2025)

Feature maps for the Laplacian kernel and its generalizations. - Sudhendu Ahir; Parthe Pandit (CoRR, 2025)

Asymptotic convexity of wide and shallow neural networks. - Vivek Borkar; Parthe Pandit (CoRR, 2025)